From f823b065ebbc4ee635a5c4c3e103d76b12b2a466 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Thu, 8 Jun 2023 15:20:47 -0400
Subject: [PATCH 001/703] Adding permissions table

---
 .../manage-access/enterprise-permissions.md   |  8 ++++++
 .../snippets/enterprise-permissions-table.md  | 26 +++++++++++++++++++
 2 files changed, 34 insertions(+)
 create mode 100644 website/snippets/enterprise-permissions-table.md

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index 3e12bed9939..8f0c8c3b2aa 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -24,6 +24,14 @@ The following permission sets are available for assignment in dbt Cloud Enterpri
 can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
 can be associated with more than one permission sets.
 
+:::info Role permisisons
+
+The following table lists the standard roles and permissions associated with them in dbt Cloud. Some roles, such as _Stakeholder_ have more nuanced access to features. View the individual role descriptions for more information beyond the table definitions.
+
+::: 
+
+<Snippet src="enterprise-permissions-table" />
+
 ### Account Admin
 
 - **Has permissions on:** Authorized projects, account-level settings
diff --git a/website/snippets/enterprise-permissions-table.md b/website/snippets/enterprise-permissions-table.md
new file mode 100644
index 00000000000..6c021707a89
--- /dev/null
+++ b/website/snippets/enterprise-permissions-table.md
@@ -0,0 +1,26 @@
+
+Key:
+
+* (C)reate &mdash; Create new feature
+* (M)odify &mdash; Change existing fields. 
+* (D)elete &mdash; Remove feature fields entirely 
+* (V)iew &mdash; Read-only access
+* ❌ = No access to the feature
+* ✅ = Can access feature that does not use C/M/D/V permissions
+
+_Scroll to view all fields_
+
+| Role            | Environments | Projects | Connections | Jobs  | Repositories | Groups | Group memberships | Account settings | Webhooks | Account-level artifacts | Notifications | Use IDE |
+|-----------------|:------------:|:--------:|:-----------:|:-----:|:------------:|:------:|:-----------------:|:----------------:|:--------:|:-----------------------:|:-------------:|:-------:|
+| Account admin   | C/M/D        | C/M/D    | C/M/D       | C/M/D | C/M/D        | C/M/D  | C/M/D             | C/M/D            | C/M/D    | C/M/D                   |   C/M/D       | ✅      |
+| Account viewer  |     V        |   V      |   V         |   V   |   V          |  V     |      V            |    V             |   ❌     |   V                     |      V        | ❌      |
+| Admin           |  C/M/D       |   V      | C/M/D       | C/M/D | C/M/D        |  V     | C/M/D             |     V            | C/M/D    | ❌                      |      V        | ✅      |
+| Analyst         | V            |    ❌    |  V          | ❌    | ❌           | ❌      | ❌                | ❌               | ❌       | ❌                      | ❌            | ✅      |
+| Database admin  | V            | V        | C/M/D       | V     | V            | ❌     | ❌                 | ❌              | ❌        | ❌                      | ❌            | ❌      |
+| Developer       | ❌           | ❌       | ❌          | C/M/D | ❌            | ❌     | ❌                | ❌               | C/M/D    | ❌                      | ❌            | ✅      |
+| Git admin       | V            | V        | V           | V     | C/M/D        | ❌     | ❌                | ❌               | ❌        | ❌                      | ❌            | ❌      | 
+| Job admin       | C/M/V        | ❌       | V           | C/M/D | ❌            | ❌     | ❌                | ❌              | ❌        | ❌                      | ❌            | ❌      | 
+| Job viewer      | V            | ❌       | ❌          | V     | ❌            | ❌     | ❌                | ❌               | ❌       | ❌                      | ❌            |  ❌     |
+| Project creator | C/M/D        | C/M/D    | C/M/D       | C/M/D | C/M/D        | V       | V                | V                | ❌       | ❌                      | V             | ✅      |
+| Stakeholder     | ❌           | ❌       | ❌          | ❌     | ❌           | ❌      | ❌               | ❌               | ❌        | ❌                      | ❌            | ❌      | 
+| Team admin      | V            | V        | ❌          | V     | V            | V       | ❌               | ❌               | ❌        | ❌                      | ❌            | ❌      |   

From f2853dbb5c939a13eda516be423cae56129487bd Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Tue, 13 Jun 2023 17:59:01 -0700
Subject: [PATCH 002/703] Update redshift-setup.md

---
 .../connect-data-platform/redshift-setup.md   | 244 ++++++++++++++++++
 1 file changed, 244 insertions(+)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 4d5042a26be..b8108247ecd 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -47,6 +47,250 @@ pip is the easiest way to install the adapter:
 <p>For further info, refer to the GitHub repository: <a href={`https://github.com/${frontMatter.meta.github_repo}`}>{frontMatter.meta.github_repo}</a>.</p>
 
 
+--------remove this bit yo
+## Authentication Methods
+
+The authentication methods that dbt Core supports are: 
+
+- `database` &mdash; Password-based authentication (default, will be used if `method` is not provided)
+- `IAM` &mdash; Kerberos
+
+
+Click on one of these authentication methods for further details on how to configure your connection profile. Each tab also includes an example `profiles.yml` configuration file for you to review.
+
+<Tabs
+  defaultValue="database"
+  values={[
+    {label: 'database', value: 'database'},
+    {label: 'Kerberos', value: 'kerberos'},
+  ]}
+>
+
+<TabItem value="database">
+
+The following table contains the parameters for database (password-based) connection method.
+
+
+| Profile field | Example | Description |
+| ------------- | ------- | ------------ |
+| `method` | `database`| Leave this parameter unconfigured, or set this to database. |
+| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster. |
+| `user`   | `username` | Account username to log into your cluster. |
+| `password`  | `password1` | Password for authentication.  |
+| `port`  | `5439` |   |
+| `dbname`  | `my_db` | Database name.|
+| `schema`  | `my_schema` | Schema name.| 
+| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out.| 
+| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below TODO| 
+| `role`  | TODO | optional| 
+| `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
+| `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
+| `region`  | us-east-1 | region to connect to your cluster with | 
+
+<br/>
+
+#### Example profiles.yml for database authentication
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+company-name:
+  target: dev
+  outputs:
+    dev:
+      type: redshift
+      host: hostname.region.redshift.amazonaws.com
+      user: username
+      password: password1
+      port: 5439
+      dbname: analytics
+      schema: analytics
+      connect_timeout: None
+      sslmode: prefer
+      role: TODO
+      ra3_node: true 
+      autocommit: true 
+      region:
+```
+
+</File>
+
+</TabItem>
+
+<TabItem value="kerberos">
+
+The following table lists the authentication parameters to set for Kerberos.
+
+For more information, refer to [Kerberos authentication](https://trino.io/docs/current/security/kerberos.html) in the Trino docs.
+
+| Profile field                               | Example             | Description                                                      |
+| ------------------------------------------- | ------------------- | ---------------------------------------------------------------- |
+| `method` | `kerberos`| Set Kerberos as the authentication method. |
+| `user`                                      | `commander`         | Username for authentication                                      |
+| `keytab`                                    | `/tmp/trino.keytab` | Path to keytab                                                   |
+| `krb5_config`                               | `/tmp/krb5.conf`    | Path to config                                                   |
+| `principal`                                 | `trino@EXAMPLE.COM` | Principal                                                        |
+| `service_name` (optional)                   | `abc123`            | Service name (default is `trino`)                               |
+| `hostname_override` (optional)              | `EXAMPLE.COM`       | Kerberos hostname for a host whose DNS name doesn't match        |
+| `mutual_authentication` (optional)          | `false`             | Boolean flag for mutual authentication                           |
+| `force_preemptive` (optional)               | `false`             | Boolean flag to preemptively initiate the Kerberos GSS exchange |
+| `sanitize_mutual_error_response` (optional) | `true`              | Boolean flag to strip content and headers from error responses   |
+| `delegate`  (optional)                      | `false`             | Boolean flag for credential delegation (`GSS_C_DELEG_FLAG`)       |
+
+<br/>
+
+#### Example profiles.yml for Kerberos
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+trino:
+  target: dev
+  outputs:
+    dev:
+      type: trino
+      method: kerberos
+      user: commander
+      keytab: /tmp/trino.keytab
+      krb5_config: /tmp/krb5.conf
+      principal: trino@EXAMPLE.COM
+      host: trino.example.com
+      port: 443
+      database: analytics
+      schema: public
+```
+
+</File>
+
+</TabItem>
+
+<TabItem value="jwt">
+
+The following table lists the authentication parameters to set for JSON Web Token.
+
+For more information, refer to [JWT authentication](https://trino.io/docs/current/security/jwt.html) in the Trino docs.
+
+| Profile field        | Example        | Description                |
+| -------------------- | -------------- | -------------------------- |
+| `method` | `jwt`| Set JWT as the authentication method. |
+| `jwt_token` | `aaaaa.bbbbb.ccccc` | The JWT string. |
+
+<br/>
+
+#### Example profiles.yml for JWT
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+trino:
+  target: dev
+  outputs:
+    dev:
+      type: trino
+      method: jwt 
+      jwt_token: [my_long_jwt_token_string]
+      host: [hostname]
+      database: [database name]
+      schema: [your dbt schema]
+      port: [port number]
+      threads: [1 or more]
+```
+
+</File>
+
+</TabItem>
+
+<TabItem value="certificate">
+
+The following table lists the authentication parameters to set for certificates.
+
+For more information, refer to [Certificate authentication](https://trino.io/docs/current/security/certificate.html) in the Trino docs.
+
+| Profile field        | Example        | Description                         |
+| -------------------- | -------------- | ----------------------------------- |
+| `method` | `certificate`| Set certificate-based authentication as the method |
+| `client_certificate` | `/tmp/tls.crt` | Path to client certificate          |
+| `client_private_key` | `/tmp/tls.key` | Path to client private key          |
+| `cert`               |                | The full path to a certificate file |
+
+<br/>
+
+#### Example profiles.yml for certificate
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+trino:
+  target: dev
+  outputs:
+    dev:
+      type: trino
+      method: certificate 
+      cert: [path/to/cert_file]
+      client_certificate: [path/to/client/cert]
+      client_private_key: [path to client key]
+      database: [database name]
+      schema: [your dbt schema]
+      port: [port number]
+      threads: [1 or more]
+```
+
+</File>
+
+</TabItem>
+
+<TabItem value="oauth">
+
+The only authentication parameter to set for OAuth 2.0 is `method: oauth`. If you're using Starburst Enterprise or Starburst Galaxy, you must enable OAuth 2.0 in Starburst before you can use this authentication method.
+
+For more information, refer to both [OAuth 2.0 authentication](https://trino.io/docs/current/security/oauth2.html) in the Trino docs and the [README](https://github.com/trinodb/trino-python-client#oauth2-authentication) for the Trino Python client.
+
+It's recommended that you install `keyring` to cache the OAuth 2.0 token over multiple dbt invocations by running `pip install 'trino[external-authentication-token-cache]'`. The `keyring` package is not installed by default.
+
+#### Example profiles.yml for OAuth
+
+```yaml
+sandbox-galaxy:
+  target: oauth
+  outputs:
+    oauth:
+      type: trino
+      method: oauth
+      host: bunbundersders.trino.galaxy-dev.io
+      catalog: dbt_target
+      schema: dataders
+      port: 433
+```
+
+</TabItem>
+
+<TabItem value="none">
+
+You don't need to set up authentication (`method: none`), however, dbt Labs strongly discourages people from using it in any real application. Its use case is only for toy purposes (as in to play around with it), like local examples such as running Trino and dbt entirely within a single Docker container.
+
+#### Example profiles.yml for no authentication
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+trino:
+  target: dev
+  outputs:
+    dev:
+      type: trino
+      method: none
+      user: commander
+      host: trino.example.com
+      port: 443
+      database: analytics
+      schema: public
+```
+
+</File>
+
+</TabItem>
+</Tabs>
+
 ## Authentication Methods
 
 ### Password-based authentication

From f1aeba68f3d16de90d593d9d28e4dba86d65c650 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 09:01:32 -0700
Subject: [PATCH 003/703] remove unrelated bit

---
 .../connect-data-platform/redshift-setup.md   | 205 +-----------------
 1 file changed, 2 insertions(+), 203 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index b8108247ecd..fd0ba9c1ee6 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -53,7 +53,7 @@ pip is the easiest way to install the adapter:
 The authentication methods that dbt Core supports are: 
 
 - `database` &mdash; Password-based authentication (default, will be used if `method` is not provided)
-- `IAM` &mdash; Kerberos
+- `IAM` &mdash; IAM 
 
 
 Click on one of these authentication methods for further details on how to configure your connection profile. Each tab also includes an example `profiles.yml` configuration file for you to review.
@@ -117,7 +117,7 @@ company-name:
 
 </TabItem>
 
-<TabItem value="kerberos">
+<TabItem value="IAM">
 
 The following table lists the authentication parameters to set for Kerberos.
 
@@ -164,211 +164,10 @@ trino:
 
 </TabItem>
 
-<TabItem value="jwt">
-
-The following table lists the authentication parameters to set for JSON Web Token.
-
-For more information, refer to [JWT authentication](https://trino.io/docs/current/security/jwt.html) in the Trino docs.
-
-| Profile field        | Example        | Description                |
-| -------------------- | -------------- | -------------------------- |
-| `method` | `jwt`| Set JWT as the authentication method. |
-| `jwt_token` | `aaaaa.bbbbb.ccccc` | The JWT string. |
-
-<br/>
-
-#### Example profiles.yml for JWT
-
-<File name='~/.dbt/profiles.yml'>
-
-```yaml
-trino:
-  target: dev
-  outputs:
-    dev:
-      type: trino
-      method: jwt 
-      jwt_token: [my_long_jwt_token_string]
-      host: [hostname]
-      database: [database name]
-      schema: [your dbt schema]
-      port: [port number]
-      threads: [1 or more]
-```
-
-</File>
-
-</TabItem>
-
-<TabItem value="certificate">
-
-The following table lists the authentication parameters to set for certificates.
-
-For more information, refer to [Certificate authentication](https://trino.io/docs/current/security/certificate.html) in the Trino docs.
-
-| Profile field        | Example        | Description                         |
-| -------------------- | -------------- | ----------------------------------- |
-| `method` | `certificate`| Set certificate-based authentication as the method |
-| `client_certificate` | `/tmp/tls.crt` | Path to client certificate          |
-| `client_private_key` | `/tmp/tls.key` | Path to client private key          |
-| `cert`               |                | The full path to a certificate file |
-
-<br/>
-
-#### Example profiles.yml for certificate
-
-<File name='~/.dbt/profiles.yml'>
-
-```yaml
-trino:
-  target: dev
-  outputs:
-    dev:
-      type: trino
-      method: certificate 
-      cert: [path/to/cert_file]
-      client_certificate: [path/to/client/cert]
-      client_private_key: [path to client key]
-      database: [database name]
-      schema: [your dbt schema]
-      port: [port number]
-      threads: [1 or more]
-```
-
-</File>
-
-</TabItem>
-
-<TabItem value="oauth">
-
-The only authentication parameter to set for OAuth 2.0 is `method: oauth`. If you're using Starburst Enterprise or Starburst Galaxy, you must enable OAuth 2.0 in Starburst before you can use this authentication method.
-
-For more information, refer to both [OAuth 2.0 authentication](https://trino.io/docs/current/security/oauth2.html) in the Trino docs and the [README](https://github.com/trinodb/trino-python-client#oauth2-authentication) for the Trino Python client.
-
-It's recommended that you install `keyring` to cache the OAuth 2.0 token over multiple dbt invocations by running `pip install 'trino[external-authentication-token-cache]'`. The `keyring` package is not installed by default.
-
-#### Example profiles.yml for OAuth
-
-```yaml
-sandbox-galaxy:
-  target: oauth
-  outputs:
-    oauth:
-      type: trino
-      method: oauth
-      host: bunbundersders.trino.galaxy-dev.io
-      catalog: dbt_target
-      schema: dataders
-      port: 433
-```
-
-</TabItem>
-
-<TabItem value="none">
-
-You don't need to set up authentication (`method: none`), however, dbt Labs strongly discourages people from using it in any real application. Its use case is only for toy purposes (as in to play around with it), like local examples such as running Trino and dbt entirely within a single Docker container.
-
-#### Example profiles.yml for no authentication
-
-<File name='~/.dbt/profiles.yml'>
-
-```yaml
-trino:
-  target: dev
-  outputs:
-    dev:
-      type: trino
-      method: none
-      user: commander
-      host: trino.example.com
-      port: 443
-      database: analytics
-      schema: public
-```
-
-</File>
-
-</TabItem>
 </Tabs>
 
-## Authentication Methods
 
-### Password-based authentication
 
-<File name='~/.dbt/profiles.yml'>
-
-```yaml
-company-name:
-  target: dev
-  outputs:
-    dev:
-      type: redshift
-      host: hostname.region.redshift.amazonaws.com
-      user: username
-      password: password1
-      port: 5439
-      dbname: analytics
-      schema: analytics
-      threads: 4
-      connect_timeout: None # optional, number of seconds before connection times out 
-      # search_path: public # optional, not recommended
-      sslmode: prefer # optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect.
-      role: # optional
-      ra3_node: true # enables cross-database sources
-      autocommit: true # enables autocommit after each statement
-      region: # optional, if not provided, will be determined from host (e.g. host.123.us-east-1.redshift-serverless.amazonaws.com)
-```
-
-</File>
-
-### IAM Authentication
-
-To set up a Redshift profile using IAM Authentication, set the `method`
-parameter to `iam` as shown below. Note that a password is not required when
-using IAM Authentication. For more information on this type of authentication,
-consult the [Redshift Documentation](https://docs.aws.amazon.com/redshift/latest/mgmt/generating-user-credentials.html)
-and [boto3
-docs](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/redshift.html#Redshift.Client.get_cluster_credentials)
-on generating user credentials with IAM Auth.
-
-If you receive the "You must specify a region" error when using IAM
-Authentication, then your aws credentials are likely misconfigured. Try running
-`aws configure` to set up AWS access keys, and pick a default region. If you have any questions,
-please refer to the official AWS documentation on [Configuration and credential file settings](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html).
-
-<File name='~/.dbt/profiles.yml'>
-
-```yaml
-my-redshift-db:
-  target: dev
-  outputs:
-    dev:
-      type: redshift
-      method: iam
-      cluster_id: CLUSTER_ID
-      host: hostname.region.redshift.amazonaws.com
-      user: alice
-      iam_profile: data_engineer # optional
-      autocreate: true           # optional
-      db_groups: ['ANALYSTS']    # optional
-
-      # Other Redshift configs:
-      port: 5439
-      dbname: analytics
-      schema: analytics
-      threads: 4
-      connect_timeout: None # optional, number of seconds before connection times out 
-      [retries](#retries): 1 # default 1 retry on error/timeout when opening connections
-      role: # optional
-      sslmode: prefer # optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect.
-      ra3_node: true # enables cross-database sources
-      autocommit: true # optional, enables autocommit after each statement
-      region: # optional, if not provided, will be determined from host (e.g. host.123.us-east-1.redshift-serverless.amazonaws.com)
-
-
-```
-
-</File>
 
 ### Specifying an IAM Profile
 

From b8dd2f547682c966cd3f075979fa8237f7752f1a Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 10:02:37 -0700
Subject: [PATCH 004/703] Update redshift-setup.md

---
 .../connect-data-platform/redshift-setup.md   | 96 ++++++++++++-------
 1 file changed, 59 insertions(+), 37 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index fd0ba9c1ee6..dfed7903faf 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -47,7 +47,6 @@ pip is the easiest way to install the adapter:
 <p>For further info, refer to the GitHub repository: <a href={`https://github.com/${frontMatter.meta.github_repo}`}>{frontMatter.meta.github_repo}</a>.</p>
 
 
---------remove this bit yo
 ## Authentication Methods
 
 The authentication methods that dbt Core supports are: 
@@ -73,19 +72,19 @@ The following table contains the parameters for database (password-based) connec
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
-| `method` | `database`| Leave this parameter unconfigured, or set this to database. |
-| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster. |
-| `user`   | `username` | Account username to log into your cluster. |
-| `password`  | `password1` | Password for authentication.  |
+| `type` | redshift | The type of data warehouse you are connecting to|
+| `method` | `database`| Leave this parameter unconfigured, or set this to database |
+| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
+| `user`   | `username` | Account username to log into your cluster |
+| `password`  | `password1` | Password for authentication  |
 | `port`  | `5439` |   |
-| `dbname`  | `my_db` | Database name.|
-| `schema`  | `my_schema` | Schema name.| 
-| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out.| 
+| `dbname`  | `my_db` | Database name|
+| `schema`  | `my_schema` | Schema name| 
+| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
 | `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below TODO| 
 | `role`  | TODO | optional| 
 | `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
 | `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
-| `region`  | us-east-1 | region to connect to your cluster with | 
 
 <br/>
 
@@ -119,45 +118,68 @@ company-name:
 
 <TabItem value="IAM">
 
-The following table lists the authentication parameters to set for Kerberos.
-
-For more information, refer to [Kerberos authentication](https://trino.io/docs/current/security/kerberos.html) in the Trino docs.
+The following table lists the authentication parameters to use IAM autentication. 
+  
+Note that a password is not required when using IAM Authentication. For more information on this type of authentication, consult the Redshift Documentation and boto3 docs on generating user credentials with IAM Auth.
+  
+If you receive the "You must specify a region" error when using IAM Authentication, then your aws credentials are likely misconfigured. Try running aws configure to set up AWS access keys, and pick a default region. If you have any questions, please refer to the official AWS documentation on Configuration and credential file settings.
 
-| Profile field                               | Example             | Description                                                      |
-| ------------------------------------------- | ------------------- | ---------------------------------------------------------------- |
-| `method` | `kerberos`| Set Kerberos as the authentication method. |
-| `user`                                      | `commander`         | Username for authentication                                      |
-| `keytab`                                    | `/tmp/trino.keytab` | Path to keytab                                                   |
-| `krb5_config`                               | `/tmp/krb5.conf`    | Path to config                                                   |
-| `principal`                                 | `trino@EXAMPLE.COM` | Principal                                                        |
-| `service_name` (optional)                   | `abc123`            | Service name (default is `trino`)                               |
-| `hostname_override` (optional)              | `EXAMPLE.COM`       | Kerberos hostname for a host whose DNS name doesn't match        |
-| `mutual_authentication` (optional)          | `false`             | Boolean flag for mutual authentication                           |
-| `force_preemptive` (optional)               | `false`             | Boolean flag to preemptively initiate the Kerberos GSS exchange |
-| `sanitize_mutual_error_response` (optional) | `true`              | Boolean flag to strip content and headers from error responses   |
-| `delegate`  (optional)                      | `false`             | Boolean flag for credential delegation (`GSS_C_DELEG_FLAG`)       |
+| Profile field | Example | Description |
+| ------------- | ------- | ------------ |
+| `type` | redshift | The type of data warehouse you are connecting to|
+| `method` | `IAM`| use IAM to authenticate |
+| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
+| `iam_profile` | default| TODO |
+| `cluster_id` | `CLUSTER_ID`| Required for IAM |
+| `user`   | `username` | Account username to log into your cluster |
+| `password`  | `password1` | Password for authentication  |
+| `port`  | `5439` |   |
+| `dbname`  | `my_db` | Database name|
+| `schema`  | `my_schema` | Schema name| 
+| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
+| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below TODO| 
+| `role`  | TODO | optional| 
+| `autocreate`  | false | Optional, default false. Creates user if they do not exist | 
+| `db_groups`  | ['ANALYSTS'] | Optional. A list of existing database group names that the DbUser joins for the current session | 
+| `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
+| `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
+| `retries`  | 1 | Number of retries | 
+| `region`  | us-east-1 | Region to connect to your cluster with. Required for IAM | 
 
 <br/>
 
-#### Example profiles.yml for Kerberos
+
+#### Example profiles.yml for IAM
 
 <File name='~/.dbt/profiles.yml'>
 
 ```yaml
-trino:
+  my-redshift-db:
   target: dev
   outputs:
     dev:
-      type: trino
-      method: kerberos
-      user: commander
-      keytab: /tmp/trino.keytab
-      krb5_config: /tmp/krb5.conf
-      principal: trino@EXAMPLE.COM
-      host: trino.example.com
-      port: 443
-      database: analytics
-      schema: public
+      type: redshift
+      method: iam
+      cluster_id: CLUSTER_ID
+      host: hostname.region.redshift.amazonaws.com
+      user: alice
+      iam_profile: default
+      autocreate: true  
+      db_groups: ['ANALYSTS']
+
+      # Other Redshift configs:
+      port: 5439
+      dbname: analytics
+      schema: analytics
+      threads: 4
+      connect_timeout: None 
+      [retries](#retries): 1 
+      role: None
+      sslmode: prefer 
+      ra3_node: true  
+      autocommit: true  
+      region: us-east-1
+
 ```
 
 </File>

From fa30318658d1b1aa729ef9af5bb4570d8183cec8 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 12:10:45 -0700
Subject: [PATCH 005/703] Update redshift-setup.md

---
 .../core/connect-data-platform/redshift-setup.md  | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index dfed7903faf..959514f044f 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -81,8 +81,8 @@ The following table contains the parameters for database (password-based) connec
 | `dbname`  | `my_db` | Database name|
 | `schema`  | `my_schema` | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
-| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below TODO| 
-| `role`  | TODO | optional| 
+| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below | 
+| `role`  | None | optional| 
 | `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
 | `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
 
@@ -106,7 +106,7 @@ company-name:
       schema: analytics
       connect_timeout: None
       sslmode: prefer
-      role: TODO
+      role: None
       ra3_node: true 
       autocommit: true 
       region:
@@ -129,7 +129,7 @@ If you receive the "You must specify a region" error when using IAM Authenticati
 | `type` | redshift | The type of data warehouse you are connecting to|
 | `method` | `IAM`| use IAM to authenticate |
 | `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
-| `iam_profile` | default| TODO |
+| `iam_profile` | analyst | dbt will use the specified profile from your ~/.aws/config file |
 | `cluster_id` | `CLUSTER_ID`| Required for IAM |
 | `user`   | `username` | Account username to log into your cluster |
 | `password`  | `password1` | Password for authentication  |
@@ -137,8 +137,8 @@ If you receive the "You must specify a region" error when using IAM Authenticati
 | `dbname`  | `my_db` | Database name|
 | `schema`  | `my_schema` | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
-| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below TODO| 
-| `role`  | TODO | optional| 
+| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| 
+| `role`  | None | optional| 
 | `autocreate`  | false | Optional, default false. Creates user if they do not exist | 
 | `db_groups`  | ['ANALYSTS'] | Optional. A list of existing database group names that the DbUser joins for the current session | 
 | `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
@@ -163,7 +163,7 @@ If you receive the "You must specify a region" error when using IAM Authenticati
       cluster_id: CLUSTER_ID
       host: hostname.region.redshift.amazonaws.com
       user: alice
-      iam_profile: default
+      iam_profile: analyst
       autocreate: true  
       db_groups: ['ANALYSTS']
 
@@ -190,7 +190,6 @@ If you receive the "You must specify a region" error when using IAM Authenticati
 
 
 
-
 ### Specifying an IAM Profile
 
 When the `iam_profile` configuration is set, dbt will use the specified profile from your `~/.aws/config` file instead of using the profile name `default`

From 42dbeb25ce153403a2b7c85128a755a441ece589 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 13:16:19 -0700
Subject: [PATCH 006/703] Update redshift-setup.md

---
 website/docs/docs/core/connect-data-platform/redshift-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 959514f044f..f5255f3904d 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -61,7 +61,7 @@ Click on one of these authentication methods for further details on how to confi
   defaultValue="database"
   values={[
     {label: 'database', value: 'database'},
-    {label: 'Kerberos', value: 'kerberos'},
+    {label: 'IAM', value: 'IAM'},
   ]}
 >
 

From 9a927c6a2a7e192616eb41acdf11747557242924 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 14:15:44 -0700
Subject: [PATCH 007/703] Update redshift-setup.md

---
 .../connect-data-platform/redshift-setup.md   | 48 ++++++++-----------
 1 file changed, 21 insertions(+), 27 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index f5255f3904d..1e0fd7ab880 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -46,8 +46,27 @@ pip is the easiest way to install the adapter:
 
 <p>For further info, refer to the GitHub repository: <a href={`https://github.com/${frontMatter.meta.github_repo}`}>{frontMatter.meta.github_repo}</a>.</p>
 
+## Configurations
+
+| Profile field | Example | Description |
+| ------------- | ------- | ------------ |
+| `type` | redshift | The type of data warehouse you are connecting to|
+| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
+| `port`  | `5439` |   |
+| `dbname`  | `my_db` | Database name|
+| `schema`  | `my_schema` | Schema name| 
+| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
+| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| 
+| `role`  | None | Optional| 
+| `autocreate`  | false | Optional, default false. Creates user if they do not exist | 
+| `db_groups`  | ['ANALYSTS'] | Optional. A list of existing database group names that the DbUser joins for the current session | 
+| `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
+| `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
+| `retries`  | 1 | Number of retries | 
+| `region`  | us-east-1 | Optional | 
 
-## Authentication Methods
+
+## Authentication Parameters
 
 The authentication methods that dbt Core supports are: 
 
@@ -72,19 +91,10 @@ The following table contains the parameters for database (password-based) connec
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
-| `type` | redshift | The type of data warehouse you are connecting to|
 | `method` | `database`| Leave this parameter unconfigured, or set this to database |
 | `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
 | `user`   | `username` | Account username to log into your cluster |
 | `password`  | `password1` | Password for authentication  |
-| `port`  | `5439` |   |
-| `dbname`  | `my_db` | Database name|
-| `schema`  | `my_schema` | Schema name| 
-| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
-| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below | 
-| `role`  | None | optional| 
-| `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
-| `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
 
 <br/>
 
@@ -126,25 +136,10 @@ If you receive the "You must specify a region" error when using IAM Authenticati
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
-| `type` | redshift | The type of data warehouse you are connecting to|
 | `method` | `IAM`| use IAM to authenticate |
-| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
 | `iam_profile` | analyst | dbt will use the specified profile from your ~/.aws/config file |
 | `cluster_id` | `CLUSTER_ID`| Required for IAM |
-| `user`   | `username` | Account username to log into your cluster |
-| `password`  | `password1` | Password for authentication  |
-| `port`  | `5439` |   |
-| `dbname`  | `my_db` | Database name|
-| `schema`  | `my_schema` | Schema name| 
-| `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
-| `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| 
-| `role`  | None | optional| 
-| `autocreate`  | false | Optional, default false. Creates user if they do not exist | 
-| `db_groups`  | ['ANALYSTS'] | Optional. A list of existing database group names that the DbUser joins for the current session | 
-| `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
-| `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
-| `retries`  | 1 | Number of retries | 
-| `region`  | us-east-1 | Region to connect to your cluster with. Required for IAM | 
+| `user`   | `username` | Account user to log into your cluster |
 
 <br/>
 
@@ -189,7 +184,6 @@ If you receive the "You must specify a region" error when using IAM Authenticati
 </Tabs>
 
 
-
 ### Specifying an IAM Profile
 
 When the `iam_profile` configuration is set, dbt will use the specified profile from your `~/.aws/config` file instead of using the profile name `default`

From 62a91cec6d90c680704ec650c1e6c76844a154ff Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 16:52:21 -0700
Subject: [PATCH 008/703] add hyperlinks

---
 .../core/connect-data-platform/redshift-setup.md   | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 1bec7684640..103025b31f6 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -130,9 +130,17 @@ company-name:
 
 The following table lists the authentication parameters to use IAM autentication. 
   
-Note that a password is not required when using IAM Authentication. For more information on this type of authentication, consult the Redshift Documentation and boto3 docs on generating user credentials with IAM Auth.
-  
-If you receive the "You must specify a region" error when using IAM Authentication, then your aws credentials are likely misconfigured. Try running aws configure to set up AWS access keys, and pick a default region. If you have any questions, please refer to the official AWS documentation on Configuration and credential file settings.
+To set up a Redshift profile using IAM Authentication, set the `method` parameter to `iam` as shown below. Note that a password is not required when using IAM Authentication. For more information on this type of authentication,
+consult the [Redshift Documentation](https://docs.aws.amazon.com/redshift/latest/mgmt/generating-user-credentials.html)
+and [boto3
+docs](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/redshift.html#Redshift.Client.get_cluster_credentials)
+on generating user credentials with IAM Auth.
+
+If you receive the "You must specify a region" error when using IAM
+Authentication, then your aws credentials are likely misconfigured. Try running
+`aws configure` to set up AWS access keys, and pick a default region. If you have any questions,
+please refer to the official AWS documentation on [Configuration and credential file settings](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html).
+
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |

From 0f2e3e17d675ebdd47ce966216d8e8afaab76874 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 14 Jun 2023 17:02:49 -0700
Subject: [PATCH 009/703] clean up syntax

---
 .../connect-data-platform/redshift-setup.md   | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 103025b31f6..ed01b28501d 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -51,10 +51,10 @@ pip is the easiest way to install the adapter:
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
 | `type` | redshift | The type of data warehouse you are connecting to|
-| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
-| `port`  | `5439` |   |
-| `dbname`  | `my_db` | Database name|
-| `schema`  | `my_schema` | Schema name| 
+| `host` | hostname.region.redshift.amazonaws.com| Host of cluster |
+| `port`  | 5439 |   |
+| `dbname`  | my_db | Database name|
+| `schema`  | my_schema | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
 | `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| 
 | `role`  | None | Optional| 
@@ -91,10 +91,10 @@ The following table contains the parameters for database (password-based) connec
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
-| `method` | `database`| Leave this parameter unconfigured, or set this to database |
-| `host` | `hostname.region.redshift.amazonaws.com`| Host of cluster |
-| `user`   | `username` | Account username to log into your cluster |
-| `password`  | `password1` | Password for authentication  |
+| `method` | database| Leave this parameter unconfigured, or set this to database |
+| `host` | hostname.region.redshift.amazonaws.com| Host of cluster |
+| `user`   | username | Account username to log into your cluster |
+| `password`  | password1 | Password for authentication  |
 
 <br/>
 
@@ -144,10 +144,10 @@ please refer to the official AWS documentation on [Configuration and credential
 
 | Profile field | Example | Description |
 | ------------- | ------- | ------------ |
-| `method` | `IAM`| use IAM to authenticate |
+| `method` |IAM| use IAM to authenticate |
 | `iam_profile` | analyst | dbt will use the specified profile from your ~/.aws/config file |
-| `cluster_id` | `CLUSTER_ID`| Required for IAM |
-| `user`   | `username` | Account user to log into your cluster |
+| `cluster_id` | CLUSTER_ID| Required for IAM |
+| `user`   | username | Account user to log into your cluster |
 
 <br/>
 

From 57cad16ead804bb1ba3d1d698937d6b8fb4e884b Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 13:25:05 -0400
Subject: [PATCH 010/703] add checker config

---
 .github/lychee.toml         | 51 +++++++++++++++++++++++++++++++++++++
 .github/workflows/links.yml | 17 +++++++++++++
 2 files changed, 68 insertions(+)
 create mode 100644 .github/lychee.toml
 create mode 100644 .github/workflows/links.yml

diff --git a/.github/lychee.toml b/.github/lychee.toml
new file mode 100644
index 00000000000..8f8c52088f5
--- /dev/null
+++ b/.github/lychee.toml
@@ -0,0 +1,51 @@
+###
+### Display
+###
+
+# Verbose program output
+# Accepts log level: "error", "warn", "info", "debug", "trace"
+verbose = "info"
+
+# Don't show interactive progress bar while checking links.
+no_progress = true
+
+###
+### Cache
+###
+
+# Enable link caching. This can be helpful to avoid checking the same links on
+# multiple runs.
+cache = false
+
+###
+### Requests
+###
+
+# Website timeout from connect to response finished.
+timeout = 30
+
+# Comma-separated list of accepted status codes for valid links.
+accept = [200, 403, 429]
+
+# Base URL or website root directory to check relative URLs.
+base = "https://docs.getdbt.com"
+
+###
+### Exclusions
+###
+
+# Exclude URLs from checking (supports regex)
+exclude = [
+  'frontMatter.',
+  'https://badge.fury.io',
+  'https://img.shields.io',
+  'https://gitlab.com',
+  'https://dbtlabs.postman.co',
+]
+
+# Exclude all mail addresses from checking
+exclude_mail = true
+
+# Check links inside `<code>` and `<pre>` blocks as well as Markdown code
+# blocks.
+include_verbatim = false
diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
new file mode 100644
index 00000000000..48409555d70
--- /dev/null
+++ b/.github/workflows/links.yml
@@ -0,0 +1,17 @@
+name: Check Docs Links
+
+on: [push]
+  # schedule:
+  #   # Run every Monday morning at 6:00AM UTC
+  #   - cron: '0 6 * * 1'
+
+jobs:
+  markdown-link-check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Link Checker
+        uses: lycheeverse/lychee-action@master
+        with:
+          args: --verbose --config .github/lychee.toml './website/**/*.md'

From d18ab6edc8854c98f25b4f6d7f04a2effdd10457 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 13:31:09 -0400
Subject: [PATCH 011/703] add excluded urls

---
 .github/lychee.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/lychee.toml b/.github/lychee.toml
index 8f8c52088f5..48aa972bf37 100644
--- a/.github/lychee.toml
+++ b/.github/lychee.toml
@@ -41,6 +41,8 @@ exclude = [
   'https://img.shields.io',
   'https://gitlab.com',
   'https://dbtlabs.postman.co',
+  'https://mobile.twitter.com',
+  'https://twitter.com',
 ]
 
 # Exclude all mail addresses from checking

From 3c3ac515b383d48eeafb32684a4309901de78e4d Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 13:34:23 -0400
Subject: [PATCH 012/703] add 2nd twitter exclude

---
 .github/lychee.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/lychee.toml b/.github/lychee.toml
index 48aa972bf37..2d9f0185496 100644
--- a/.github/lychee.toml
+++ b/.github/lychee.toml
@@ -43,6 +43,7 @@ exclude = [
   'https://dbtlabs.postman.co',
   'https://mobile.twitter.com',
   'https://twitter.com',
+  'https://www.twitter.com',
 ]
 
 # Exclude all mail addresses from checking

From dd52e92496f819376f892353b5fa12e88c61cc63 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 14:25:22 -0400
Subject: [PATCH 013/703] update faq prop name

---
 website/docs/docs/build/jinja-macros.md       | 14 ++++----
 website/docs/docs/build/seeds.md              | 18 +++++------
 website/docs/docs/build/snapshots.md          | 14 ++++----
 website/docs/docs/build/sources.md            | 16 +++++-----
 website/docs/docs/build/sql-models.md         | 32 +++++++++----------
 website/docs/docs/build/tests.md              | 16 +++++-----
 .../dbt-cloud-ide/develop-in-the-cloud.md     |  2 +-
 .../docs/docs/cloud/git/authenticate-azure.md |  2 +-
 website/docs/docs/cloud/git/connect-github.md |  2 +-
 website/docs/docs/cloud/git/connect-gitlab.md |  6 ++--
 .../docs/docs/collaborate/documentation.md    | 12 +++----
 .../connect-data-platform/bigquery-setup.md   |  4 +--
 website/docs/docs/core/pip-install.md         |  6 ++--
 website/docs/docs/core/source-install.md      |  6 ++--
 .../docs/docs/dbt-cloud-apis/user-tokens.md   |  4 +--
 website/docs/quickstarts/bigquery-qs.md       | 16 +++++-----
 website/docs/quickstarts/databricks-qs.md     | 16 +++++-----
 website/docs/quickstarts/manual-install-qs.md | 30 ++++++++---------
 website/docs/quickstarts/redshift-qs.md       | 16 +++++-----
 website/docs/quickstarts/snowflake-qs.md      |  6 ++--
 .../docs/quickstarts/starburst-galaxy-qs.md   | 16 +++++-----
 website/docs/reference/commands/compile.md    |  2 +-
 .../docs/reference/configs-and-properties.md  | 12 +++----
 .../reference/dbt-jinja-functions/dispatch.md |  2 +-
 .../resource-configs/full_refresh.md          |  2 +-
 .../resource-configs/target_schema.md         |  2 +-
 .../change-way-model-materialized.md          |  6 ++--
 .../quickstarts/delete-example-models.md      |  4 +--
 .../snippets/quickstarts/schedule-a-job.md    |  2 +-
 .../test-and-document-your-project.md         |  4 +--
 .../snippets/tutorial-add-tests-to-models.md  | 14 ++++----
 website/src/components/faqs/index.js          |  8 ++---
 website/src/pages/styles.js                   |  6 ++--
 33 files changed, 159 insertions(+), 159 deletions(-)

diff --git a/website/docs/docs/build/jinja-macros.md b/website/docs/docs/build/jinja-macros.md
index 5b0df69e898..8375eed594f 100644
--- a/website/docs/docs/build/jinja-macros.md
+++ b/website/docs/docs/build/jinja-macros.md
@@ -148,13 +148,13 @@ You can also qualify a macro in your own project by prefixing it with your [pack
 
 ## FAQs
 
-<FAQ src="Accounts/dbt-specific-jinja" />
-<FAQ src="Jinja/which-jinja-docs" />
-<FAQ src="Jinja/quoting-column-names" />
-<FAQ src="Jinja/jinja-whitespace" />
-<FAQ src="Project/debugging-jinja" />
-<FAQ src="Docs/documenting-macros" />
-<FAQ src="Project/why-so-many-macros" />
+<FAQ path="Accounts/dbt-specific-jinja" />
+<FAQ path="Jinja/which-jinja-docs" />
+<FAQ path="Jinja/quoting-column-names" />
+<FAQ path="Jinja/jinja-whitespace" />
+<FAQ path="Project/debugging-jinja" />
+<FAQ path="Docs/documenting-macros" />
+<FAQ path="Project/why-so-many-macros" />
 
 ## dbtonic Jinja
 
diff --git a/website/docs/docs/build/seeds.md b/website/docs/docs/build/seeds.md
index 5ea92a78d4a..6b1abf8f1c3 100644
--- a/website/docs/docs/build/seeds.md
+++ b/website/docs/docs/build/seeds.md
@@ -80,12 +80,12 @@ Seeds are configured in your `dbt_project.yml`, check out the [seed configuratio
 You can document and test seeds in YAML by declaring properties — check out the docs on [seed properties](/reference/seed-properties) for more information.
 
 ## FAQs
-<FAQ src="Seeds/load-raw-data-with-seed" />
-<FAQ src="Tests/configurable-data-path" />
-<FAQ src="Seeds/full-refresh-seed" />
-<FAQ src="Tests/testing-seeds" />
-<FAQ src="Seeds/seed-datatypes" />
-<FAQ src="Runs/run-downstream-of-seed" />
-<FAQ src="Seeds/leading-zeros-in-seed" />
-<FAQ src="Seeds/build-one-seed" />
-<FAQ src="Seeds/seed-hooks" />
+<FAQ path="Seeds/load-raw-data-with-seed" />
+<FAQ path="Tests/configurable-data-path" />
+<FAQ path="Seeds/full-refresh-seed" />
+<FAQ path="Tests/testing-seeds" />
+<FAQ path="Seeds/seed-datatypes" />
+<FAQ path="Runs/run-downstream-of-seed" />
+<FAQ path="Seeds/leading-zeros-in-seed" />
+<FAQ path="Seeds/build-one-seed" />
+<FAQ path="Seeds/seed-hooks" />
diff --git a/website/docs/docs/build/snapshots.md b/website/docs/docs/build/snapshots.md
index 01330c5f0aa..947d4ebef38 100644
--- a/website/docs/docs/build/snapshots.md
+++ b/website/docs/docs/build/snapshots.md
@@ -408,10 +408,10 @@ Snapshot results:
 
 
 ## FAQs
-<FAQ src="Runs/run-one-snapshot" />
-<FAQ src="Runs/snapshot-frequency" />
-<FAQ src="Snapshots/snapshot-schema-changes" />
-<FAQ src="Snapshots/snapshot-hooks" />
-<FAQ src="Snapshots/snapshot-target-schema" />
-<FAQ src="Accounts/configurable-snapshot-path" />
-<FAQ src="Snapshots/snapshot-target-is-not-a-snapshot-table" />
+<FAQ path="Runs/run-one-snapshot" />
+<FAQ path="Runs/snapshot-frequency" />
+<FAQ path="Snapshots/snapshot-schema-changes" />
+<FAQ path="Snapshots/snapshot-hooks" />
+<FAQ path="Snapshots/snapshot-target-schema" />
+<FAQ path="Accounts/configurable-snapshot-path" />
+<FAQ path="Snapshots/snapshot-target-is-not-a-snapshot-table" />
diff --git a/website/docs/docs/build/sources.md b/website/docs/docs/build/sources.md
index 8c7cf934464..d1b269cdd13 100644
--- a/website/docs/docs/build/sources.md
+++ b/website/docs/docs/build/sources.md
@@ -124,11 +124,11 @@ sources:
 You can find more details on the available properties for sources in the [reference section](/reference/source-properties).
 
 ### FAQs
-<FAQ src="Project/source-has-bad-name" />
-<FAQ src="Project/source-in-different-database" />
-<FAQ src="Models/source-quotes" />
-<FAQ src="Tests/testing-sources" />
-<FAQ src="Runs/running-models-downstream-of-source" />
+<FAQ path="Project/source-has-bad-name" />
+<FAQ path="Project/source-in-different-database" />
+<FAQ path="Models/source-quotes" />
+<FAQ path="Tests/testing-sources" />
+<FAQ path="Runs/running-models-downstream-of-source" />
 
 ## Snapshotting source data freshness
 With a couple of extra configs, dbt can optionally snapshot the "freshness" of the data in your source tables. This is useful for understanding if your data pipelines are in a healthy state, and is a critical component of defining SLAs for your warehouse.
@@ -204,6 +204,6 @@ where _etl_loaded_at >= date_sub(current_date(), interval 1 day)
 ```
 
 ### FAQs
-<FAQ src="Project/exclude-table-from-freshness" />
-<FAQ src="Snapshots/snapshotting-freshness-for-one-source" />
-<FAQ src="Project/dbt-source-freshness" />
+<FAQ path="Project/exclude-table-from-freshness" />
+<FAQ path="Snapshots/snapshotting-freshness-for-one-source" />
+<FAQ path="Project/dbt-source-freshness" />
diff --git a/website/docs/docs/build/sql-models.md b/website/docs/docs/build/sql-models.md
index bfe844e394e..65fdd58adf0 100644
--- a/website/docs/docs/build/sql-models.md
+++ b/website/docs/docs/build/sql-models.md
@@ -103,11 +103,11 @@ Why a _view_ named `dbt_alice.customers`? By default dbt will:
 You can use _configurations_ to change any of these behaviors — more on that later.
 
 ### FAQs
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
-<FAQ src="Models/sql-dialect" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
+<FAQ path="Models/sql-dialect" />
 
 ## Configuring models
 Configurations are "model settings"  that can be set in your `dbt_project.yml` file, _and_ in your model file using a `config` block. Some example configurations include:
@@ -158,8 +158,8 @@ It is important to note that configurations are applied hierarchically — a con
 You can learn more about configurations in the [reference docs](/reference/model-configs).
 
 ### FAQs
-<FAQ src="Models/available-materializations" />
-<FAQ src="Models/available-configurations" />
+<FAQ path="Models/available-materializations" />
+<FAQ path="Models/available-configurations" />
 
 
 ## Building dependencies between models
@@ -265,12 +265,12 @@ Additionally, the `ref` function encourages you to write modular transformations
 You can also document and test models &mdash; skip ahead to the section on [testing](/docs/build/tests) and [documentation](/docs/collaborate/documentation) for more information.
 
 ## Additional FAQs
-<FAQ src="Project/example-projects" alt_header="Are there any example dbt models?" />
-<FAQ src="Models/configurable-model-path" />
-<FAQ src="Models/model-custom-schemas" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Models/removing-deleted-models" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
-<FAQ src="Models/insert-records" />
-<FAQ src="Project/why-not-write-dml" />
-<FAQ src="Models/specifying-column-types" />
+<FAQ path="Project/example-projects" alt_header="Are there any example dbt models?" />
+<FAQ path="Models/configurable-model-path" />
+<FAQ path="Models/model-custom-schemas" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Models/removing-deleted-models" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Models/insert-records" />
+<FAQ path="Project/why-not-write-dml" />
+<FAQ path="Models/specifying-column-types" />
diff --git a/website/docs/docs/build/tests.md b/website/docs/docs/build/tests.md
index 51a0346443f..1a40dd42b53 100644
--- a/website/docs/docs/build/tests.md
+++ b/website/docs/docs/build/tests.md
@@ -261,11 +261,11 @@ Note that, if you elect to store test failures:
 
 ## FAQs
 
-<FAQ src="Tests/test-one-model" />
-<FAQ src="Runs/failed-tests" />
-<FAQ src="Tests/recommended-tests" />
-<FAQ src="Tests/when-to-test" />
-<FAQ src="Tests/configurable-data-test-path" />
-<FAQ src="Tests/testing-sources" />
-<FAQ src="Tests/custom-test-thresholds" />
-<FAQ src="Tests/uniqueness-two-columns" />
+<FAQ path="Tests/test-one-model" />
+<FAQ path="Runs/failed-tests" />
+<FAQ path="Tests/recommended-tests" />
+<FAQ path="Tests/when-to-test" />
+<FAQ path="Tests/configurable-data-test-path" />
+<FAQ path="Tests/testing-sources" />
+<FAQ path="Tests/custom-test-thresholds" />
+<FAQ path="Tests/uniqueness-two-columns" />
diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index c5bb2dc8b9e..a128cbb9bd1 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -141,7 +141,7 @@ The dbt Cloud IDE makes it possible to [build and view](/docs/collaborate/build-
 
 ## Related questions
 
-<FAQ src="Git/gitignore"/> <br />
+<FAQ path="Git/gitignore"/> <br />
 
 <details>
   <summary>Is there a cost to using the Cloud IDE?</summary>
diff --git a/website/docs/docs/cloud/git/authenticate-azure.md b/website/docs/docs/cloud/git/authenticate-azure.md
index f3a534ac923..9e755519e67 100644
--- a/website/docs/docs/cloud/git/authenticate-azure.md
+++ b/website/docs/docs/cloud/git/authenticate-azure.md
@@ -25,4 +25,4 @@ You will be directed back to dbt Cloud, and your profile should be linked. You a
 
 ## FAQs
 
-<FAQ src="Git/gitignore"/>
+<FAQ path="Git/gitignore"/>
diff --git a/website/docs/docs/cloud/git/connect-github.md b/website/docs/docs/cloud/git/connect-github.md
index 6113e3ccb42..410b28c9767 100644
--- a/website/docs/docs/cloud/git/connect-github.md
+++ b/website/docs/docs/cloud/git/connect-github.md
@@ -74,4 +74,4 @@ The next time you log into dbt Cloud, you will be able to do so via OAuth throug
 
 ## FAQs
 
-<FAQ src="Git/gitignore"/>
+<FAQ path="Git/gitignore"/>
diff --git a/website/docs/docs/cloud/git/connect-gitlab.md b/website/docs/docs/cloud/git/connect-gitlab.md
index e66fa577e5b..1ec8fb08817 100644
--- a/website/docs/docs/cloud/git/connect-gitlab.md
+++ b/website/docs/docs/cloud/git/connect-gitlab.md
@@ -119,6 +119,6 @@ If you imported a repository using the dbt Cloud native integration with GitLab,
 
 ## FAQs
 
-<FAQ src="Git/gitignore"/>
-<FAQ src="Git/gitlab-authentication"/>
-<FAQ src="Git/gitlab-selfhosted"/>
+<FAQ path="Git/gitignore"/>
+<FAQ path="Git/gitlab-authentication"/>
+<FAQ path="Git/gitlab-selfhosted"/>
diff --git a/website/docs/docs/collaborate/documentation.md b/website/docs/docs/collaborate/documentation.md
index a28ea8ff55f..b613fd7a5ef 100644
--- a/website/docs/docs/collaborate/documentation.md
+++ b/website/docs/docs/collaborate/documentation.md
@@ -67,12 +67,12 @@ First, run `dbt docs generate` — this command tells dbt to compile relevant in
 Then, run `dbt docs serve` to use these `.json` files to populate a local website.
 
 ## FAQs
-<FAQ src="Project/example-projects" alt_header="Are there any example dbt documentation sites?"/>
-<FAQ src="Docs/document-all-columns" />
-<FAQ src="Docs/long-descriptions" />
-<FAQ src="Docs/sharing-documentation" />
-<FAQ src="Docs/document-other-resources" />
-<FAQ src="Project/docs-for-multiple-projects" />
+<FAQ path="Project/example-projects" alt_header="Are there any example dbt documentation sites?"/>
+<FAQ path="Docs/document-all-columns" />
+<FAQ path="Docs/long-descriptions" />
+<FAQ path="Docs/sharing-documentation" />
+<FAQ path="Docs/document-other-resources" />
+<FAQ path="Project/docs-for-multiple-projects" />
 
 
 ## Using Docs Blocks
diff --git a/website/docs/docs/core/connect-data-platform/bigquery-setup.md b/website/docs/docs/core/connect-data-platform/bigquery-setup.md
index bfb97237b29..99ae473b0e2 100644
--- a/website/docs/docs/core/connect-data-platform/bigquery-setup.md
+++ b/website/docs/docs/core/connect-data-platform/bigquery-setup.md
@@ -457,8 +457,8 @@ my-profile:
 
 For a general overview of this process, see the official docs for [Creating Short-lived Service Account Credentials](https://cloud.google.com/iam/docs/creating-short-lived-service-account-credentials).
 
-<FAQ src="Warehouse/bq-impersonate-service-account-why" />
-<FAQ src="Warehouse/bq-impersonate-service-account-setup" />
+<FAQ path="Warehouse/bq-impersonate-service-account-why" />
+<FAQ path="Warehouse/bq-impersonate-service-account-setup" />
 
 ### Execution project
 <Changelog>New in v0.21.0</Changelog>
diff --git a/website/docs/docs/core/pip-install.md b/website/docs/docs/core/pip-install.md
index 912af40b9b7..26a15d8ad37 100644
--- a/website/docs/docs/core/pip-install.md
+++ b/website/docs/docs/core/pip-install.md
@@ -8,9 +8,9 @@ You need to use `pip` to install dbt Core on Windows or Linux operating systems.
 You can install dbt Core and plugins using `pip` because they are Python modules distributed on [PyPI](https://pypi.org/project/dbt/). We recommend using virtual environments when installing with `pip`.
 
 
-<FAQ src="Core/install-pip-os-prereqs" />
-<FAQ src="Core/install-python-compatibility" />
-<FAQ src="Core/install-pip-best-practices" />
+<FAQ path="Core/install-pip-os-prereqs" />
+<FAQ path="Core/install-python-compatibility" />
+<FAQ path="Core/install-pip-best-practices" />
 
 
 Once you know [which adapter](/docs/supported-data-platforms) you're using, you can install it as `dbt-<adapter>`. For example, if using Postgres:
diff --git a/website/docs/docs/core/source-install.md b/website/docs/docs/core/source-install.md
index 6714e88cd10..be9918223fe 100644
--- a/website/docs/docs/core/source-install.md
+++ b/website/docs/docs/core/source-install.md
@@ -35,6 +35,6 @@ You do _not_ need to install `dbt-core` before installing an adapter plugin -- t
 
 To install in editable mode, such as while contributing, use `pip install -e .` instead.
 
-<FAQ src="Core/install-pip-os-prereqs" />
-<FAQ src="Core/install-python-compatibility" />
-<FAQ src="Core/install-pip-best-practices" />
+<FAQ path="Core/install-pip-os-prereqs" />
+<FAQ path="Core/install-python-compatibility" />
+<FAQ path="Core/install-pip-best-practices" />
diff --git a/website/docs/docs/dbt-cloud-apis/user-tokens.md b/website/docs/docs/dbt-cloud-apis/user-tokens.md
index 70f3f8f615f..e56d8b2f974 100644
--- a/website/docs/docs/dbt-cloud-apis/user-tokens.md
+++ b/website/docs/docs/dbt-cloud-apis/user-tokens.md
@@ -17,5 +17,5 @@ label.
 
 ## FAQs
 
-<FAQ src="API/rotate-token" />
-<FAQ src="Accounts/find-user-id" />
\ No newline at end of file
+<FAQ path="API/rotate-token" />
+<FAQ path="Accounts/find-user-id" />
diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md
index ed913cda647..2ff0ad92606 100644
--- a/website/docs/quickstarts/bigquery-qs.md
+++ b/website/docs/quickstarts/bigquery-qs.md
@@ -179,11 +179,11 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs
 
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Project/which-schema" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Project/which-schema" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
 
 ## Change the way your model is materialized
 
@@ -287,9 +287,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 
 <Snippet src="quickstarts/test-and-document-your-project" />
diff --git a/website/docs/quickstarts/databricks-qs.md b/website/docs/quickstarts/databricks-qs.md
index 096f6d2b04a..706e87352ee 100644
--- a/website/docs/quickstarts/databricks-qs.md
+++ b/website/docs/quickstarts/databricks-qs.md
@@ -257,11 +257,11 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs
 
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Project/which-schema" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Project/which-schema" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
 
 ## Change the way your model is materialized
 
@@ -365,9 +365,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 
 <Snippet src="quickstarts/test-and-document-your-project" />
diff --git a/website/docs/quickstarts/manual-install-qs.md b/website/docs/quickstarts/manual-install-qs.md
index f7d15b897f7..97eb8f8e8ea 100644
--- a/website/docs/quickstarts/manual-install-qs.md
+++ b/website/docs/quickstarts/manual-install-qs.md
@@ -130,11 +130,11 @@ When developing locally, dbt connects to your <Term id="data-warehouse" /> using
 
 #### FAQs
 
-<FAQ src="Warehouse/sample-profiles" alt_header="My data team uses a different data warehouse. What should my profiles.yml file look like for my warehouse?"/>
-<FAQ src="Project/separate-profile" />
-<FAQ src="Environments/profile-name" />
-<FAQ src="Environments/target-names" />
-<FAQ src="Environments/profile-env-vars" />
+<FAQ path="Warehouse/sample-profiles" alt_header="My data team uses a different data warehouse. What should my profiles.yml file look like for my warehouse?"/>
+<FAQ path="Project/separate-profile" />
+<FAQ path="Environments/profile-name" />
+<FAQ path="Environments/target-names" />
+<FAQ path="Environments/profile-env-vars" />
 
 ### Perform your first dbt run
 
@@ -200,11 +200,11 @@ When you return to the BigQuery console, you can `select` from this model.
 
 #### FAQs
 
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Project/which-schema" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Project/which-schema" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
 
 ### Change the way your model is materialized
 
@@ -411,9 +411,9 @@ When you return to the BigQuery console, you can `select` from this model.
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 ### Next steps
 
@@ -438,8 +438,8 @@ You can also explore:
 
 #### FAQs
 
-<FAQ src="Docs/long-descriptions" />
-<FAQ src="Docs/sharing-documentation" />
+<FAQ path="Docs/long-descriptions" />
+<FAQ path="Docs/sharing-documentation" />
 
 
 #### Next steps
diff --git a/website/docs/quickstarts/redshift-qs.md b/website/docs/quickstarts/redshift-qs.md
index b5277a47559..f672f16c4ed 100644
--- a/website/docs/quickstarts/redshift-qs.md
+++ b/website/docs/quickstarts/redshift-qs.md
@@ -273,11 +273,11 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs
 
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Project/which-schema" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Project/which-schema" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
 
 ## Change the way your model is materialized
 
@@ -381,9 +381,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 <Snippet src="quickstarts/test-and-document-your-project" />
 
diff --git a/website/docs/quickstarts/snowflake-qs.md b/website/docs/quickstarts/snowflake-qs.md
index 6ae3b66097e..9fa30439563 100644
--- a/website/docs/quickstarts/snowflake-qs.md
+++ b/website/docs/quickstarts/snowflake-qs.md
@@ -395,9 +395,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 ## Build models on top of sources
 
diff --git a/website/docs/quickstarts/starburst-galaxy-qs.md b/website/docs/quickstarts/starburst-galaxy-qs.md
index eb3691d4e04..783a91c47da 100644
--- a/website/docs/quickstarts/starburst-galaxy-qs.md
+++ b/website/docs/quickstarts/starburst-galaxy-qs.md
@@ -293,11 +293,11 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs
 
-<FAQ src="Runs/checking-logs" />
-<FAQ src="Project/which-schema" />
-<FAQ src="Models/create-a-schema" />
-<FAQ src="Models/run-downtime" />
-<FAQ src="Troubleshooting/sql-errors" />
+<FAQ path="Runs/checking-logs" />
+<FAQ path="Project/which-schema" />
+<FAQ path="Models/create-a-schema" />
+<FAQ path="Models/run-downtime" />
+<FAQ path="Troubleshooting/sql-errors" />
 
 ## Change the way your model is materialized
 
@@ -401,9 +401,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 #### FAQs {#faq-2}
 
-<FAQ src="Runs/run-one-model" />
-<FAQ src="Models/unique-model-names" />
-<FAQ src="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
+<FAQ path="Runs/run-one-model" />
+<FAQ path="Models/unique-model-names" />
+<FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 <Snippet src="quickstarts/test-and-document-your-project" />
 
diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 7b3f18a9c15..5cb4c4ec9ab 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -43,4 +43,4 @@ The command accesses the data platform to cache related metadata, and to run int
 
 
 ### FAQs
-<FAQ src="Warehouse/db-connection-dbt-compile" />
+<FAQ path="Warehouse/db-connection-dbt-compile" />
diff --git a/website/docs/reference/configs-and-properties.md b/website/docs/reference/configs-and-properties.md
index 1001d7b177e..c2ad5b77629 100644
--- a/website/docs/reference/configs-and-properties.md
+++ b/website/docs/reference/configs-and-properties.md
@@ -161,12 +161,12 @@ You can find an exhaustive list of each supported property and config, broken do
 * Exposure [properties](/reference/exposure-properties)
 
 ## FAQs
-<FAQ src="Project/schema-yml-name" />
-<FAQ src="Project/resource-yml-name" />
-<FAQ src="Project/multiple-resource-yml-files" />
-<FAQ src="Project/properties-not-in-config" />
-<FAQ src="Project/why-version-2" />
-<FAQ src="Project/yaml-file-extension" />
+<FAQ path="Project/schema-yml-name" />
+<FAQ path="Project/resource-yml-name" />
+<FAQ path="Project/multiple-resource-yml-files" />
+<FAQ path="Project/properties-not-in-config" />
+<FAQ path="Project/why-version-2" />
+<FAQ path="Project/yaml-file-extension" />
 
 ## Troubleshooting common errors
 
diff --git a/website/docs/reference/dbt-jinja-functions/dispatch.md b/website/docs/reference/dbt-jinja-functions/dispatch.md
index d615bbdb430..a165ae59eb0 100644
--- a/website/docs/reference/dbt-jinja-functions/dispatch.md
+++ b/website/docs/reference/dbt-jinja-functions/dispatch.md
@@ -266,4 +266,4 @@ In rare cases, the child adapter may prefer the default implementation to its pa
 
 ## FAQs
 
-<FAQ src="Troubleshooting/dispatch-could-not-find-package" />
+<FAQ path="Troubleshooting/dispatch-could-not-find-package" />
diff --git a/website/docs/reference/resource-configs/full_refresh.md b/website/docs/reference/resource-configs/full_refresh.md
index bc875c2ad2b..f75fe3a583b 100644
--- a/website/docs/reference/resource-configs/full_refresh.md
+++ b/website/docs/reference/resource-configs/full_refresh.md
@@ -85,7 +85,7 @@ This logic is encoded in the [`should_full_refresh()`](https://github.com/dbt-la
 
 ### Seeds
 
-<FAQ src="Seeds/full-refresh-seed" />
+<FAQ path="Seeds/full-refresh-seed" />
 
 ## Recommendation
 Set `full_refresh: false` for models of especially large datasets, which you would _never_ want dbt to fully drop and recreate.
diff --git a/website/docs/reference/resource-configs/target_schema.md b/website/docs/reference/resource-configs/target_schema.md
index c612a99604d..041f004e20c 100644
--- a/website/docs/reference/resource-configs/target_schema.md
+++ b/website/docs/reference/resource-configs/target_schema.md
@@ -35,7 +35,7 @@ On **BigQuery**, this is analogous to a `dataset`.
 This is a **required** parameter, no default is provided.
 
 ## FAQs
-<FAQ src="Snapshots/snapshot-target-schema" />
+<FAQ path="Snapshots/snapshot-target-schema" />
 
 ## Examples
 ### Build all snapshots in a schema named `snapshots`
diff --git a/website/snippets/quickstarts/change-way-model-materialized.md b/website/snippets/quickstarts/change-way-model-materialized.md
index 04f92aac9a7..08d1b5ca0d8 100644
--- a/website/snippets/quickstarts/change-way-model-materialized.md
+++ b/website/snippets/quickstarts/change-way-model-materialized.md
@@ -59,6 +59,6 @@ By default, everything gets created as a view. You can override that by material
 
 #### FAQs
 
-<FAQ src="Models/available-materializations" />
-<FAQ src="Project/which-materialization" />
-<FAQ src="Models/available-configurations" />
+<FAQ path="Models/available-materializations" />
+<FAQ path="Project/which-materialization" />
+<FAQ path="Models/available-configurations" />
diff --git a/website/snippets/quickstarts/delete-example-models.md b/website/snippets/quickstarts/delete-example-models.md
index e290877437c..b2ce36a40f2 100644
--- a/website/snippets/quickstarts/delete-example-models.md
+++ b/website/snippets/quickstarts/delete-example-models.md
@@ -31,5 +31,5 @@ You can now delete the files that dbt created when you initialized the project:
 
 #### FAQs
 
-<FAQ src="Models/removing-deleted-models" />
-<FAQ src="Troubleshooting/unused-model-configurations" />
+<FAQ path="Models/removing-deleted-models" />
+<FAQ path="Troubleshooting/unused-model-configurations" />
diff --git a/website/snippets/quickstarts/schedule-a-job.md b/website/snippets/quickstarts/schedule-a-job.md
index 55504636192..59d428bdfaa 100644
--- a/website/snippets/quickstarts/schedule-a-job.md
+++ b/website/snippets/quickstarts/schedule-a-job.md
@@ -40,6 +40,6 @@ Congratulations 🎉! You've just deployed your first dbt project!
 
 #### FAQs
 
-<FAQ src="Runs/failed-prod-run" />
+<FAQ path="Runs/failed-prod-run" />
 
 
diff --git a/website/snippets/quickstarts/test-and-document-your-project.md b/website/snippets/quickstarts/test-and-document-your-project.md
index e5315b13a09..932f58712af 100644
--- a/website/snippets/quickstarts/test-and-document-your-project.md
+++ b/website/snippets/quickstarts/test-and-document-your-project.md
@@ -10,6 +10,6 @@
 
 #### FAQs
 
-<FAQ src="Docs/long-descriptions" />
-<FAQ src="Docs/sharing-documentation" />
+<FAQ path="Docs/long-descriptions" />
+<FAQ path="Docs/sharing-documentation" />
 
diff --git a/website/snippets/tutorial-add-tests-to-models.md b/website/snippets/tutorial-add-tests-to-models.md
index 937a444a1f0..491fc72ba85 100644
--- a/website/snippets/tutorial-add-tests-to-models.md
+++ b/website/snippets/tutorial-add-tests-to-models.md
@@ -52,10 +52,10 @@ When you run `dbt test`, dbt iterates through your YAML files, and constructs a
 
 #### FAQs
 
-<FAQ src="Tests/available-tests" alt_header="What tests are available for me to use in dbt? Can I add my own custom tests?" />
-<FAQ src="Tests/test-one-model" />
-<FAQ src="Runs/failed-tests" />
-<FAQ src="Project/schema-yml-name" alt_header="Does my test file need to be named `schema.yml`?" />
-<FAQ src="Project/why-version-2" />
-<FAQ src="Tests/recommended-tests" />
-<FAQ src="Tests/when-to-test" />
+<FAQ path="Tests/available-tests" alt_header="What tests are available for me to use in dbt? Can I add my own custom tests?" />
+<FAQ path="Tests/test-one-model" />
+<FAQ path="Runs/failed-tests" />
+<FAQ path="Project/schema-yml-name" alt_header="Does my test file need to be named `schema.yml`?" />
+<FAQ path="Project/why-version-2" />
+<FAQ path="Tests/recommended-tests" />
+<FAQ path="Tests/when-to-test" />
diff --git a/website/src/components/faqs/index.js b/website/src/components/faqs/index.js
index e1f1e5b2cf4..52c4573d883 100644
--- a/website/src/components/faqs/index.js
+++ b/website/src/components/faqs/index.js
@@ -2,18 +2,18 @@ import React, { useState, useEffect } from 'react';
 import styles from './styles.module.css';
 import { usePluginData } from '@docusaurus/useGlobalData';
 
-function FAQ({ src, alt_header = null }) {
+function FAQ({ path, alt_header = null }) {
 
   const [isOn, setOn] = useState(false);
-  const [filePath, setFilePath] = useState(src)
+  const [filePath, setFilePath] = useState(path)
   const [fileContent, setFileContent] = useState({})
 
   // Get all faq file paths from plugin
   const { faqFiles } = usePluginData('docusaurus-build-global-data-plugin');
 
   useEffect(() => {
-    // Search for faq where frontmatter ID matches src prop
-    const faqFile = faqFiles.find(file => file.id === src)
+    // Search for faq where frontmatter ID matches path prop
+    const faqFile = faqFiles.find(file => file.id === path)
 
     // If faqFile found with ID, set filePath for this file
     if (faqFile?.id) {
diff --git a/website/src/pages/styles.js b/website/src/pages/styles.js
index 6e65d9775c3..d0bcf2f227c 100644
--- a/website/src/pages/styles.js
+++ b/website/src/pages/styles.js
@@ -95,9 +95,9 @@ description: "this is \[an escaped link](docs.getdbt.com)"
 
             <div className='section' style={{marginTop: '40px'}}>
                 <h1>FAQ</h1>
-<pre>{`<FAQ src='Troubleshooting/sql-errors' alt_header="a header" />`}</pre>
-                <FAQ src='Troubleshooting/sql-errors' />
-                <FAQ src='Troubleshooting/sql-errors' alt_header="an overriden header" />
+<pre>{`<FAQ path='Troubleshooting/sql-errors' alt_header="a header" />`}</pre>
+                <FAQ path='Troubleshooting/sql-errors' />
+                <FAQ path='Troubleshooting/sql-errors' alt_header="an overriden header" />
             </div>
 
             <div className='section' style={{marginTop: '40px'}}>

From a05e0d726a216d199e6915432542277d0d43f910 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 14:25:45 -0400
Subject: [PATCH 014/703] update snippet prop name

---
 contributing/single-sourcing-content.md          |  4 ++--
 website/docs/docs/build/hooks-operations.md      |  2 +-
 website/docs/docs/build/incremental-models.md    |  2 +-
 website/docs/docs/build/jinja-macros.md          |  2 +-
 website/docs/docs/build/materializations.md      |  2 +-
 website/docs/docs/build/metrics.md               |  2 +-
 website/docs/docs/build/project-variables.md     |  2 +-
 website/docs/docs/cloud/about-cloud/tenancy.md   |  2 +-
 .../connect-apache-spark.md                      |  2 +-
 .../connect-data-platform/connect-snowflake.md   |  2 +-
 .../connect-starburst-trino.md                   |  4 ++--
 .../docs/docs/cloud/git/connect-azure-devops.md  |  2 +-
 website/docs/docs/cloud/git/setup-azure.md       |  2 +-
 .../docs/cloud/manage-access/auth0-migration.md  |  2 +-
 .../set-up-sso-azure-active-directory.md         |  2 +-
 .../manage-access/set-up-sso-google-workspace.md |  2 +-
 .../docs/cloud/manage-access/set-up-sso-okta.md  |  4 ++--
 .../cloud/manage-access/set-up-sso-saml-2.0.md   | 14 +++++++-------
 .../docs/cloud/manage-access/sso-overview.md     |  2 +-
 .../core/connect-data-platform/bigquery-setup.md |  2 +-
 .../connect-data-platform/databricks-setup.md    |  2 +-
 .../core/connect-data-platform/postgres-setup.md |  2 +-
 .../core/connect-data-platform/redshift-setup.md |  2 +-
 .../connect-data-platform/snowflake-setup.md     |  2 +-
 .../core/connect-data-platform/spark-setup.md    |  4 ++--
 .../core/connect-data-platform/trino-setup.md    |  6 +++---
 .../docs/dbt-cloud-apis/discovery-querying.md    |  2 +-
 .../schema-discovery-environment.mdx             |  2 +-
 .../schema-discovery-modelByEnv.mdx              |  2 +-
 website/docs/docs/dbt-versions/core-versions.md  |  4 ++--
 .../docs/dbt-versions/upgrade-core-in-cloud.md   |  4 ++--
 website/docs/docs/deploy/job-notifications.md    |  2 +-
 website/docs/docs/introduction.md                |  2 +-
 .../use-dbt-semantic-layer/dbt-semantic-layer.md |  4 ++--
 .../quickstart-semantic-layer.md                 |  6 +++---
 .../set-dbt-semantic-layer.md                    |  6 +++---
 website/docs/faqs/Accounts/slack.md              |  2 +-
 .../orchestration/webhooks/zapier-ms-teams.md    |  4 ++--
 .../webhooks/zapier-new-cloud-job.md             |  4 ++--
 .../orchestration/webhooks/zapier-slack.md       |  4 ++--
 website/docs/quickstarts/bigquery-qs.md          | 12 ++++++------
 website/docs/quickstarts/databricks-qs.md        | 12 ++++++------
 website/docs/quickstarts/manual-install-qs.md    | 16 ++++++++--------
 website/docs/quickstarts/redshift-qs.md          | 12 ++++++------
 website/docs/quickstarts/snowflake-qs.md         | 14 +++++++-------
 website/docs/quickstarts/starburst-galaxy-qs.md  | 12 ++++++------
 .../docs/reference/dbt-jinja-functions/this.md   |  2 +-
 website/docs/reference/node-selection/syntax.md  |  2 +-
 .../project-configs/on-run-start-on-run-end.md   |  2 +-
 .../resource-configs/bigquery-configs.md         |  4 ++--
 .../docs/reference/resource-configs/grants.md    |  2 +-
 .../resource-configs/pre-hook-post-hook.md       |  8 ++++----
 .../reference/resource-configs/spark-configs.md  |  2 +-
 .../test-and-document-your-project.md            |  4 ++--
 website/snippets/tutorial-managed-repo.md        |  4 ++--
 website/src/components/snippet/index.js          |  4 ++--
 56 files changed, 118 insertions(+), 118 deletions(-)

diff --git a/contributing/single-sourcing-content.md b/contributing/single-sourcing-content.md
index 7a80e71728c..2b854dbff13 100644
--- a/contributing/single-sourcing-content.md
+++ b/contributing/single-sourcing-content.md
@@ -226,7 +226,7 @@ The Snippet component allows for content to be reusable throughout the Docs. Thi
 Creating and using a snippet requires two steps:
 
 1. Create a new markdown snippet file in the `website/snippets` directory.
-2. Use the `<Snippet src="filename" />` component within a Docs file.
+2. Use the `<Snippet path="filename" />` component within a Docs file.
 
 ### Snippet properties
 
@@ -247,7 +247,7 @@ Now, we can add this snippet to a Docs file with the Snippet component:
 ```markdown
 Docs content here.
 
-<Snippet src="lorem-ipsum" />
+<Snippet path="lorem-ipsum" />
 
 Docs content here.
 ```
diff --git a/website/docs/docs/build/hooks-operations.md b/website/docs/docs/build/hooks-operations.md
index 660a8d379c5..1abc5657bad 100644
--- a/website/docs/docs/build/hooks-operations.md
+++ b/website/docs/docs/build/hooks-operations.md
@@ -38,7 +38,7 @@ Hooks are snippets of SQL that are executed at different times:
 
 Hooks are a more-advanced capability that enable you to run custom SQL, and leverage database-specific actions, beyond what dbt makes available out-of-the-box with standard materializations and configurations.
 
-<Snippet src="hooks-to-grants" />
+<Snippet path="hooks-to-grants" />
 
 <VersionBlock firstVersion="1.2">
 
diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index 15b24520711..28345ba1873 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -456,5 +456,5 @@ The syntax depends on how you configure your `incremental_strategy`:
 
 </VersionBlock>
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="incremental"/>
diff --git a/website/docs/docs/build/jinja-macros.md b/website/docs/docs/build/jinja-macros.md
index 8375eed594f..a4122dfe09b 100644
--- a/website/docs/docs/build/jinja-macros.md
+++ b/website/docs/docs/build/jinja-macros.md
@@ -185,5 +185,5 @@ Writing a macro for the first time? Check whether we've open sourced one in [dbt
 {% endfor %}
 ```
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="wee"/>
diff --git a/website/docs/docs/build/materializations.md b/website/docs/docs/build/materializations.md
index d639e9f3ebf..70c7878bd69 100644
--- a/website/docs/docs/build/materializations.md
+++ b/website/docs/docs/build/materializations.md
@@ -179,6 +179,6 @@ def model(dbt, session):
 
 **Note:** Incremental models are supported on BigQuery/Dataproc for the `merge` incremental strategy. The `insert_overwrite` strategy is not yet supported.
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="materialization"/>
 
diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index 4f24dface7e..8475cfe8684 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -714,5 +714,5 @@ The above example will return a dataset that contains the metric provided in the
 
 </VersionBlock>
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="metrics"/>
diff --git a/website/docs/docs/build/project-variables.md b/website/docs/docs/build/project-variables.md
index b6e1b564ec8..a69132d6a3b 100644
--- a/website/docs/docs/build/project-variables.md
+++ b/website/docs/docs/build/project-variables.md
@@ -120,5 +120,5 @@ If dbt is unable to find a definition for a variable after checking these four p
 
 **Note:** Variable scope is based on the node ultimately using that variable. Imagine the case where a model defined in the root project is calling a macro defined in an installed package. That macro, in turn, uses the value of a variable. The variable will be resolved based on the _root project's_ scope, rather than the package's scope.
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="variables"/>
diff --git a/website/docs/docs/cloud/about-cloud/tenancy.md b/website/docs/docs/cloud/about-cloud/tenancy.md
index 0d312767b82..5785533264e 100644
--- a/website/docs/docs/cloud/about-cloud/tenancy.md
+++ b/website/docs/docs/cloud/about-cloud/tenancy.md
@@ -24,4 +24,4 @@ _To learn more about setting up a dbt Cloud single tenant deployment, [please co
 
 ### Available features
 
-<Snippet src="cloud-feature-parity" />
+<Snippet path="cloud-feature-parity" />
diff --git a/website/docs/docs/cloud/connect-data-platform/connect-apache-spark.md b/website/docs/docs/cloud/connect-data-platform/connect-apache-spark.md
index f9a48233977..670b628547b 100644
--- a/website/docs/docs/cloud/connect-data-platform/connect-apache-spark.md
+++ b/website/docs/docs/cloud/connect-data-platform/connect-apache-spark.md
@@ -5,7 +5,7 @@ description: "Setup instructions for connecting Apache Spark to dbt Cloud"
 sidebar_label: "Connect Apache Spark"
 ---
 
-<Snippet src="dbt-databricks-for-databricks" />
+<Snippet path="dbt-databricks-for-databricks" />
 
 :::note
 See [Connect Databricks](#connect-databricks) for the Databricks version of this page.
diff --git a/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md b/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md
index 600e1dddaa3..4f31c56e8aa 100644
--- a/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md
+++ b/website/docs/docs/cloud/connect-data-platform/connect-snowflake.md
@@ -9,7 +9,7 @@ The following fields are required when creating a Snowflake connection
 
 | Field | Description | Examples |
 | ----- | ----------- | -------- |
-| Account | The Snowflake account to connect to. Take a look [here](/docs/core/connect-data-platform/snowflake-setup#account) to determine what the account field should look like based on your region.| <Snippet src="snowflake-acct-name" /> |
+| Account | The Snowflake account to connect to. Take a look [here](/docs/core/connect-data-platform/snowflake-setup#account) to determine what the account field should look like based on your region.| <Snippet path="snowflake-acct-name" /> |
 | Role | A mandatory field indicating what role should be assumed after connecting to Snowflake | `transformer` |
 | Database | The logical database to connect to and run queries against. | `analytics` |
 | Warehouse | The virtual warehouse to use for running queries. | `transforming` |
diff --git a/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md b/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md
index 224cf4d9691..db0d3f61728 100644
--- a/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md
+++ b/website/docs/docs/cloud/connect-data-platform/connect-starburst-trino.md
@@ -17,11 +17,11 @@ The following are the required fields for setting up a connection with a [Starbu
 
 ## Roles in Starburst Enterprise
 
-<Snippet src="connect-starburst-trino/roles-starburst-enterprise" />
+<Snippet path="connect-starburst-trino/roles-starburst-enterprise" />
 
 ## Catalogs and schemas
 
-<Snippet src="connect-starburst-trino/schema-db-fields" />
+<Snippet path="connect-starburst-trino/schema-db-fields" />
 
 ## Configuration
 
diff --git a/website/docs/docs/cloud/git/connect-azure-devops.md b/website/docs/docs/cloud/git/connect-azure-devops.md
index 7e656a11f7e..a84e593a1e2 100644
--- a/website/docs/docs/cloud/git/connect-azure-devops.md
+++ b/website/docs/docs/cloud/git/connect-azure-devops.md
@@ -3,7 +3,7 @@ title: "Connect to Azure DevOps"
 id: "connect-azure-devops"
 ---
 
-<Snippet src="available-enterprise-tier-only" />
+<Snippet path="available-enterprise-tier-only" />
 
 
 ## About Azure DevOps and dbt Cloud
diff --git a/website/docs/docs/cloud/git/setup-azure.md b/website/docs/docs/cloud/git/setup-azure.md
index 363e63eb088..9eca77d7014 100644
--- a/website/docs/docs/cloud/git/setup-azure.md
+++ b/website/docs/docs/cloud/git/setup-azure.md
@@ -5,7 +5,7 @@ description: "You can set up your Azure DevOps by creating an Azure AD app and a
 sidebar_label: "Set up Azure DevOps"
 ---
 
-<Snippet src="available-enterprise-tier-only" />
+<Snippet path="available-enterprise-tier-only" />
 
 ## Overview
 
diff --git a/website/docs/docs/cloud/manage-access/auth0-migration.md b/website/docs/docs/cloud/manage-access/auth0-migration.md
index af430772ca4..68598d92f91 100644
--- a/website/docs/docs/cloud/manage-access/auth0-migration.md
+++ b/website/docs/docs/cloud/manage-access/auth0-migration.md
@@ -16,7 +16,7 @@ If you have not yet configured SSO in dbt Cloud, refer instead to our setup guid
 
 ## Auth0 Multi-tenant URIs
 
-<Snippet src="auth0-uri" />
+<Snippet path="auth0-uri" />
 
 ## Start the migration
 
diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md b/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
index 13f49422832..fcc9a79e860 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
@@ -156,7 +156,7 @@ To complete setup, follow the steps below in the dbt Cloud application.
     here, you can navigate to the login URL generated for your account's _slug_ to
     test logging in with Azure AD.
 
-<Snippet src="login_url_note" />
+<Snippet path="login_url_note" />
 
 
 
diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
index 314d1128cb0..1cfa9a49286 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
@@ -113,7 +113,7 @@ Settings.
 
 If the verification information looks appropriate, then you have completed the configuration of GSuite SSO. 
 
-<Snippet src="login_url_note" />
+<Snippet path="login_url_note" />
 
 ## Setting up RBAC
 Now you have completed setting up SSO with GSuite, the next steps will be to set up
diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-okta.md b/website/docs/docs/cloud/manage-access/set-up-sso-okta.md
index 70de8285450..0d493bcf29f 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-okta.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-okta.md
@@ -72,7 +72,7 @@ dashes. It should contain only letters, numbers, and dashes. For example, the _l
 `dbt-labs`. Login slugs must be unique across all dbt Cloud accounts,
 so pick a slug that uniquely identifies your company.
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 * **Single sign on URL**: `https://YOUR_AUTH0_URI/login/callback?connection=<login slug>`
 * **Audience URI (SP Entity ID)**: `urn:auth0:<YOUR_AUTH0_ENTITYID>:{login slug}`
@@ -187,7 +187,7 @@ configured in the steps above.
     test logging in with Okta. Additionally, users added the the Okta app
     will be able to log in to dbt Cloud from Okta directly.
 
-<Snippet src="login_url_note" />
+<Snippet path="login_url_note" />
 
 
 ## Setting up RBAC
diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
index 297e92600f7..d5a16e91792 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
@@ -18,7 +18,7 @@ provider in order to configure Single Sign On and [role-based access control](/d
 
 ## Auth0 Multi-tenant URIs
 
-<Snippet src="auth0-uri" />
+<Snippet path="auth0-uri" />
 
 ## Generic SAML 2.0 integrations
 
@@ -39,7 +39,7 @@ You'll need administrator access to your SAML 2.0 compliant identity provider to
 
 #### Configuring the application
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 To complete this section, you will need to create a login slug. This slug controls the URL where users on your account can log into your application. Login slugs are typically the lowercased name of your organization. It should contain only letters, numbers, and dashes.
 separated with dashes. For example, the login slug for dbt Labs would be `dbt-labs`.
@@ -117,7 +117,7 @@ You can use the instructions in this section to configure Okta as your identity
 
 ### Configure the Okta application
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 To complete this section, you will need to create a login slug. This slug controls the URL where users on your account can log into your application. Login slugs are typically the lowercased name of your organization. It should contain only letters, numbers, and dashes.
 separated with dashes. For example, the login slug for dbt Labs would be `dbt-labs`.
@@ -220,7 +220,7 @@ Use this section if you are configuring Google as your identity provider.
 
 ### Configure the Google application
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 To complete this section, you will need to create a login slug. This slug controls the URL where users on your account
 can log into your application. Login slugs are typically the lowercased name of your organization
@@ -288,7 +288,7 @@ If you're using Azure Active Directory (Azure AD), the instructions below will h
 
 ### Create Azure AD Enterprise application
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 To complete this section, you will need to create a login slug. This slug controls the URL where users on your account can log into your application. Login slugs are typically the lowercased name of your organization
 separated with dashes. It should contain only letters, numbers, and dashes. For example, the login slug for dbt Labs would be `dbt-labs`.
@@ -352,7 +352,7 @@ To configure OneLogin, you will need **Administrator** access.
 
 ### Configure the OneLogin application
 
-<Snippet src="access_url" />
+<Snippet path="access_url" />
 
 To complete this section, you will need to create a login slug. This slug controls the URL where users on your account can log into your application. Login slugs are typically the lowercased name of your organization
 separated with dashes. It should contain only letters, numbers, and dashes. For example, the login slug for dbt Labs would be `dbt-labs`.
@@ -432,7 +432,7 @@ To complete setup, follow the steps below in dbt Cloud:
 5. After completing the setup, you can navigate to the URL generated for your account's _slug_ to test logging in with your identity provider. Additionally, users added the the SAML 2.0 app will be able to log in to dbt Cloud from the IdP directly.
 
 
-<Snippet src="login_url_note" />
+<Snippet path="login_url_note" />
 
 ### Setting up RBAC
 
diff --git a/website/docs/docs/cloud/manage-access/sso-overview.md b/website/docs/docs/cloud/manage-access/sso-overview.md
index b129b40c029..7e44859c73a 100644
--- a/website/docs/docs/cloud/manage-access/sso-overview.md
+++ b/website/docs/docs/cloud/manage-access/sso-overview.md
@@ -17,7 +17,7 @@ Once you configure SSO, even partially, you cannot disable or revert it. When yo
 
 ## Auth0 Multi-tenant URIs
 
-<Snippet src="auth0-uri" />
+<Snippet path="auth0-uri" />
 
 ## SSO process
 
diff --git a/website/docs/docs/core/connect-data-platform/bigquery-setup.md b/website/docs/docs/core/connect-data-platform/bigquery-setup.md
index 99ae473b0e2..b0fc9fa7cf0 100644
--- a/website/docs/docs/core/connect-data-platform/bigquery-setup.md
+++ b/website/docs/docs/core/connect-data-platform/bigquery-setup.md
@@ -16,7 +16,7 @@ meta:
 ---
 
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
diff --git a/website/docs/docs/core/connect-data-platform/databricks-setup.md b/website/docs/docs/core/connect-data-platform/databricks-setup.md
index eef6522a8f5..0d24a3b04aa 100644
--- a/website/docs/docs/core/connect-data-platform/databricks-setup.md
+++ b/website/docs/docs/core/connect-data-platform/databricks-setup.md
@@ -16,7 +16,7 @@ meta:
   config_page: '/reference/resource-configs/databricks-configs'
 --- 
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
diff --git a/website/docs/docs/core/connect-data-platform/postgres-setup.md b/website/docs/docs/core/connect-data-platform/postgres-setup.md
index a6948e6f1ad..5d7467c786d 100644
--- a/website/docs/docs/core/connect-data-platform/postgres-setup.md
+++ b/website/docs/docs/core/connect-data-platform/postgres-setup.md
@@ -16,7 +16,7 @@ meta:
   config_page: '/reference/resource-configs/postgres-configs'
 ---
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 7d5fdbf7a97..a86bc7df849 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -16,7 +16,7 @@ meta:
   config_page: '/reference/resource-configs/redshift-configs'
 ---
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
diff --git a/website/docs/docs/core/connect-data-platform/snowflake-setup.md b/website/docs/docs/core/connect-data-platform/snowflake-setup.md
index 147cfb87867..6bc9c980922 100644
--- a/website/docs/docs/core/connect-data-platform/snowflake-setup.md
+++ b/website/docs/docs/core/connect-data-platform/snowflake-setup.md
@@ -16,7 +16,7 @@ meta:
   config_page: '/reference/resource-configs/snowflake-configs'
 ---
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
diff --git a/website/docs/docs/core/connect-data-platform/spark-setup.md b/website/docs/docs/core/connect-data-platform/spark-setup.md
index 00de640ee05..2e3b5a66de8 100644
--- a/website/docs/docs/core/connect-data-platform/spark-setup.md
+++ b/website/docs/docs/core/connect-data-platform/spark-setup.md
@@ -17,8 +17,8 @@ meta:
 ---
 
 
-<Snippet src="warehouse-setups-cloud-callout" />
-<Snippet src="dbt-databricks-for-databricks" />
+<Snippet path="warehouse-setups-cloud-callout" />
+<Snippet path="dbt-databricks-for-databricks" />
 
 :::note
 See [Databricks setup](#databricks-setup) for the Databricks version of this page.
diff --git a/website/docs/docs/core/connect-data-platform/trino-setup.md b/website/docs/docs/core/connect-data-platform/trino-setup.md
index 711e735ab6d..396634dc6e6 100644
--- a/website/docs/docs/core/connect-data-platform/trino-setup.md
+++ b/website/docs/docs/core/connect-data-platform/trino-setup.md
@@ -16,7 +16,7 @@ meta:
   config_page: '/reference/resource-configs/trino-configs'
 ---
 
-<Snippet src="warehouse-setups-cloud-callout" />
+<Snippet path="warehouse-setups-cloud-callout" />
 
 <h2> Overview of {frontMatter.meta.pypi_package} </h2>
 
@@ -70,10 +70,10 @@ The following profile fields are always required except for `user`, which is als
 |   `user`   | Format for Starburst Enterprise or Trino: <br/> <ul><li>`user.name`</li><li>`user.name@mydomain.com`</li></ul><br/>Format for Starburst Galaxy:<br/> <ul><li>`user.name@mydomain.com/role`</li></ul> | The username (of the account) to log in to your cluster. When connecting to Starburst Galaxy clusters, you must include the role of the user as a suffix to the username. |
 
 ### Roles in Starburst Enterprise
-<Snippet src="connect-starburst-trino/roles-starburst-enterprise" />
+<Snippet path="connect-starburst-trino/roles-starburst-enterprise" />
 
 ### Schemas and databases
-<Snippet src="connect-starburst-trino/schema-db-fields" />
+<Snippet path="connect-starburst-trino/schema-db-fields" />
 
 ## Additional parameters
 
diff --git a/website/docs/docs/dbt-cloud-apis/discovery-querying.md b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
index 8d602e73e5f..02625eb00a3 100644
--- a/website/docs/docs/dbt-cloud-apis/discovery-querying.md
+++ b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
@@ -10,7 +10,7 @@ Use the Discovery API to evaluate data pipeline health and project state across
 
 Since GraphQL provides a description of the data in the API, the schema displayed in the GraphQL explorer accurately represents the graph and fields available to query.  
 
-<Snippet src="metadata-api-prerequisites" />
+<Snippet path="metadata-api-prerequisites" />
 
 ## Authorization
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
index fdf6aab729d..41fd5555c3f 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
@@ -6,7 +6,7 @@ id: "discovery-schema-environment"
 
 import { ArgsTable, SchemaTable } from "./schema";
 
-<Snippet src="discovery-public-preview-banner" />
+<Snippet path="discovery-public-preview-banner" />
 
 This environment object allows you to query information about a particular model based on `environmentId`.
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
index 400735bdce4..078d2512256 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
@@ -6,7 +6,7 @@ id: "discovery-schema-modelByEnv"
 
 import { ArgsTable, SchemaTable } from "./schema";
 
-<Snippet src="discovery-public-preview-banner" />
+<Snippet path="discovery-public-preview-banner" />
 
 This model by environment object allows you to query information about a particular model based on `environmentId`.
 
diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index 328b6cf4166..1e3ed62a4ec 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -8,10 +8,10 @@ dbt Core releases follow [semantic versioning](https://semver.org/) guidelines.
 
 dbt Labs provides different support levels for different versions, which may include new features, bug fixes, or security patches:
 
-<Snippet src="core-version-support" />
+<Snippet path="core-version-support" />
 
 
-<Snippet src="core-versions-table" />
+<Snippet path="core-versions-table" />
 
 ### Further reading
 
diff --git a/website/docs/docs/dbt-versions/upgrade-core-in-cloud.md b/website/docs/docs/dbt-versions/upgrade-core-in-cloud.md
index 6c9ffe5d60e..d143aab5ef1 100644
--- a/website/docs/docs/dbt-versions/upgrade-core-in-cloud.md
+++ b/website/docs/docs/dbt-versions/upgrade-core-in-cloud.md
@@ -29,11 +29,11 @@ dbt Labs has always encouraged users to upgrade dbt Core versions whenever a new
 
 We provide different support levels for different versions, which may include new features, bug fixes, or security patches:
 
-<Snippet src="core-version-support" />
+<Snippet path="core-version-support" />
 
 We'll continue to update the following release table so that users know when we plan to stop supporting different versions of Core in dbt Cloud.
 
-<Snippet src="core-versions-table" />
+<Snippet path="core-versions-table" />
 
 Starting with v1.0, dbt Cloud will ensure that you're always using the latest compatible patch release of `dbt-core` and plugins, including all the latest fixes. You may also choose to try prereleases of those patch releases before they are generally available.
 
diff --git a/website/docs/docs/deploy/job-notifications.md b/website/docs/docs/deploy/job-notifications.md
index 62a5b822069..c240ca12183 100644
--- a/website/docs/docs/deploy/job-notifications.md
+++ b/website/docs/docs/deploy/job-notifications.md
@@ -22,4 +22,4 @@ There are two options for setting up email notifications. As a **user**, you can
 
 ### Slack
 
-<Snippet src="slack-notifications-config-steps" />
+<Snippet path="slack-notifications-config-steps" />
diff --git a/website/docs/docs/introduction.md b/website/docs/docs/introduction.md
index a28e401feb4..c4cfd6e45ac 100644
--- a/website/docs/docs/introduction.md
+++ b/website/docs/docs/introduction.md
@@ -3,7 +3,7 @@ title: "What is dbt?"
 id: "introduction"
 ---
 
-<Snippet src="what-is-dbt-intro" />
+<Snippet path="what-is-dbt-intro" />
 
 dbt compiles and runs your analytics code against your data platform, enabling you and your team to collaborate on a single source of truth for metrics, insights, and business definitions. This single source of truth, combined with the ability to define tests for your data, reduces errors when logic changes, and alerts you when issues arise.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
index 5fe781ffeb6..95962610f8d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
@@ -35,9 +35,9 @@ The dbt Semantic Layer reduces code duplication and inconsistency regarding your
 ## Prerequisites
 To use the dbt Semantic Layer, you’ll need to meet the following:
 
-<Snippet src="sl-prerequisites" />
+<Snippet path="sl-prerequisites" />
 
-<Snippet src="sl-considerations-banner" />
+<Snippet path="sl-considerations-banner" />
 
 ## Public Preview
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
index 19a5fb15057..af8de189fa9 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
@@ -42,9 +42,9 @@ To try out the features of the dbt Semantic Layer, you first need to have a dbt
 ## Prerequisites
 To use the dbt Semantic Layer, you’ll need to meet the following:
 
-<Snippet src="sl-prerequisites" />
+<Snippet path="sl-prerequisites" />
 
-<Snippet src="sl-considerations-banner" />
+<Snippet path="sl-considerations-banner" />
 
 
 :::info 📌 
@@ -210,7 +210,7 @@ Once you’ve defined metrics in your dbt project, you can perform a job run in
 
 ## Set up dbt Semantic Layer
     
-<Snippet src="sl-set-up-steps" />
+<Snippet path="sl-set-up-steps" />
 
       
 ## Troubleshooting
diff --git a/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
index b045725ca62..9d0c1eee752 100644
--- a/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
@@ -19,15 +19,15 @@ With the dbt Semantic Layer, you'll be able to centrally define business metrics
 
 Before you set up the dbt Semantic Layer, make sure you meet the following:
 
-<Snippet src="sl-prerequisites" />
+<Snippet path="sl-prerequisites" />
 
 
-<Snippet src="sl-considerations-banner" />
+<Snippet path="sl-considerations-banner" />
 
 
 ## Set up dbt Semantic Layer
 
-<Snippet src="sl-set-up-steps" />
+<Snippet path="sl-set-up-steps" />
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
 
diff --git a/website/docs/faqs/Accounts/slack.md b/website/docs/faqs/Accounts/slack.md
index 01001141e2e..4faa60fb09a 100644
--- a/website/docs/faqs/Accounts/slack.md
+++ b/website/docs/faqs/Accounts/slack.md
@@ -5,4 +5,4 @@ sidebar_label: 'How to set up Slack'
 id: slack
 ---
 
-<Snippet src="slack-notifications-config-steps" />
+<Snippet path="slack-notifications-config-steps" />
diff --git a/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md b/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
index 5c622d69f21..aa95b999d4c 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
@@ -47,7 +47,7 @@ In the next step, you will need the Webhook Secret Key from the prior step, and
 
 Zapier allows you to [store secrets](https://help.zapier.com/hc/en-us/articles/8496293271053-Save-and-retrieve-data-from-Zaps), which prevents your keys from being displayed in plaintext in the Zap code. You will be able to access them via the [StoreClient utility](https://help.zapier.com/hc/en-us/articles/8496293969549-Store-data-from-code-steps-with-StoreClient).
 
-<Snippet src="webhook_guide_zapier_secret_store" />
+<Snippet path="webhook_guide_zapier_secret_store" />
 
 ### 5. Add a code action
 Select **Code by Zapier** as the App, and **Run Python** as the Event. 
@@ -156,4 +156,4 @@ When you're happy with it, remember to ensure that your `run_id` and `account_id
 ## Other notes
 - If you post to a chat instead of a team channel, you don't need to add the Zapier app to Microsoft Teams.
 - If you post to a chat instead of a team channel, note that markdown is not supported and you will need to remove the markdown formatting. 
-- If you chose the **Catch Hook** trigger instead of **Catch Raw Hook**, you will need to pass each required property from the webhook as an input instead of running `json.loads()` against the raw body. You will also need to remove the validation code. 
\ No newline at end of file
+- If you chose the **Catch Hook** trigger instead of **Catch Raw Hook**, you will need to pass each required property from the webhook as an input instead of running `json.loads()` against the raw body. You will also need to remove the validation code. 
diff --git a/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md b/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
index 75897c30150..49b01d0db7e 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
@@ -36,7 +36,7 @@ In the next step, you will need the Webhook Secret Key from the prior step, and
 
 Zapier allows you to [store secrets](https://help.zapier.com/hc/en-us/articles/8496293271053-Save-and-retrieve-data-from-Zaps), which prevents your keys from being displayed in plaintext in the Zap code. You will be able to access them via the [StoreClient utility](https://help.zapier.com/hc/en-us/articles/8496293969549-Store-data-from-code-steps-with-StoreClient).
 
-<Snippet src="webhook_guide_zapier_secret_store" />
+<Snippet path="webhook_guide_zapier_secret_store" />
 
 ### 4. Add a code action
 Select **Code by Zapier** as the App, and **Run Python** as the Event. 
@@ -88,4 +88,4 @@ return
 ```
 
 ### 5. Test and deploy
-When you're happy with it, remember to ensure that your `account_id` is no longer hardcoded, then publish your Zap.
\ No newline at end of file
+When you're happy with it, remember to ensure that your `account_id` is no longer hardcoded, then publish your Zap.
diff --git a/website/docs/guides/orchestration/webhooks/zapier-slack.md b/website/docs/guides/orchestration/webhooks/zapier-slack.md
index 03c3402ffe8..d3b0473502b 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-slack.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-slack.md
@@ -46,7 +46,7 @@ In the next step, you will need the Webhook Secret Key from the prior step, and
 Zapier allows you to [store secrets](https://help.zapier.com/hc/en-us/articles/8496293271053-Save-and-retrieve-data-from-Zaps). This prevents your keys from being displayed as plaintext in the Zap code. You can access them with the [StoreClient utility](https://help.zapier.com/hc/en-us/articles/8496293969549-Store-data-from-code-steps-with-StoreClient).
 
 
-<Snippet src="webhook_guide_zapier_secret_store" />
+<Snippet path="webhook_guide_zapier_secret_store" />
 
 ### 4. Add a code action
 Select **Code by Zapier** as the App, and **Run Python** as the Event. 
@@ -301,4 +301,4 @@ Set the **Message Text** to **5. Threaded Errors Post** from the Run Python step
 
 ### 8. Test and deploy
 
-When you're done testing your Zap, publish it.
\ No newline at end of file
+When you're done testing your Zap, publish it.
diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md
index 2ff0ad92606..84e3b3ae545 100644
--- a/website/docs/quickstarts/bigquery-qs.md
+++ b/website/docs/quickstarts/bigquery-qs.md
@@ -93,7 +93,7 @@ In order to let dbt connect to your warehouse, you'll need to generate a keyfile
 
 
 ## Set up a dbt Cloud managed repository 
-<Snippet src="tutorial-managed-repo" />
+<Snippet path="tutorial-managed-repo" />
 
 
 ## Initialize your dbt project​ and start developing
@@ -187,15 +187,15 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 ## Change the way your model is materialized
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ## Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ## Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -292,8 +292,8 @@ Later, you can connect your business intelligence (BI) tools to these views and
 <FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 
-<Snippet src="quickstarts/test-and-document-your-project" />
+<Snippet path="quickstarts/test-and-document-your-project" />
 
-<Snippet src="quickstarts/schedule-a-job" />
+<Snippet path="quickstarts/schedule-a-job" />
 
 
diff --git a/website/docs/quickstarts/databricks-qs.md b/website/docs/quickstarts/databricks-qs.md
index 706e87352ee..1222ef2a7d5 100644
--- a/website/docs/quickstarts/databricks-qs.md
+++ b/website/docs/quickstarts/databricks-qs.md
@@ -172,7 +172,7 @@ If you want to connect manually, refer to [Connect to dbt Cloud manually](https:
 ## Set up a dbt Cloud managed repository 
 If you used Partner Connect, you can skip to [initializing your dbt project](#initialize-your-dbt-project-and-start-developing) as the Partner Connect provides you with a managed repository. Otherwise, you will need to create your repository connection. 
 
-<Snippet src="tutorial-managed-repo" />
+<Snippet path="tutorial-managed-repo" />
 
 ## Initialize your dbt project​ and start developing
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:
@@ -265,15 +265,15 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 ## Change the way your model is materialized
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ## Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ## Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -370,6 +370,6 @@ Later, you can connect your business intelligence (BI) tools to these views and
 <FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
 
-<Snippet src="quickstarts/test-and-document-your-project" />
+<Snippet path="quickstarts/test-and-document-your-project" />
 
-<Snippet src="quickstarts/schedule-a-job" />
+<Snippet path="quickstarts/schedule-a-job" />
diff --git a/website/docs/quickstarts/manual-install-qs.md b/website/docs/quickstarts/manual-install-qs.md
index 97eb8f8e8ea..ea3c6c7ec84 100644
--- a/website/docs/quickstarts/manual-install-qs.md
+++ b/website/docs/quickstarts/manual-install-qs.md
@@ -189,7 +189,7 @@ Check out a new git branch to work on new code:
 2. Create a new SQL file in the `models` directory, named `models/customers.sql`.
 3. Paste the following query into the `models/customers.sql` file.
 
-    <Snippet src="tutorial-sql-query" />
+    <Snippet path="tutorial-sql-query" />
 
 4. From the command line, enter `dbt run`.
     <div style={{maxWidth: '400px'}}>
@@ -210,15 +210,15 @@ When you return to the BigQuery console, you can `select` from this model.
 
 
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ### Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ### Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -417,7 +417,7 @@ When you return to the BigQuery console, you can `select` from this model.
 
 ### Next steps
 
-<Snippet src="tutorial-next-steps-1st-model" />
+<Snippet path="tutorial-next-steps-1st-model" />
 
 You can also explore:
 
@@ -428,11 +428,11 @@ You can also explore:
 
 ### Add tests to your models
 
-<Snippet src="tutorial-add-tests-to-models" />
+<Snippet path="tutorial-add-tests-to-models" />
 
 ### Document your models
 
-<Snippet src="tutorial-document-your-models" />
+<Snippet path="tutorial-document-your-models" />
 
 3. Run `dbt docs serve` command to launch the documentation in a local website.
 
@@ -444,7 +444,7 @@ You can also explore:
 
 #### Next steps
 
-<Snippet src="tutorial-next-steps-tests" />
+<Snippet path="tutorial-next-steps-tests" />
 
 ### Commit updated changes
 
diff --git a/website/docs/quickstarts/redshift-qs.md b/website/docs/quickstarts/redshift-qs.md
index f672f16c4ed..fc7e178f163 100644
--- a/website/docs/quickstarts/redshift-qs.md
+++ b/website/docs/quickstarts/redshift-qs.md
@@ -188,7 +188,7 @@ Now we are going to load our sample data into the S3 bucket that our Cloudformat
 7. Click **Next** if the test succeeded. If it failed, you might need to check your Redshift settings and credentials.
 
 ## Set up a dbt Cloud managed repository 
-<Snippet src="tutorial-managed-repo" />
+<Snippet path="tutorial-managed-repo" />
 
 ## Initialize your dbt project​ and start developing
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:
@@ -281,15 +281,15 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 ## Change the way your model is materialized
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ## Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ## Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -385,7 +385,7 @@ Later, you can connect your business intelligence (BI) tools to these views and
 <FAQ path="Models/unique-model-names" />
 <FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
-<Snippet src="quickstarts/test-and-document-your-project" />
+<Snippet path="quickstarts/test-and-document-your-project" />
 
-<Snippet src="quickstarts/schedule-a-job" />
+<Snippet path="quickstarts/schedule-a-job" />
 
diff --git a/website/docs/quickstarts/snowflake-qs.md b/website/docs/quickstarts/snowflake-qs.md
index 9fa30439563..bf318a597a4 100644
--- a/website/docs/quickstarts/snowflake-qs.md
+++ b/website/docs/quickstarts/snowflake-qs.md
@@ -185,7 +185,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno
 4. Enter your **Settings** for Snowflake with: 
     * **Account** &mdash; Find your account by using the Snowflake trial account URL and removing `snowflakecomputing.com`. The order of your account information will vary by Snowflake version. For example, Snowflake's Classic console URL might look like: `oq65696.west-us-2.azure.snowflakecomputing.com`. The AppUI or Snowsight URL might look more like: `snowflakecomputing.com/west-us-2.azure/oq65696`. In both examples, your account will be: `oq65696.west-us-2.azure`. For more information, see [Account Identifiers](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html) in the Snowflake docs.  
 
-        <Snippet src="snowflake-acct-name" />
+        <Snippet path="snowflake-acct-name" />
     
     * **Role** &mdash; Leave blank for now. You can update this to a default Snowflake role later.
     * **Database** &mdash; `analytics`.  This tells dbt to create new models in the analytics database.
@@ -211,7 +211,7 @@ Using Partner Connect allows you to create a complete dbt account with your [Sno
 ## Set up a dbt Cloud managed repository 
 If you used Partner Connect, you can skip to [initializing your dbt project](#initialize-your-dbt-project-and-start-developing) as the Partner Connect provides you with a managed repository. Otherwise, you will need to create your repository connection. 
 
-<Snippet src="tutorial-managed-repo" />
+<Snippet path="tutorial-managed-repo" />
 
 ## Initialize your dbt project​ and start developing
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:
@@ -295,15 +295,15 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 ## Change the way your model is materialized
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ## Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ## Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -466,6 +466,6 @@ Sources make it possible to name and describe the data loaded into your warehous
     test and document your raw data and also understand the lineage of your sources. 
 
 
-<Snippet src="quickstarts/test-and-document-your-project" />
+<Snippet path="quickstarts/test-and-document-your-project" />
 
-<Snippet src="quickstarts/schedule-a-job" />
+<Snippet path="quickstarts/schedule-a-job" />
diff --git a/website/docs/quickstarts/starburst-galaxy-qs.md b/website/docs/quickstarts/starburst-galaxy-qs.md
index 783a91c47da..d9bd3b98a43 100644
--- a/website/docs/quickstarts/starburst-galaxy-qs.md
+++ b/website/docs/quickstarts/starburst-galaxy-qs.md
@@ -214,7 +214,7 @@ To query the Jaffle Shop data with Starburst Galaxy, you need to create tables u
 12. Click **Next** if the test succeeded. If it failed, you might need to check your Starburst Galaxy settings and credentials.
 
 ## Set up a dbt Cloud managed repository 
-<Snippet src="tutorial-managed-repo" />
+<Snippet path="tutorial-managed-repo" />
 
 ## Initialize your dbt project​ and start developing
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:
@@ -301,15 +301,15 @@ Later, you can connect your business intelligence (BI) tools to these views and
 
 ## Change the way your model is materialized
 
-<Snippet src="quickstarts/change-way-model-materialized" />
+<Snippet path="quickstarts/change-way-model-materialized" />
 
 ## Delete the example models
 
-<Snippet src="quickstarts/delete-example-models" />
+<Snippet path="quickstarts/delete-example-models" />
 
 ## Build models on top of other models
 
-<Snippet src="quickstarts/intro-build-models-atop-other-models" />
+<Snippet path="quickstarts/intro-build-models-atop-other-models" />
 
 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query.
 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query.
@@ -405,9 +405,9 @@ Later, you can connect your business intelligence (BI) tools to these views and
 <FAQ path="Models/unique-model-names" />
 <FAQ path="Project/structure-a-project" alt_header="As I create more models, how should I keep my project organized? What should I name my models?" />
 
-<Snippet src="quickstarts/test-and-document-your-project" />
+<Snippet path="quickstarts/test-and-document-your-project" />
 
-<Snippet src="quickstarts/schedule-a-job" />
+<Snippet path="quickstarts/schedule-a-job" />
 
 
 ## Connect to multiple data sources
diff --git a/website/docs/reference/dbt-jinja-functions/this.md b/website/docs/reference/dbt-jinja-functions/this.md
index 0e6208efb3c..9065c660cb0 100644
--- a/website/docs/reference/dbt-jinja-functions/this.md
+++ b/website/docs/reference/dbt-jinja-functions/this.md
@@ -15,7 +15,7 @@ description: "Represents the current model in the database."
 
 ## Examples
 
-<Snippet src="hooks-to-grants" />
+<Snippet path="hooks-to-grants" />
 
 <VersionBlock lastVersion="1.1">
 
diff --git a/website/docs/reference/node-selection/syntax.md b/website/docs/reference/node-selection/syntax.md
index 2e983f9d08f..87772262514 100644
--- a/website/docs/reference/node-selection/syntax.md
+++ b/website/docs/reference/node-selection/syntax.md
@@ -78,7 +78,7 @@ As your selection logic gets more complex, and becomes unwieldly to type out as
 consider using a [yaml selector](/reference/node-selection/yaml-selectors). You can use a predefined definition with the `--selector` flag.
 Note that when you're using `--selector`, most other flags (namely `--select` and `--exclude`) will be ignored.
 
-<Snippet src="discourse-help-feed-header" />
+<Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="node-selection"/>
 
 ## About node selection 
diff --git a/website/docs/reference/project-configs/on-run-start-on-run-end.md b/website/docs/reference/project-configs/on-run-start-on-run-end.md
index 43004795be9..2c5cde4c0c2 100644
--- a/website/docs/reference/project-configs/on-run-start-on-run-end.md
+++ b/website/docs/reference/project-configs/on-run-start-on-run-end.md
@@ -31,7 +31,7 @@ A SQL statement (or list of SQL statements) to be run at the start, or end, of t
 
 ## Examples
 
-<Snippet src="hooks-to-grants" />
+<Snippet path="hooks-to-grants" />
 
 <VersionBlock lastVersion="1.1">
 
diff --git a/website/docs/reference/resource-configs/bigquery-configs.md b/website/docs/reference/resource-configs/bigquery-configs.md
index fcebb0befdd..b3c05c1d827 100644
--- a/website/docs/reference/resource-configs/bigquery-configs.md
+++ b/website/docs/reference/resource-configs/bigquery-configs.md
@@ -728,7 +728,7 @@ view, dbt will grant the view model access to select from the list of datasets
 provided. See [BQ docs on authorized views](https://cloud.google.com/bigquery/docs/share-access-views)
 for more details.
 
-<Snippet src="grants-vs-access-to" />
+<Snippet path="grants-vs-access-to" />
 
 <File name='dbt_project.yml'>
 
@@ -803,4 +803,4 @@ models:
   path:
     materialized: materialized_view
 ```
-</File>
\ No newline at end of file
+</File>
diff --git a/website/docs/reference/resource-configs/grants.md b/website/docs/reference/resource-configs/grants.md
index f8a728abdc8..8ef726788dc 100644
--- a/website/docs/reference/resource-configs/grants.md
+++ b/website/docs/reference/resource-configs/grants.md
@@ -211,7 +211,7 @@ We encourage you to read Google's documentation for more context:
 - [Understanding GCP roles](https://cloud.google.com/iam/docs/understanding-roles)
 - [How to format grantees](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-control-language#user_list)
 
-<Snippet src="grants-vs-access-to" />
+<Snippet path="grants-vs-access-to" />
 
 ### BigQuery examples
 
diff --git a/website/docs/reference/resource-configs/pre-hook-post-hook.md b/website/docs/reference/resource-configs/pre-hook-post-hook.md
index 4f5071e7aef..1660c50049b 100644
--- a/website/docs/reference/resource-configs/pre-hook-post-hook.md
+++ b/website/docs/reference/resource-configs/pre-hook-post-hook.md
@@ -16,7 +16,7 @@ datatype: sql-statement | [sql-statement]
 
 <TabItem value="models">
 
-<Snippet src="post-and-pre-hooks-sql-statement" /> 
+<Snippet path="post-and-pre-hooks-sql-statement" /> 
 
 <File name='dbt_project.yml'>
 
@@ -51,7 +51,7 @@ select ...
 
 <TabItem value="seeds">
 
-<Snippet src="post-and-pre-hooks-sql-statement" /> 
+<Snippet path="post-and-pre-hooks-sql-statement" /> 
 
 <File name='dbt_project.yml'>
 
@@ -70,7 +70,7 @@ seeds:
 
 <TabItem value="snapshots">
 
-<Snippet src="post-and-pre-hooks-sql-statement" /> 
+<Snippet path="post-and-pre-hooks-sql-statement" /> 
 
 <File name='dbt_project.yml'>
 
@@ -124,7 +124,7 @@ dbt aims to provide all the boilerplate SQL you need (DDL, DML, and DCL) via out
 
 ## Examples
 
-<Snippet src="hooks-to-grants" />
+<Snippet path="hooks-to-grants" />
 
 <VersionBlock firstVersion="1.2">
 
diff --git a/website/docs/reference/resource-configs/spark-configs.md b/website/docs/reference/resource-configs/spark-configs.md
index 7ef25f70a3f..95a853107f6 100644
--- a/website/docs/reference/resource-configs/spark-configs.md
+++ b/website/docs/reference/resource-configs/spark-configs.md
@@ -9,7 +9,7 @@ To-do:
 - use the reference doc structure for this article/split into separate articles
 --->
 
-<Snippet src="dbt-databricks-for-databricks" />
+<Snippet path="dbt-databricks-for-databricks" />
 
 :::note
 See [Databricks configuration](#databricks-configs) for the Databricks version of this page.
diff --git a/website/snippets/quickstarts/test-and-document-your-project.md b/website/snippets/quickstarts/test-and-document-your-project.md
index 932f58712af..e8df2d6a1de 100644
--- a/website/snippets/quickstarts/test-and-document-your-project.md
+++ b/website/snippets/quickstarts/test-and-document-your-project.md
@@ -1,10 +1,10 @@
 ## Add tests to your models
 
-<Snippet src="tutorial-add-tests-to-models" />
+<Snippet path="tutorial-add-tests-to-models" />
 
 ## Document your models
 
-<Snippet src="tutorial-document-your-models" />
+<Snippet path="tutorial-document-your-models" />
 
 3. Click the book icon in the Develop interface to launch documentation in a new tab.
 
diff --git a/website/snippets/tutorial-managed-repo.md b/website/snippets/tutorial-managed-repo.md
index eb78f835fc0..78221bbb957 100644
--- a/website/snippets/tutorial-managed-repo.md
+++ b/website/snippets/tutorial-managed-repo.md
@@ -1,8 +1,8 @@
-<Snippet src="available-git-providers" />
+<Snippet path="available-git-providers" />
 
 To set up a managed repository:
 
 1. Under "Setup a repository", select **Managed**.
 2. Type a name for your repo such as `bbaggins-dbt-quickstart`
 3. Click **Create**. It will take a few seconds for your repository to be created and imported.
-4. Once you see the "Successfully imported repository," click **Continue**.
\ No newline at end of file
+4. Once you see the "Successfully imported repository," click **Continue**.
diff --git a/website/src/components/snippet/index.js b/website/src/components/snippet/index.js
index a4011385458..daf674c3d14 100644
--- a/website/src/components/snippet/index.js
+++ b/website/src/components/snippet/index.js
@@ -7,8 +7,8 @@ import clsx from 'clsx';
   Pass the filename of a snippet within the snippets directory
   as a prop to use throughout the docs.
 */}
-export default function Snippet({ src }) {
-  const file = require('../../../snippets/' + src + '.md')
+export default function Snippet({ path }) {
+  const file = require('../../../snippets/' + path + '.md')
   const contents = file.default({});
   return (
     <div className={clsx(styles.snippet, 'snippet')}>

From 738ff7a6b1a51fc1dbec92b83a67bf219334406f Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 5 Jul 2023 14:35:00 -0400
Subject: [PATCH 015/703] trigger build


From 48d5744f297747beb6d1cdb02440335cc6503b00 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 10 Jul 2023 21:17:13 +0100
Subject: [PATCH 016/703] Update sidebars.js

new sidebar
---
 website/sidebars.js | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/website/sidebars.js b/website/sidebars.js
index 3198d95e0f3..46db14dd6d4 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -404,13 +404,21 @@ const sidebarSettings = {
     {
       type: "category",
       label: "Use the dbt Semantic Layer",
-      collapsed: true,
+			link: { type: "doc", id: "docs/use-dbt-semantic-layer/dbt-sl" },
       items: [
-        "docs/use-dbt-semantic-layer/quickstart-semantic-layer",
-        "docs/use-dbt-semantic-layer/dbt-semantic-layer",
-        "docs/use-dbt-semantic-layer/setup-dbt-semantic-layer",
+				"docs/use-dbt-semantic-layer/quickstart-sl",
+        "docs/use-dbt-semantic-layer/setup-dbt-sl",
         "docs/use-dbt-semantic-layer/avail-sl-integrations",
-      ],
+        {
+          type: "category",
+          label: "Semantic Layer APIs",
+					link: { type: "doc", id: "docs/use-dbt-semantic-layer/sl-api-overview" },
+          items: [
+            "docs/use-dbt-semantic-layer/jdbc",
+            "docs/use-dbt-semantic-layer/graphql",
+          ],
+        },
+		 ],
     },
     {
       type: "category",

From 33e15d87ca11c8536ef6b503e8ceab032285d8a6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 10 Jul 2023 21:20:11 +0100
Subject: [PATCH 017/703] Update metrics.md

---
 website/docs/docs/build/metrics.md | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index c43e2a86915..4e870dc49a9 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -8,21 +8,17 @@ keywords:
 
 <VersionBlock firstVersion="1.6">
 
-:::info dbt Metrics isn't supported
+:::info dbt metrics no longer supported
 
-dbt Metrics is no longer supported in v1.6 and higher. To build your semantic layer, define and query metrics, and provide data governance - refer to [Build your Semantic Layer](/docs/build/build-metrics-intro) for updated guidance.
+dbt_metrics is no longer supported in v1.6 and higher, and has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics). [MetricFlow](/docs/build/about-metricflow?version=1.6), a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), is a new framework for defining metrics in dbt and has replaced dbt_metrics.
 
-:::
-</VersionBlock>
-
-<VersionBlock lastVersion="1.5">
+You can still use the dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and don't receive any code fixes. 
 
-:::info dbt Metrics not recommended
-
-dbt Metrics won't be supported in v1.6 and higher, and is being replaced with MetricFlow. [Defining metrics](/docs/build/build-semantic-layer-intro) with MetricFlow will help shape the future of the dbt Semantic Layer &mdash; let us know [your thoughts and join the convo](https://github.com/dbt-labs/dbt-core/discussions/7456) to help build it!
+To build your metrics and semantic layer, we **highly** recommend you [upgrade to dbt v1.6 and higher](link to migration guide) to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
 
 :::
 
+</VersionBlock>
 
 <Changelog>
 

From aaca11edb6b75bf4d2127cdf89b8ae89c73bc32f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 11:55:01 +0100
Subject: [PATCH 018/703] mirnas changes

---
 website/dbt-versions.js                       |  12 +
 website/docs/docs/build/metrics.md            |  16 +-
 .../docs/docs/dbt-cloud-apis/apis-overview.md |   3 +-
 .../avail-sl-integrations.md                  |  46 +-
 .../dbt-semantic-layer.md                     | 146 ---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 295 ++++++
 .../docs/use-dbt-semantic-layer/graphql.md    |  24 +
 .../docs/docs/use-dbt-semantic-layer/jdbc.md  |  22 +
 ...art-semantic-layer.md => quickstart-sl.md} | 199 +++-
 .../set-dbt-semantic-layer.md                 |  40 -
 .../docs/use-dbt-semantic-layer/setup-sl.md   | 107 +++
 .../use-dbt-semantic-layer/sl-api-overview.md | 101 ++
 .../use-dbt-semantic-layer/sl-manifest.md     | 878 ++++++++++++++++++
 .../docs/reference/artifacts/dbt-artifacts.md |   6 +-
 website/package-lock.json                     | 448 ++++++++-
 website/package.json                          |   1 +
 website/sidebars.js                           |  11 +-
 website/snippets/_legacy-sl-callout.md        |  13 +
 website/snippets/_v2-sl-prerequisites.md      |  52 ++
 website/static/_redirects                     |   4 +
 20 files changed, 2178 insertions(+), 246 deletions(-)
 delete mode 100644 website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/graphql.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/jdbc.md
 rename website/docs/docs/use-dbt-semantic-layer/{quickstart-semantic-layer.md => quickstart-sl.md} (57%)
 delete mode 100644 website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/setup-sl.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/sl-manifest.md
 create mode 100644 website/snippets/_legacy-sl-callout.md
 create mode 100644 website/snippets/_v2-sl-prerequisites.md

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 52b68d6c084..f2834c921b6 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -175,6 +175,18 @@ exports.versionedPages = [
     "page": "docs/build/ratio",
     "firstVersion": "1.6",
   },
+  {
+    "category": "docs/use-dbt-semantic-layer/sl-api-overview",
+    "firstVersion": "1.6",
+  },
+	{
+    "category": "docs/use-dbt-semantic-layer/graphql",
+    "firstVersion": "1.6",
+  },
+	{
+    "category": "docs/use-dbt-semantic-layer/jdbc",
+    "firstVersion": "1.6",
+  },
 ]
 
 exports.versionedCategories = [
diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index 4e870dc49a9..4492d2f5770 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -6,20 +6,16 @@ keywords:
   - dbt metrics layer
 ---
 
-<VersionBlock firstVersion="1.6">
-
 :::info dbt metrics no longer supported
 
 dbt_metrics is no longer supported in v1.6 and higher, and has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics). [MetricFlow](/docs/build/about-metricflow?version=1.6), a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), is a new framework for defining metrics in dbt and has replaced dbt_metrics.
 
 You can still use the dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and don't receive any code fixes. 
 
-To build your metrics and semantic layer, we **highly** recommend you [upgrade to dbt v1.6 and higher](link to migration guide) to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
+To build your metrics and semantic layer, we **highly** recommend you [upgrade to dbt v1.6 and higher](/guides/migration/versions/upgrading-to-v1.6) to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
 
 :::
 
-</VersionBlock>
-
 <Changelog>
 
 * **v1.3.0**: Metrics have been moved out of the experimental phase
@@ -27,6 +23,7 @@ To build your metrics and semantic layer, we **highly** recommend you [upgrade t
 
 </Changelog>
  
+<VersionBlock lastVersion="1.5">
 
 A metric is an aggregation over a <Term id="table" /> that supports zero or more dimensions. Some examples of metrics include:
 - active users
@@ -214,9 +211,12 @@ Metrics can have many declared **properties**, which define aspects of your metr
 ### Available calculation methods
 
 <VersionBlock firstVersion="1.3">
+
 The method of calculation (aggregation or derived) that is applied to the expression.
 </VersionBlock> 
+
 <VersionBlock lastVersion="1.2">
+
 The type of calculation (aggregation or expression) that is applied to the sql property.
 </VersionBlock> 
  
@@ -537,6 +537,7 @@ The period to date secondary calculation performs an aggregation on a defined pe
 #### Rolling:
 
 <VersionBlock firstVersion="1.3" >
+
 The rolling secondary calculation performs an aggregation on a number of rows in metric dataset. For example, if the user selects the `week` grain and sets a rolling secondary calculation to `4` then the value returned will be a rolling 4 week calculation of whatever aggregation type was selected. If the `interval` input is not provided then the rolling caclulation will be unbounded on all preceding rows.
 
 | Input                      | Example | Description | Required |
@@ -548,6 +549,7 @@ The rolling secondary calculation performs an aggregation on a number of rows in
 </VersionBlock>
 
 <VersionBlock lastVersion="1.2" >
+
 The rolling secondary calculation performs an aggregation on a number of rows in the metric dataset. For example, if the user selects the `week` grain and sets a rolling secondary calculation to `4`, then the value returned will be a rolling 4-week calculation of whatever aggregation type was selected.
 
 | Input                      | Example | Description | Required |
@@ -708,7 +710,7 @@ The above example will return a dataset that contains the metric provided in the
 
 **Important caveat** - You _must_ wrap the `expression` property for `derived` metrics in double quotes to render it. For example,  `expression: "{{ metric('develop_metric') }} - 1 "`.
 
-</VersionBlock>
-
 <Snippet src="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="metrics"/>
+
+</VersionBlock>
diff --git a/website/docs/docs/dbt-cloud-apis/apis-overview.md b/website/docs/docs/dbt-cloud-apis/apis-overview.md
index 9f7c22a7580..8323e3423da 100644
--- a/website/docs/docs/dbt-cloud-apis/apis-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/apis-overview.md
@@ -8,10 +8,11 @@ id: "overview"
 
 Accounts on the _Team_ and _Enterprise_ plans can query the dbt Cloud APIs.
 
-dbt Cloud provides two APIs:
+dbt Cloud provides the following APIs:
 
 - The [dbt Cloud Administrative API](/docs/dbt-cloud-apis/admin-cloud-api) can be used to administrate a dbt Cloud account.
 - The [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api) can be used to fetch metadata related to the state and health of your dbt project.
+- The [dbt Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) provides multiple API options which allow you to query your metrics defined in the dbt Semantic Layer.
 
 If you want to learn more about webhooks, refer to [Webhooks for your jobs](/docs/deploy/webhooks).
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 8c004d865bb..b3f10309999 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -5,14 +5,44 @@ description: "Review a wide range of partners you can integrate and query with t
 sidebar_label: "Available integrations"
 ---
 
-:::info Coming soon
-The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+<VersionBlock firstVersion="1.6">
 
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
+:::info Revamped dbt Semantic Layer
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
 
-**What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
 :::
 
+Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (Discovery, JDBC, and GraphQL API). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+
+A wide variety of data applications across the modern data stack natively integrate with the dbt Semantic Layer, powered by MetricFlow &mdash; from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+
+The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
+
+- Tableau &mdash; 
+- Google Sheets &mdash;
+- Hex &mdash;
+- Mode &mdash; 
+
+For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
+
+
+## Related docs
+
+- [dbt Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) to learn how to integrate with the Discovery, JDBC, and GraphQL API to query your metrics in downstream tools.
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo />
+
 A wide variety of data applications across the modern data stack natively integrate with the dbt Semantic Layer and dbt metrics &mdash; from Business Intelligence tools to notebooks, data catalogs, and more.
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and leveraging the dbt Server. 
@@ -24,8 +54,6 @@ For information on the partner integrations, their documentation, and more &mdas
 
 ## Related docs
 
-- [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-semantic-layer) to learn more about the dbt Semantic Layer.
-- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more information on plan availability.
-- [Public Preview information](/docs/use-dbt-semantic-layer/quickstart-semantic-layer#public-preview) to understand what Public Preview for the dbt Semantic Layer means.
-- [dbt Semantic Layer partner integration guide](/guides/dbt-ecosystem/sl-partner-integration-guide) for information about partner integration guidelines, product roadmap, and API connectivity.
-- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) to understand best practices for designing and structuring metrics in your dbt project.
+- [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn more about the dbt Semantic Layer.
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
deleted file mode 100644
index 5fe781ffeb6..00000000000
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
+++ /dev/null
@@ -1,146 +0,0 @@
----
-title: "dbt Semantic Layer"
-id: dbt-semantic-layer
-description: "Introducing the dbt Semantic Layer"
-sidebar_label: "dbt Semantic Layer"
----
-
-:::info Coming soon
-The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
-
-**What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
-:::
-
-The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
-
-The result? You have less duplicate coding for data teams and more consistency for data consumers. 
-
-The dbt Semantic Layer has four main parts:
-
-- Define your metrics in version-controlled dbt project code using MetricFlow
-- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
-- Query your metric data via the dbt Proxy Server
-- Explore and analyze dbt metrics in downstream tools
-
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
-
-### What makes the dbt Semantic Layer different?
-
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
-
-
-## Prerequisites
-To use the dbt Semantic Layer, you’ll need to meet the following:
-
-<Snippet src="sl-prerequisites" />
-
-<Snippet src="sl-considerations-banner" />
-
-## Public Preview
-
-The dbt Semantic Layer is currently available for Public Preview, which means:
-
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more info on plan availability.
-
-- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
-partner tool to import metric definition.
-- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
-
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-    
-&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
-
-&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
-
-## Product architecture 
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
-    
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
-
-dbt Semantic Layer integrations will:
-
-- Leverage the Discovery API to fetch a list of objects and their attributes, like metrics
-- Generate a dbt-SQL statement
-- Then query the SQL proxy to evaluate the results of this statement
-
-
-## Manage metrics
-
-:::info 📌
-
-New to dbt or metrics? Check out our [quickstart guide](/quickstarts) to build your first dbt project! If you'd like to define your first metrics, try our [Jaffle Shop](https://github.com/dbt-labs/jaffle_shop_metrics) example project.
-
-:::
-
-If you're not sure whether to define a metric in dbt or not, ask yourself the following: 
-
-> *Is this something our teams consistently need to report on?*  
-
-An important business metric should be:
-
-- Well-defined (the definition is agreed upon throughout the entire organization)
-- Time-bound (able to be compared across time)
-
-A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
-
-- ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
-- ❌ `1-off experimental metric`
-
-
-### Design and define metrics
-
-**Design metrics**
-To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.
-
-**Define metrics**
-You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
-
-- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project
-- [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
-- [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
-- [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
-    
-Review our helpful metrics video below, which explains what metrics are, why they're important and how you can get started:
-    
-<LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
-
-## Related questions
-    
-<details>
-  <summary>How are you storing my data?</summary>
-  <div>
-    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
-  </div>
-</details>
-<details>
-  <summary>Is the dbt Semantic Layer open source?</summary>
-  <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open source. <br></br><br></br>
-      
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
-
-<ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
-    </ul></div> </div>
-</details>
-<details>
-    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
-  <div>
-    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
-    </div>
-  </div>
-</details>
-      <br></br>   
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
new file mode 100644
index 00000000000..6901c5b1726
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -0,0 +1,295 @@
+---
+title: "dbt Semantic Layer"
+id: dbt-sl
+description: "Introducing the improved dbt Semantic Layer, which allows data teams to centrally define and query metrics."
+sidebar_label: "About the dbt Semantic Layer"
+---
+
+<VersionBlock firstVersion="1.6">
+
+:::info Revamped dbt Semantic Layer
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+
+:::
+
+The dbt Semantic Layer, powered by MetricFlow, allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
+
+The result? You have less duplicate coding for data teams and more consistency for data consumers. 
+
+The dbt Semantic Layer has four main parts:
+
+- Define your metrics in version-controlled dbt project code using MetricFlow
+- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
+- Query your metric data using the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC driver, GraphQL API, and the Discovery API)
+- Explore and analyze your metrics in downstream tools
+
+ADD ARCHITECTURE IMAGE
+
+### What makes the dbt Semantic Layer different?
+
+The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
+
+
+## Prerequisites
+
+To use the dbt Semantic Layer, you’ll need to meet the following:
+
+import SetUp from '/snippets/_v2-sl-prerequisites.md';
+
+<SetUp />
+
+<Snippet src="sl-considerations-banner" />
+
+## Public Preview
+
+The dbt Semantic Layer is currently available for Public Preview, which means:
+
+&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](#product-architecture) for more info on plan availability.
+
+- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
+partner tool to import metric definition.
+- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
+
+&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backward compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+    
+&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+
+&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
+
+## Product architecture 
+
+The dbt Semantic Layer product architecture includes four primary components:
+
+| Components | Information | Developer plans | Team plans | Enterprise plans | License |
+| --- | --- | :---: | :---: | :---: | --- |
+| **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
+| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
+| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)**  | Use the Semantic Layer APIS, which includes the Discovery, JDBC, and GraphQL API, to access metric definitions using integrations. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
+
+ADD COMPONENT IMAGE
+
+dbt Semantic Layer integrations will:
+
+- Leverage the Semantic Layer APIs to fetch a list of objects and their attributes, like metrics
+- Generate a dbt-SQL statement
+- Then query the SQL proxy to evaluate the results of this statement
+
+
+## Manage metrics
+
+:::tip 
+New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
+:::
+
+If you're not sure whether to define a metric in dbt or not, ask yourself the following: 
+
+> *Is this something our teams consistently need to report on?*  
+
+An important business metric should be:
+
+- Well-defined (the definition is agreed upon throughout the entire organization)
+- Time-bound (able to be compared across time)
+
+A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
+
+- ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
+- ❌ `1-off experimental metric`
+
+
+### Build metrics
+
+Use [MetricFlow](/docs/build/build-metrics-intro) in dbt to centrally build and define your metrics. MetricFlow is a key component of the dbt Semantic Layer, handles SQL query construction, and defines the specification for dbt semantic models and metrics.
+
+With MetricFlow, you can use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+
+You can define your metrics in `.yml` files nested under a semantic model key to building your own metrics in your dbt project.
+
+To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a dbt Cloud Team or Enterprise account.
+
+To begin building and defining metrics, refer to the detailed [MetricFlow](/docs/build/build-metrics-intro) docs for more info. 
+
+**Design metrics**
+To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.  
+
+## Related questions
+
+<details>
+  <summary>How do I migrate from the old Semantic Layer to the new one?</summary>
+  <div>
+    <div>ADD INFO HERE AND MIGRATION SCRIPT.</div>
+  </div>
+</details>
+    
+<details>
+  <summary>How are you storing my data?</summary>
+  <div>
+    <div>The dbt Semantic Layer does not store, cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
+  </div>
+</details>
+<details>
+  <summary>Is the dbt Semantic Layer open source?</summary>
+  <div>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
+      
+During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+
+<ul>
+<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
+<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
+<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
+    </ul></div> </div>
+</details>
+<details>
+    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
+  <div>
+    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
+    </div>
+  </div>
+</details>
+      <br></br>
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo />
+
+The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
+
+The result? You have less duplicate coding for data teams and more consistency for data consumers. 
+
+The dbt Semantic Layer has four main parts:
+
+- Define your metrics in version-controlled dbt project code using MetricFlow 
+	* dbt_metrics is now deprecated
+- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
+- Query your metric data via the dbt Proxy Server
+- Explore and analyze dbt metrics in downstream tools
+
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
+
+### What makes the dbt Semantic Layer different?
+
+The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
+
+
+## Prerequisites
+To use the dbt Semantic Layer, you’ll need to meet the following:
+
+<Snippet src="sl-prerequisites" />
+
+<Snippet src="sl-considerations-banner" />
+
+## Public Preview
+
+The dbt Semantic Layer is currently available for Public Preview, which means:
+
+&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more info on plan availability.
+
+- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
+partner tool to import metric definition.
+- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
+
+&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+    
+&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+
+&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
+
+## Product architecture 
+
+The dbt Semantic Layer product architecture includes four primary components:
+
+| Components | Information | Developer plans | Team plans | Enterprise plans | License |
+| --- | --- | :---: | :---: | :---: | --- |
+| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
+| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
+| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
+    
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
+
+dbt Semantic Layer integrations will:
+
+- Leverage the Discovery API to fetch a list of objects and their attributes, like metrics
+- Generate a dbt-SQL statement
+- Then query the SQL proxy to evaluate the results of this statement
+
+
+## Manage metrics
+
+:::info 📌
+
+New to dbt or metrics? Check out our [quickstart guide](/quickstarts) to build your first dbt project! If you'd like to define your first metrics, try our [Jaffle Shop](https://github.com/dbt-labs/jaffle_shop_metrics) example project.
+
+:::
+
+If you're not sure whether to define a metric in dbt or not, ask yourself the following: 
+
+> *Is this something our teams consistently need to report on?*  
+
+An important business metric should be:
+
+- Well-defined (the definition is agreed upon throughout the entire organization)
+- Time-bound (able to be compared across time)
+
+A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
+
+- ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
+- ❌ `1-off experimental metric`
+
+
+### Design and define metrics
+
+**Design metrics**
+To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.
+
+**Define metrics**
+You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
+
+- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project
+- [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics, and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
+- [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
+- [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
+    
+Review our helpful metrics video below, which explains what metrics are, why they're important, and how you can get started:
+    
+<LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
+
+## Related questions
+    
+<details>
+  <summary>How are you storing my data?</summary>
+  <div>
+    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
+  </div>
+</details>
+<details>
+  <summary>Is the dbt Semantic Layer open source?</summary>
+  <div>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
+      
+During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+
+<ul>
+<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
+<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
+<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
+    </ul></div> </div>
+</details>
+<details>
+    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
+  <div>
+    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
+    </div>
+  </div>
+</details>
+      <br></br>
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/graphql.md b/website/docs/docs/use-dbt-semantic-layer/graphql.md
new file mode 100644
index 00000000000..726039dcf68
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/graphql.md
@@ -0,0 +1,24 @@
+---
+title: "GraphQL"
+id: graphql
+description: "Integrate and use the GraphQL API to query your metrics."
+tags: ["semantic-layer, apis"]
+---
+
+<VersionBlock lastVersion="1.5">
+
+:::info
+
+The dbt Semantic Layer APIs are available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about the Semantic APIs, make sure you select v1.6 or higher in the docs navigation header and upgrade your dbt version. 
+
+:::
+
+ADD GRAPHQL CONTENT HERE
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.6">
+
+ADD GRAPHQL CONTENT HERE
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
new file mode 100644
index 00000000000..d22f500765d
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -0,0 +1,22 @@
+---
+title: "JDBC"
+id: jdbc
+description: "Integrate and use the JDBC API to query your metrics."
+tags: ["semantic-layer, apis"]
+---
+
+<VersionBlock lastVersion="1.5">
+
+:::info
+The dbt Semantic Layer APIs are available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about the Semantic APIs, make sure you select v1.6 or higher in the docs navigation header and upgrade your dbt version. 
+:::
+
+ADD JDBC CONTENT HERE
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.6">
+
+ADD JDBC CONTENT HERE
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
similarity index 57%
rename from website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
rename to website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 19a5fb15057..e844132c587 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -1,36 +1,190 @@
 ---
-title: "Quickstart"
-id: quickstart-semantic-layer
-description: "Define metrics and set up the dbt Semantic Layer"
-sidebar_label: "Quickstart"
+title: "Get started with the dbt Semantic Layer"
+id: quickstart-sl
+description: "Go through this get started guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
+sidebar_label: "Getting started"
 ---
 
-:::info Coming soon
-The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+<VersionBlock firstVersion="1.6">
 
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
+This getting started page recommends a workflow to help you get started creating your first metrics. Here are the following steps you'll take:
 
-**What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
+- [Create a semantic model](#create-a-semantic-model)
+- [Create your metrics](#create-your-metrics)
+- [Run your production job](#run-your-production-job)
+- [Set up dbt Semantic Layer](#setup)
+- [Connect to the Semantic Layer APIs](#connect-to-apis)
+- [Query your metrics using partner integrations](#query-metrics)
+
+## Prerequisites
+
+import SetUp from '/snippets/_v2-sl-prerequisites.md';
+
+<SetUp />
+
+<Snippet src="sl-considerations-banner" />
+
+:::tip 
+New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
+:::
+
+## Create a semantic model
+
+In MetricFlow, which powers the dbt Semantic Layer, there are two main objects: 
+
+- [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
+- [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
+
+This step will guide you through setting up your semantic models, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
+
+1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
+
+```yaml
+semantic_models:
+  - name: transactions
+    description: |
+    This table captures every transaction starting July 02, 2014. Each row represents one transaction
+    model: ref('fact_transactions')
+  ```
+
+2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `transaction_id`, and so on.
+
+```yaml
+  entities:
+    - name: transaction
+      type: primary
+      expr: id_transaction
+    - name: customer
+      type: foreign
+      expr: id_customer
+  ```
+
+3. Define your dimensions and measures. dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
+
+```yaml
+measures:
+    - name: transaction_amount_usd
+      description: The total USD value of the transaction.
+      agg: sum
+  dimensions:
+    - name: is_large
+      type: categorical
+      expr: case when transaction_amount_usd >= 30 then true else false end
+```
+
+:::tip
+
+If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
+```sql
+select
+  metric_time_day,  -- time
+  country,  -- categorical dimension
+  sum(revenue_usd) -- measure
+from
+  snowflake.fact_transactions  -- sql table
+group by metric_time_day, country  -- dimensions
+  ```
 :::
 
-## Public Preview 
+## Create your metrics
+
+Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). You can define metrics in the same YAML files as your semantic models, or create a new file.
+
+The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
+
+```yaml
+---
+metrics:
+  - name: transaction_amount_usd
+    type: simple
+    type_params:
+    measure: transaction_amount_usd
+```
+ 
+1. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
+* Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
+2. Run `mf validate-configs` to validate the changes before committing them.
+3. Commit and merge the code changes that contain the metric definitions.
+
+Review the [MetricFlow](/docs/build/build-metrics-intro) documents to further build your own metrics. 
+
+## Run your production job
+
+Once you’ve defined metrics in your dbt project, you can perform a job run in your deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
+
+1. Go to **Deploy** in the navigation header
+2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.
+3. Your metric should appear as a red node in the dbt Cloud IDE and dbt directed acyclic graphs (DAG).
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/metrics_red_nodes.png" title="DAG with metrics appearing as a red node" />
+
+
+**What’s happening internally?**
+
+- Merging the code into your main branch allows dbt Cloud to pull those changes and builds the definition in the manifest produced by the run.
+- Re-running the job in the deployment environment helps materialize the models, which the metrics depend on, in the data platform. It also makes sure that the manifest is up to date.
+- Your dbt Discovery API pulls in the most recent manifest and allows your integration information to extract metadata from it.
+
+## Set up dbt Semantic Layer
     
-We're excited to announce the dbt Semantic Layer is currently available for Public Preview, which means:
+<Snippet src="sl-set-up-steps" />
+
+## Connect to the Semantic Layer APIs
+
+add content here
+
+## Query your metrics using partner integrations
+
+add content
+
+:::info 📌 
+
+**Note:** You will need access to dbt Cloud and the dbt Semantic Layer from your integrated partner tool of choice. 
 
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more info on plan availability.
+:::
 
-- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
-partner tool to import metric definition.
-- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which                  requires access to the Discovery API. 
+## FAQs
 
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
     
-&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+<details>
+  <summary>How are you storing my data?</summary>
+  <div>
+    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
+  </div>
+</details>
+<details>
+    <summary>Is the dbt Semantic Layer open source?</summary>
+  <div>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
 
-&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
+During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+<ul>    
+<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, you will need to have a dbt Cloud account.</li><br></br><br></br>
+<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br><br></br>
+<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definitions.</li>
+    </ul>
+    </div>
+    </div>
+</details>
 
+   
+## Next steps
 
-## Introduction
+Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer: 
+
+- [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
+- [About MetricFlow](/docs/build/about-metricflow)
+- [Semantic models](/docs/build/semantic-models)
+- [Metrics](/docs/build/metrics-overview)
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo />
 
 To try out the features of the dbt Semantic Layer, you first need to have a dbt project set up. This quickstart guide will lay out the following steps, and recommends a workflow that demonstrates some of its essential features:
 
@@ -56,7 +210,6 @@ New to dbt or metrics? Check out our [quickstart guide](/quickstarts) to build
 ## Installing dbt metrics package
 The dbt Semantic Layer supports the calculation of metrics by using the [dbt metrics package](https://hub.getdbt.com/dbt-labs/metrics/latest/). You can install the dbt metrics package in your dbt project by copying the below code blocks.
 
-
 <VersionBlock firstVersion="1.3" lastVersion="1.3">
 
 ```yml
@@ -176,7 +329,7 @@ metrics:
 2. Commit and merge the code changes that contain the metric definitions.
 3. If you'd like to further design and define your own metrics, review the following documentation:
 
-    - [dbt metrics](/docs/build/metrics) will povide you in-depth detail on attributes, properties, filters, and how to define and query metrics.
+    - [dbt metrics](/docs/build/metrics) will provide you in-depth detail on attributes, properties, filters, and how to define and query metrics.
    
     - Review [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project.
 
@@ -226,7 +379,7 @@ If you're encountering some issues when defining your metrics or setting up the
 <details>
     <summary>Is the dbt Semantic Layer open source?</summary>
   <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open source. <br></br><br></br>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
 
 During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
 <ul>    
@@ -298,4 +451,6 @@ Are you ready to define your own metrics and bring consistency to data consumers
 - [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) to understand best practices for designing and structuring metrics in your dbt project
 - [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, properties, filters, and how to define and query metrics
 - [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
-- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations.
+- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. 
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
deleted file mode 100644
index b045725ca62..00000000000
--- a/website/docs/docs/use-dbt-semantic-layer/set-dbt-semantic-layer.md
+++ /dev/null
@@ -1,40 +0,0 @@
----
-title: "Set up the dbt Semantic Layer"
-id: setup-dbt-semantic-layer
-description: "You can set up the dbt Semantic Layer in dbt Cloud."
-sidebar_label: "Set up the dbt Semantic Layer"
----
-
-:::info Coming soon
-The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
-
-**What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
-:::
-
-With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
-
-## Prerequisites
-
-Before you set up the dbt Semantic Layer, make sure you meet the following:
-
-<Snippet src="sl-prerequisites" />
-
-
-<Snippet src="sl-considerations-banner" />
-
-
-## Set up dbt Semantic Layer
-
-<Snippet src="sl-set-up-steps" />
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
-
- 
-## Related docs
-
-- [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
-- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) page for more information on plan availability
-- [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, properties, filters, and how to define and query metrics
-- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
new file mode 100644
index 00000000000..40acade5138
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -0,0 +1,107 @@
+---
+title: "Set up the dbt Semantic Layer"
+id: setup-sl
+description: "You can set up the dbt Semantic Layer in dbt Cloud."
+sidebar_label: "Set up the dbt Semantic Layer"
+---
+
+<VersionBlock firstVersion="1.6">
+
+:::info Upgrade your dbt version to access the dbt Semantic Layer
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+
+:::
+
+With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
+
+## Prerequisites
+
+Before you set up the dbt Semantic Layer, make sure you meet the following:
+
+import SetUp from '/snippets/_v2-sl-prerequisites.md';
+
+<SetUp />
+
+
+<Snippet src="sl-considerations-banner" />
+
+
+## Set up dbt Semantic Layer
+
+Before you continue with the following steps, you **must** have a multi-tenant dbt Cloud account hosted in North America. 
+ * Team and Enterprise accounts can set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated partner tool to import metric definitions. 
+ * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
+
+You can set up the dbt Semantic Layer in dbt Cloud at the environment level by following these steps:
+
+1. Login to your dbt Cloud account
+2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 
+3. Assign permissions to service account tokens depending on the integration tool you choose. You can review the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
+4. Go to **Deploy** and then **Environment**, and select your **Deployment** environment.
+5. Click on **Settings** on the top right side of the page.
+6. Click **Edit** on the top right side of the page.
+7. Select dbt version 1.2 or higher.
+8. Toggle the Semantic Layer **On**.
+9. Copy the full proxy server URL (like `https://eagle-hqya7.proxy.cloud.getdbt.com`) to connect to your [integrated partner tool](https://www.getdbt.com/product/semantic-layer-integrations). 
+10. Use the URL in the data source configuration of the integrated partner tool.
+11. Use the data platform login credentials that make sense for how the data is consumed.
+
+:::info📌 
+
+Note  - It is _not_ recommended that you use your dbt Cloud credentials due to elevated permissions. Instead, you can use your specific integration tool permissions.
+
+:::
+
+12. Set up the [Discovery API](/docs/dbt-cloud-apis/discovery-api) (Team and Enterprise accounts only) in the integrated partner tool to import the metric definitions. The [integrated partner tool](https://www.getdbt.com/product/semantic-layer-integrations) will treat the dbt Server as another data source (like a data platform). This requires:
+
+- The account ID, environment ID, and job ID (visible in the job URL)
+- An [API service token](/docs/dbt-cloud-apis/service-tokens) with job admin and metadata permissions
+- Add the items above to the relevant fields in your integration tool
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
+
+ 
+## Related docs
+
+- [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
+- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) page for more information on plan availability
+- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo />
+
+With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
+
+## Prerequisites
+
+Before you set up the dbt Semantic Layer, make sure you meet the following:
+
+<Snippet src="sl-prerequisites" />
+
+
+<Snippet src="sl-considerations-banner" />
+
+
+## Set up dbt Semantic Layer
+
+<Snippet src="sl-set-up-steps" />
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
+
+ 
+## Related docs
+
+- [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
+- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) page for more information on plan availability
+- [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, properties, filters, and how to define and query metrics
+- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
new file mode 100644
index 00000000000..39c9e1c571a
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
@@ -0,0 +1,101 @@
+---
+title: "Semantic Layer APIs"
+id: sl-api-overview
+description: "Integrate and query using the Semantic Layer APIs."
+tags: ["semantic-layer, apis"]
+hide_table_of_contents: true
+---
+
+<VersionBlock lastVersion="1.5">
+
+:::info
+
+The revamped dbt Semantic Layer is available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
+
+To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+
+:::
+
+Query dbt metrics via external integrations using the dbt Semantic Layer APIs. With the Semantic Layer APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
+
+By leveraging metrics in dbt Cloud, you can create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and dbt metrics — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+
+The dbt Semantic Layer generates a [`semantic_manifest.json`](/docs/use-dbt-semantic-layer/sl-manifest) artifact file. This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+
+The dbt Semantic Layer provides the following APIs:
+
+<div className="grid--3-col">
+
+<Card
+    title="JDBC API"
+    body="Use a JDBC driver to query metrics in downstream tools."
+    link="/docs/use-dbt-semantic-layer/jdbc"
+    icon="dbt-bit"/>
+
+<Card
+    title="GraphQL API"
+    body="Use GraphQL to query metrics in downstream tools."
+    link="/docs/use-dbt-semantic-layer/graphql"
+    icon="dbt-bit"/>
+
+<Card
+    title="Discovery API"
+    body="Uses the Discovery API to query metrics in downstream tools using metadata details about your project’s models, sources, and other nodes along with their execution results."
+    link="/docs/dbt-cloud-apis/discovery-api"
+    icon="dbt-bit"/>
+
+</div>
+
+## Authentication
+
+Requests to the dbt Semantic Layer APIs can be authorized through two types of API tokens: 
+
+- [User tokens](/docs/dbt-cloud-apis/user-tokens)  
+- [Service account tokens](/docs/dbt-cloud-apis/service-tokens)
+
+
+</VersionBlock>
+
+
+<VersionBlock firstVersion="1.6">
+
+Query dbt metrics via external integrations using the dbt Semantic Layer APIs. With the Semantic Layer APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
+
+By leveraging metrics in dbt Cloud, you can create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and dbt metrics — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+
+The dbt Semantic Layer generates a [`semantic_manifest.json`](/docs/use-dbt-semantic-layer/sl-manifest) artifact file. This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+
+To query metrics using the dbt Semantic Lay API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
+
+The dbt Semantic Layer provides the following APIs:
+
+<div className="grid--3-col">
+
+<Card
+    title="JDBC API"
+    body="Use a JDBC driver to query metrics in downstream tools."
+    link="/docs/use-dbt-semantic-layer/jdbc"
+    icon="dbt-bit"/>
+
+<Card
+    title="GraphQL API"
+    body="Use GraphQL to query metrics in downstream tools."
+    link="/docs/use-dbt-semantic-layer/graphql"
+    icon="dbt-bit"/>
+
+<Card
+    title="Discovery API"
+    body="Uses the Discovery API to query metrics in downstream tools using metadata details about your project’s models, sources, and other nodes along with their execution results."
+    link="/docs/dbt-cloud-apis/discovery-api"
+    icon="dbt-bit"/>
+
+</div>
+
+## Authentication
+
+Requests to the dbt Semantic Layer APIs can be authorized through two types of API tokens: 
+
+- [User tokens](/docs/dbt-cloud-apis/user-tokens)  
+- [Service account tokens](/docs/dbt-cloud-apis/service-tokens)
+
+</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-manifest.md b/website/docs/docs/use-dbt-semantic-layer/sl-manifest.md
new file mode 100644
index 00000000000..241a137ab42
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-manifest.md
@@ -0,0 +1,878 @@
+---
+title: "Semantic manifest"
+id: sl-manifest
+description: "Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."
+sidebar_label: "Semantic manifest"
+---
+
+The dbt Semantic Layer creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+
+Similar to the `manifest.json` file, the Semantic Manifest also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+
+The Semantic Manifest allows you to gain insights into the entities and relationships defined in your Semantic Layer, making it easier to understand your data models and their dependencies.
+
+QUESTIONS:
+- WHAT IS IT PRODUCED BY?
+- WHAT ARE THE TOP LEVEL KEYS?
+- HOW DOES IT RELATED TO THE [JSON SCHEMA FILE](https://schemas.getdbt.com/)?
+
+<details>
+<summary><code>target/semantic_manifest.json</code> file </summary>
+
+```json
+{
+    "semantic_models": [
+        {
+            "name": "orders",
+            "defaults": {
+                "agg_time_dimension": "ds"
+            },
+            "description": "Model containing order data. The grain of the table is the order id.\n",
+            "node_relation": {
+                "alias": "orders",
+                "schema_name": "dbt_sl_test",
+                "database": "ANALYTICS",
+                "relation_name": "ANALYTICS.dbt_sl_test.orders"
+            },
+            "entities": [
+                {
+                    "name": "order",
+                    "description": null,
+                    "type": "primary",
+                    "role": null,
+                    "expr": "order_id",
+                    "metadata": null
+                },
+                {
+                    "name": "location",
+                    "description": null,
+                    "type": "foreign",
+                    "role": null,
+                    "expr": "location_id",
+                    "metadata": null
+                },
+                {
+                    "name": "customer",
+                    "description": null,
+                    "type": "foreign",
+                    "role": null,
+                    "expr": "customer_id",
+                    "metadata": null
+                }
+            ],
+            "measures": [
+                {
+                    "name": "order_total",
+                    "agg": "sum",
+                    "description": null,
+                    "create_metric": true,
+                    "expr": null,
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "food_orders",
+                    "agg": "sum",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "case when is_food_order then order_total else 0 end",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "orders",
+                    "agg": "sum",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "1",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "max_order_value",
+                    "agg": "max",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "order_total",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "min_order_value",
+                    "agg": "min",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "order_total",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "customers",
+                    "agg": "count_distinct",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "customer_id",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "average_order_value",
+                    "agg": "average",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": "order_total",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "order_cost",
+                    "agg": "sum",
+                    "description": null,
+                    "create_metric": false,
+                    "expr": null,
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "locations_with_orders",
+                    "agg": "count_distinct",
+                    "description": null,
+                    "create_metric": true,
+                    "expr": "location_id",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "median_order_value",
+                    "agg": "median",
+                    "description": null,
+                    "create_metric": true,
+                    "expr": "order_total",
+                    "agg_params": null,
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "order_value_p99",
+                    "agg": "percentile",
+                    "description": null,
+                    "create_metric": true,
+                    "expr": "order_total",
+                    "agg_params": {
+                        "percentile": 0.99,
+                        "use_discrete_percentile": true,
+                        "use_approximate_percentile": false
+                    },
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                },
+                {
+                    "name": "discrete_order_value_p99",
+                    "agg": "percentile",
+                    "description": null,
+                    "create_metric": true,
+                    "expr": "order_total",
+                    "agg_params": {
+                        "percentile": 0.99,
+                        "use_discrete_percentile": true,
+                        "use_approximate_percentile": false
+                    },
+                    "metadata": null,
+                    "non_additive_dimension": null,
+                    "agg_time_dimension": "ds"
+                }
+            ],
+            "dimensions": [
+                {
+                    "name": "location_name",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "ds",
+                    "description": null,
+                    "type": "time",
+                    "is_partition": false,
+                    "type_params": {
+                        "time_granularity": "day",
+                        "validity_params": null
+                    },
+                    "expr": "ordered_at",
+                    "metadata": null
+                },
+                {
+                    "name": "is_food_order",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "is_drink_order",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "order_total_dim",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": "order_total",
+                    "metadata": null
+                }
+            ],
+            "metadata": null
+        },
+        {
+            "name": "customers",
+            "defaults": {
+                "agg_time_dimension": "first_ordered_at"
+            },
+            "description": "Customer dimension table. The grain of the table is one row per customer.\n",
+            "node_relation": {
+                "alias": "customers",
+                "schema_name": "dbt_sl_test",
+                "database": "ANALYTICS",
+                "relation_name": "ANALYTICS.dbt_sl_test.customers"
+            },
+            "entities": [
+                {
+                    "name": "customer",
+                    "description": null,
+                    "type": "primary",
+                    "role": null,
+                    "expr": "customer_id",
+                    "metadata": null
+                }
+            ],
+            "measures": [],
+            "dimensions": [
+                {
+                    "name": "customer_name",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "count_lifetime_orders",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "lifetime_spend_pre_tax",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "lifetime_spend",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "customer_type",
+                    "description": null,
+                    "type": "categorical",
+                    "is_partition": false,
+                    "type_params": null,
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "first_ordered_at",
+                    "description": null,
+                    "type": "time",
+                    "is_partition": false,
+                    "type_params": {
+                        "time_granularity": "day",
+                        "validity_params": null
+                    },
+                    "expr": null,
+                    "metadata": null
+                },
+                {
+                    "name": "last_ordered_at",
+                    "description": null,
+                    "type": "time",
+                    "is_partition": false,
+                    "type_params": {
+                        "time_granularity": "day",
+                        "validity_params": null
+                    },
+                    "expr": null,
+                    "metadata": null
+                }
+            ],
+            "metadata": null
+        }
+    ],
+    "metrics": [
+        {
+            "name": "east_coast_order_amount",
+            "description": "Sum of orders from the east coast.",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "orders",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": {
+                "where_sql_template": "dimension('location_name') in ('Philadelphia')\""
+            },
+            "metadata": null
+        },
+        {
+            "name": "large_order",
+            "description": "Sum of orders from the east coast.",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "orders",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": {
+                "where_sql_template": "dimension('order_total_dim')\" >= 20"
+            },
+            "metadata": null
+        },
+        {
+            "name": "food_order_amount",
+            "description": "Sum order amount for food orders.",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "food_orders",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "food_order_pct",
+            "description": "The food cost as the % of the total order",
+            "type": "ratio",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": {
+                    "name": "food_orders",
+                    "filter": null,
+                    "alias": null
+                },
+                "denominator": {
+                    "name": "order_total",
+                    "filter": null,
+                    "alias": null
+                },
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "new_customer_order_pct",
+            "description": "The food cost as the % of the total order",
+            "type": "ratio",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": {
+                    "name": "customers",
+                    "filter": {
+                        "where_sql_template": "dimension('customer_type', entity_path=['customer'])\" = 'new'"
+                    },
+                    "alias": "customer_numerator"
+                },
+                "denominator": {
+                    "name": "customers",
+                    "filter": null,
+                    "alias": null
+                },
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "order_gross_profit",
+            "description": "Gross profit from each order.",
+            "type": "derived",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": null,
+                "denominator": null,
+                "expr": "revenue - cost",
+                "window": null,
+                "grain_to_date": null,
+                "metrics": [
+                    {
+                        "name": "food_order_amount",
+                        "filter": null,
+                        "alias": "revenue",
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    },
+                    {
+                        "name": "order_amount",
+                        "filter": null,
+                        "alias": "cost",
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    }
+                ]
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "food_order_pct_cumulative",
+            "description": "Example to show a ratio referenced in a derived ",
+            "type": "derived",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": null,
+                "denominator": null,
+                "expr": "food_order_pct * 100",
+                "window": null,
+                "grain_to_date": null,
+                "metrics": [
+                    {
+                        "name": "food_order_pct",
+                        "filter": null,
+                        "alias": null,
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    }
+                ]
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "cumulative_growth_mom",
+            "description": "Example to show a ratio referenced in a derived ",
+            "type": "derived",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": null,
+                "denominator": null,
+                "expr": "cumulative_order_amount/cumulative_order_amount_lm",
+                "window": null,
+                "grain_to_date": null,
+                "metrics": [
+                    {
+                        "name": "cumulative_order_amount",
+                        "filter": null,
+                        "alias": null,
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    },
+                    {
+                        "name": "cumulative_order_amount",
+                        "filter": null,
+                        "alias": "cumulative_order_amount_lm",
+                        "offset_window": {
+                            "count": 1,
+                            "granularity": "month"
+                        },
+                        "offset_to_grain": null
+                    }
+                ]
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "food_order_gross_profit",
+            "description": "The gross profit for each order",
+            "type": "derived",
+            "type_params": {
+                "measure": null,
+                "measures": [],
+                "numerator": null,
+                "denominator": null,
+                "expr": "revenue - cost",
+                "window": null,
+                "grain_to_date": null,
+                "metrics": [
+                    {
+                        "name": "order_total",
+                        "filter": {
+                            "where_sql_template": "dimension('is_food_order')\" = True"
+                        },
+                        "alias": "revenue",
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    },
+                    {
+                        "name": "order_amount",
+                        "filter": {
+                            "where_sql_template": "dimension('is_food_order')\" = True"
+                        },
+                        "alias": "cost",
+                        "offset_window": null,
+                        "offset_to_grain": null
+                    }
+                ]
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "cumulative_order_amount",
+            "description": "The cumulative value of all orders",
+            "type": "cumulative",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "order_total",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "cumulative_order_amount_l1m",
+            "description": "The cumulative value of all orders",
+            "type": "cumulative",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "order_total",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": {
+                    "count": 1,
+                    "granularity": "month"
+                },
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "cumulative_order_amount_mtd",
+            "description": "The cumulative value of all orders",
+            "type": "cumulative",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "order_total",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": "month",
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "order_count",
+            "description": "The number of orders placed",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "orders",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "max_order_amount",
+            "description": "The highest order value for a given period",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "max_order_value",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "min_order_amount",
+            "description": "The lowest order value for a given period",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "min_order_value",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "customers_with_orders",
+            "description": "Unique count of customers placing orders",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "customers",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "returning_customers_with_orders",
+            "description": "Unique count of customers placing orders",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "customers",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": {
+                "where_sql_template": "dimension('customer_type', entity_path=['customer']) \" = 'returning'"
+            },
+            "metadata": null
+        },
+        {
+            "name": "new_customer_with_orders",
+            "description": "Unique count of customers placing orders",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "customers",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": {
+                "where_sql_template": "dimension('customer_type', entity_path=['customer']) \" = 'new'"
+            },
+            "metadata": null
+        },
+        {
+            "name": "average_order_amount",
+            "description": "The average order value",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "average_order_value",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        },
+        {
+            "name": "order_amount",
+            "description": "The cost of fulfilling each order",
+            "type": "simple",
+            "type_params": {
+                "measure": null,
+                "measures": [
+                    {
+                        "name": "order_cost",
+                        "filter": null,
+                        "alias": null
+                    }
+                ],
+                "numerator": null,
+                "denominator": null,
+                "expr": null,
+                "window": null,
+                "grain_to_date": null,
+                "metrics": []
+            },
+            "filter": null,
+            "metadata": null
+        }
+    ],
+    "interfaces_version": "0.1.0.dev3"
+}
+```
+</details>
+
+## Related docs
+
+- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [About dbt artifacts](/reference/artifacts/dbt-artifacts)
+
diff --git a/website/docs/reference/artifacts/dbt-artifacts.md b/website/docs/reference/artifacts/dbt-artifacts.md
index 2fbcc329484..84a7a40c986 100644
--- a/website/docs/reference/artifacts/dbt-artifacts.md
+++ b/website/docs/reference/artifacts/dbt-artifacts.md
@@ -3,12 +3,15 @@ title: "About dbt artifacts"
 sidebar_label: "About dbt artifacts"
 ---
 
-With every invocation, dbt generates and saves one or more *artifacts*. Several of these are <Term id="json" /> files (`manifest.json`, `catalog.json`, `run_results.json`, and `sources.json`) that are used to power:
+With every invocation, dbt generates and saves one or more *artifacts*. Several of these are <Term id="json" /> files (`semantic_manifest.json`, `manifest.json`, `catalog.json`, `run_results.json`, and `sources.json`) that are used to power:
+
 - [documentation](/docs/collaborate/documentation)
 - [state](/reference/node-selection/syntax#about-node-selection)
 - [visualizing source freshness](/docs/build/sources#snapshotting-source-data-freshness)
 
 They could also be used to:
+
+- gain insights into your [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl)
 - calculate project-level test coverage
 - perform longitudinal analysis of run timing
 - identify historical changes in <Term id="table" /> structure
@@ -19,6 +22,7 @@ dbt has produced artifacts since the release of dbt-docs in v0.11.0. Starting in
 ## When are artifacts produced?
 
 Most dbt commands (and corresponding RPC methods) produce artifacts:
+- [semantic manifest](/docs/use-dbt-semantic-layer/sl-manifest): Lives in the `/target` directory of your dbt project and stores various artifacts (such as compiled models and tests) generated during the execution of your project.
 - [manifest](/reference/artifacts/manifest-json): produced by commands that read and understand your project
 - [run results](/reference/artifacts/run-results-json): produced by commands that run, compile, or catalog nodes in your DAG
 - [catalog](catalog-json): produced by `docs generate`
diff --git a/website/package-lock.json b/website/package-lock.json
index b15a903e97f..65c5f6ba759 100644
--- a/website/package-lock.json
+++ b/website/package-lock.json
@@ -16,6 +16,7 @@
         "@stoplight/elements": "^7.7.17",
         "@svgr/webpack": "^6.0.0",
         "axios": "^0.27.2",
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+        "safe-stable-stringify": {
+          "version": "2.4.3",
+          "resolved": "https://registry.npmjs.org/safe-stable-stringify/-/safe-stable-stringify-2.4.3.tgz",
+          "integrity": "sha512-e2bDA2WJT0wxseVd4lsDP4+3ONX6HpMXQa1ZhFQ7SU+GjvORCmShbCMltrtIDfkYhVHrOcPtj+KhmDBdPdZD1g=="
+        }
+      }
+    },
+    "winston-transport": {
+      "version": "4.5.0",
+      "resolved": "https://registry.npmjs.org/winston-transport/-/winston-transport-4.5.0.tgz",
+      "integrity": "sha512-YpZzcUzBedhlTAfJg6vJDlyEai/IFMIVcaEZZyl3UXIl4gmqRpU7AE89AHLkbzLUsv0NVmw7ts+iztqKxxPW1Q==",
+      "requires": {
+        "logform": "^2.3.2",
+        "readable-stream": "^3.6.0",
+        "triple-beam": "^1.3.0"
+      }
+    },
     "wolfy87-eventemitter": {
       "version": "5.2.9",
       "resolved": "https://registry.npmjs.org/wolfy87-eventemitter/-/wolfy87-eventemitter-5.2.9.tgz",
@@ -43817,6 +44230,11 @@
       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
       "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
     },
+    "wrench": {
+      "version": "1.3.9",
+      "resolved": "https://registry.npmjs.org/wrench/-/wrench-1.3.9.tgz",
+      "integrity": "sha512-srTJQmLTP5YtW+F5zDuqjMEZqLLr/eJOZfDI5ibfPfRMeDh3oBUefAscuH0q5wBKE339ptH/S/0D18ZkfOfmKQ=="
+    },
     "write-file-atomic": {
       "version": "3.0.3",
       "resolved": "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-3.0.3.tgz",
diff --git a/website/package.json b/website/package.json
index afb7a9b1cd4..080ede9e3e6 100644
--- a/website/package.json
+++ b/website/package.json
@@ -19,6 +19,7 @@
     "@stoplight/elements": "^7.7.17",
     "@svgr/webpack": "^6.0.0",
     "axios": "^0.27.2",
+    "build": "^0.1.4",
     "classnames": "^2.3.1",
     "clsx": "^1.1.1",
     "color": "^3.1.2",
diff --git a/website/sidebars.js b/website/sidebars.js
index 46db14dd6d4..2c68ecf443a 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -404,21 +404,22 @@ const sidebarSettings = {
     {
       type: "category",
       label: "Use the dbt Semantic Layer",
-			link: { type: "doc", id: "docs/use-dbt-semantic-layer/dbt-sl" },
+      link: { type: "doc", id: "docs/use-dbt-semantic-layer/dbt-sl" },
       items: [
-				"docs/use-dbt-semantic-layer/quickstart-sl",
-        "docs/use-dbt-semantic-layer/setup-dbt-sl",
+        "docs/use-dbt-semantic-layer/quickstart-sl",
+        "docs/use-dbt-semantic-layer/setup-sl",
         "docs/use-dbt-semantic-layer/avail-sl-integrations",
         {
           type: "category",
           label: "Semantic Layer APIs",
-					link: { type: "doc", id: "docs/use-dbt-semantic-layer/sl-api-overview" },
+          link: { type: "doc", id: "docs/use-dbt-semantic-layer/sl-api-overview" },
           items: [
             "docs/use-dbt-semantic-layer/jdbc",
             "docs/use-dbt-semantic-layer/graphql",
+            "docs/use-dbt-semantic-layer/sl-manifest",
           ],
         },
-		 ],
+      ],
     },
     {
       type: "category",
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
new file mode 100644
index 00000000000..8ed4e162f75
--- /dev/null
+++ b/website/snippets/_legacy-sl-callout.md
@@ -0,0 +1,13 @@
+:::info Upgrade your dbt version to access the dbt Semantic Layer
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+
+**Who does this affect?** The revamped dbt Semantic Layer is available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+
+**What should you do?** We **highly** recommend you upgrade to dbt v1.6 and higher to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
+
+You can still use and configure the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and won't receive any code fixes. 
+
+:::
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
new file mode 100644
index 00000000000..bcd5fd02452
--- /dev/null
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -0,0 +1,52 @@
+
+<VersionBlock firstVersion="1.6">
+
+- Have a [multi-tenant dbt Cloud](/docs/deploy/regions) instance, hosted in North America
+- Have both your production and development environments running dbt version 1.6 or higher 
+- Use Snowflake and Postgres, BigQuery, Databricks, and Redshift data platform
+- Install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow) package
+  * Note &mdash; After installing the package, make sure you run at least one model.
+- Set up the [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated tool to import metric definitions 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
+
+</VersionBlock>
+
+
+<VersionBlock lastVersion="1.5">
+
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br />
+- Have both your production and development environments running dbt version 1.3 or higher <br />
+- Use Snowflake data platform <br />
+- Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=1.3.0, <1.4.0</code> in your dbt project <br />
+  * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
+- Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.3" lastVersion="1.3" >
+
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br />
+- Have both your production and development environments running dbt version 1.3 or higher <br />
+- Use Snowflake data platform <br />
+- Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=1.3.0, <1.4.0</code> in your dbt project <br />
+  * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
+- Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.2">
+
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br /> 
+- Have both your production and development environments running dbt version 1.2 <br />
+- Use Snowflake data platform <br />
+- Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=0.3.0, <0.4.0</code> in your dbt project <br />
+  * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
+- Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+
+</VersionBlock>
diff --git a/website/static/_redirects b/website/static/_redirects
index 27e30e83c9e..8f5d274d3f3 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,4 +1,8 @@
+## semantic layer
 
+/docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
+/docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
+/docs/use-dbt-semantic-layer/quickstart-semantic-layer /docs/use-dbt-semantic-layer/quickstart-sl 301
 ## refocus deploy page
 /docs/collaborate/environments/environments-in-dbt /docs/environments-in-dbt 301
 /docs/collaborate/environments/dbt-cloud-environments /docs/deploy/dbt-cloud-environments 301

From cfc986a09cc2bebde2ed5887ac3d95f23c08f60a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 12:22:10 +0100
Subject: [PATCH 019/703] fix links

---
 website/docs/docs/build/about-metricflow.md                   | 2 +-
 website/docs/docs/build/build-metrics-intro.md                | 2 +-
 website/docs/docs/cloud/about-cloud/dbt-cloud-features.md     | 2 +-
 website/docs/docs/deploy/deploy-environments.md               | 2 +-
 .../docs/guides/dbt-ecosystem/sl-partner-integration-guide.md | 4 ++--
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index f35bed24044..e02fa778b05 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -10,7 +10,7 @@ This guide introduces MetricFlow's fundamental ideas for new users. MetricFlow,
 
 :::info
 
-MetricFlow is a new way to define metrics in dbt and one of the key components of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-semantic-layer). It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
+MetricFlow is a new way to define metrics in dbt and one of the key components of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
 
 To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 
diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index e98ee013d0b..617b3d1bc4a 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -7,7 +7,7 @@ tags: [Metrics, Semantic Layer, Governance]
 hide_table_of_contents: true
 ---
 
-Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-semantic-layer) and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics.
+Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics.
 
 Use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
diff --git a/website/docs/docs/cloud/about-cloud/dbt-cloud-features.md b/website/docs/docs/cloud/about-cloud/dbt-cloud-features.md
index f301dfce34b..a7555adb6a8 100644
--- a/website/docs/docs/cloud/about-cloud/dbt-cloud-features.md
+++ b/website/docs/docs/cloud/about-cloud/dbt-cloud-features.md
@@ -63,7 +63,7 @@ link="/docs/cloud/dbt-cloud-ide/develop-in-the-cloud"
    <Card
     title="dbt Semantic Layer*"
     body="Use the dbt Semantic Layer to define metrics alongside your dbt models and query them from any integrated analytics tool. Get the same answers everywhere, every time."
-    link="/docs/use-dbt-semantic-layer/dbt-semantic-layer"
+    link="/docs/use-dbt-semantic-layer/dbt-sl"
     icon="pencil-paper"/>  
 
    <Card
diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md
index da54b918436..9c747b2112c 100644
--- a/website/docs/docs/deploy/deploy-environments.md
+++ b/website/docs/docs/deploy/deploy-environments.md
@@ -30,7 +30,7 @@ To create a new dbt Cloud development environment, navigate to **Deploy** -> **E
 
 ### Semantic Layer
 
-For Semantic Layer-eligible customers, the next section of environment settings is the Semantic Layer configurations. [The Semantic Layer setup guide](/docs/use-dbt-semantic-layer/setup-dbt-semantic-layer) has the most up-to-date setup instructions!
+For Semantic Layer-eligible customers, the next section of environment settings is the Semantic Layer configurations. [The Semantic Layer setup guide](/docs/use-dbt-semantic-layer/setup-sl) has the most up-to-date setup instructions!
 
 ### Deployment connection
 
diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index f2fffd43994..6f7ae1df2e7 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -86,7 +86,7 @@ To build a successful and seamless dbt Semantic Layer integration, it should exp
 
 This section will explain how to connect to and query the [Discovery API](/docs/dbt-cloud-apis/discovery-api) for model and metric definitions. 
 
-To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-semantic-layer/dbt-semantic-layer#prerequisites). 
+To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-semantic-layer/dbt-sl#prerequisites). 
 
 <details>
   <summary><b>Discovery API authorization</b></summary>
@@ -652,7 +652,7 @@ If you'd like to become a formal partner, have product feedback/questions, or ar
 
 ## Related docs
 
-- [dbt Semantic Layer docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-semantic-layer) to learn about the product.
+- [dbt Semantic Layer docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
 - [dbt Metrics docs](https://docs.getdbt.com/docs/building-a-dbt-project/metrics) for more information about its components.
 - [dbt Semantic Layer intro blog](https://www.getdbt.com/blog/dbt-semantic-layer/) and [launch blog](https://www.getdbt.com/blog/frontiers-of-the-dbt-semantic-layer/) to learn more about the product vision and purpose. 
 - [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.

From 81d018c3f5524f76dd2ea2794049517c43e13981 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 11 Jul 2023 12:30:02 +0100
Subject: [PATCH 020/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 40acade5138..b16a815198c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -67,7 +67,6 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 ## Related docs
 
 - [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
-- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) page for more information on plan availability
 - [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
 
 </VersionBlock>

From 15e48c822d8dabce00fd7af2391b18f1e3c16da6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 13:12:16 +0100
Subject: [PATCH 021/703] adding partials

---
 website/dbt-versions.js                       |  4 +
 website/docs/docs/build/metrics.md            | 10 +--
 .../avail-sl-integrations.md                  |  8 +-
 .../docs/use-dbt-semantic-layer/dbt-sl.md     |  8 +-
 .../docs/use-dbt-semantic-layer/graphql.md    |  6 +-
 .../docs/docs/use-dbt-semantic-layer/jdbc.md  |  6 +-
 .../use-dbt-semantic-layer/quickstart-sl.md   |  4 +
 .../docs/use-dbt-semantic-layer/setup-sl.md   | 82 +++++++++----------
 .../use-dbt-semantic-layer/sl-api-overview.md | 23 +++---
 website/snippets/_legacy-sl-callout.md        |  4 +-
 website/snippets/_new-sl-changes.md           |  8 ++
 website/snippets/_upgrade-new-sl.md           |  7 ++
 website/snippets/_v2-sl-prerequisites.md      |  4 +-
 13 files changed, 89 insertions(+), 85 deletions(-)
 create mode 100644 website/snippets/_new-sl-changes.md
 create mode 100644 website/snippets/_upgrade-new-sl.md

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index f2834c921b6..8802538a8e6 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -187,6 +187,10 @@ exports.versionedPages = [
     "category": "docs/use-dbt-semantic-layer/jdbc",
     "firstVersion": "1.6",
   },
+  {
+    "category": "docs/use-dbt-semantic-layer/sl-manifest",
+    "firstVersion": "1.6",
+  },
 ]
 
 exports.versionedCategories = [
diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index 4492d2f5770..4e16752e3ae 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -6,15 +6,9 @@ keywords:
   - dbt metrics layer
 ---
 
-:::info dbt metrics no longer supported
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
-dbt_metrics is no longer supported in v1.6 and higher, and has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics). [MetricFlow](/docs/build/about-metricflow?version=1.6), a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), is a new framework for defining metrics in dbt and has replaced dbt_metrics.
-
-You can still use the dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and don't receive any code fixes. 
-
-To build your metrics and semantic layer, we **highly** recommend you [upgrade to dbt v1.6 and higher](/guides/migration/versions/upgrading-to-v1.6) to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
-
-:::
+<LegacyInfo />
 
 <Changelog>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index b3f10309999..977f05bac2d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -7,13 +7,9 @@ sidebar_label: "Available integrations"
 
 <VersionBlock firstVersion="1.6">
 
-:::info Revamped dbt Semantic Layer
+import NewSLChanges from '/snippets/_new-sl-changes.md';
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
-
-:::
+<NewSLChanges />>
 
 Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (Discovery, JDBC, and GraphQL API). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 6901c5b1726..f252c2d55de 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -7,13 +7,9 @@ sidebar_label: "About the dbt Semantic Layer"
 
 <VersionBlock firstVersion="1.6">
 
-:::info Revamped dbt Semantic Layer
+import NewSLChanges from '/snippets/_new-sl-changes.md';
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
-
-:::
+<NewSLChanges />
 
 The dbt Semantic Layer, powered by MetricFlow, allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/graphql.md b/website/docs/docs/use-dbt-semantic-layer/graphql.md
index 726039dcf68..fd2f60347b0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/graphql.md
+++ b/website/docs/docs/use-dbt-semantic-layer/graphql.md
@@ -7,11 +7,9 @@ tags: ["semantic-layer, apis"]
 
 <VersionBlock lastVersion="1.5">
 
-:::info
+import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
-The dbt Semantic Layer APIs are available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about the Semantic APIs, make sure you select v1.6 or higher in the docs navigation header and upgrade your dbt version. 
-
-:::
+<UpgradeSL />
 
 ADD GRAPHQL CONTENT HERE
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
index d22f500765d..c7189794aa3 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -7,9 +7,9 @@ tags: ["semantic-layer, apis"]
 
 <VersionBlock lastVersion="1.5">
 
-:::info
-The dbt Semantic Layer APIs are available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about the Semantic APIs, make sure you select v1.6 or higher in the docs navigation header and upgrade your dbt version. 
-:::
+import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+
+<UpgradeSL />
 
 ADD JDBC CONTENT HERE
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index e844132c587..9c5411ad3c3 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -7,6 +7,10 @@ sidebar_label: "Getting started"
 
 <VersionBlock firstVersion="1.6">
 
+import NewSLChanges from '/snippets/_new-sl-changes.md';
+
+<NewSLChanges />
+
 This getting started page recommends a workflow to help you get started creating your first metrics. Here are the following steps you'll take:
 
 - [Create a semantic model](#create-a-semantic-model)
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index b16a815198c..8e1f04db105 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -7,13 +7,9 @@ sidebar_label: "Set up the dbt Semantic Layer"
 
 <VersionBlock firstVersion="1.6">
 
-:::info Upgrade your dbt version to access the dbt Semantic Layer
+import NewSLChanges from '/snippets/_new-sl-changes.md';
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
-
-:::
+<NewSLChanges />
 
 With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
 
@@ -29,6 +25,44 @@ import SetUp from '/snippets/_v2-sl-prerequisites.md';
 <Snippet src="sl-considerations-banner" />
 
 
+## Set up dbt Semantic Layer
+
+You can set up the dbt Semantic Layer in dbt Cloud at the environment and  project level by following these steps:
+
+
+1. Create a new environment in dbt Cloud by selecting **Deploy** and then **Environments**.
+2. Select **dbt Version 1.6** (or the latest) and enter your deployment credentials.
+3. To configure the new Semantic Layer, you must have a successful run in your new environment. We recommend running `dbt ls` since `dbt build` won’t succeed until you’ve created and defined semantic models and metrics.
+4. To enable the dbt Semantic Layer, go to the **Account Settings** page and then select the specific project you want to enable the Semantic Layer for.
+5. In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
+6. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
+8. You’re done 🎉! The semantic layer should is now enabled for your project. 
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" width="85%" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
+
+
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo />
+
+With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
+
+## Prerequisites
+
+Before you set up the dbt Semantic Layer, make sure you meet the following:
+
+<Snippet src="sl-prerequisites" />
+
+
+<Snippet src="sl-considerations-banner" />
+
+
 ## Set up dbt Semantic Layer
 
 Before you continue with the following steps, you **must** have a multi-tenant dbt Cloud account hosted in North America. 
@@ -61,46 +95,10 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 - An [API service token](/docs/dbt-cloud-apis/service-tokens) with job admin and metadata permissions
 - Add the items above to the relevant fields in your integration tool
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
-
- 
-## Related docs
-
-- [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
-- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
-
-</VersionBlock>
-
-<VersionBlock lastVersion="1.5">
-
-import LegacyInfo from '/snippets/_legacy-sl-callout.md';
-
-<LegacyInfo />
-
-With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
-
-## Prerequisites
-
-Before you set up the dbt Semantic Layer, make sure you meet the following:
-
-<Snippet src="sl-prerequisites" />
-
-
-<Snippet src="sl-considerations-banner" />
-
-
-## Set up dbt Semantic Layer
 
-<Snippet src="sl-set-up-steps" />
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
 
- 
-## Related docs
 
-- [Integrated partner tools](https://www.getdbt.com/product/semantic-layer-integrations) for info on the different integration partners and their documentation
-- [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) page for more information on plan availability
-- [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, properties, filters, and how to define and query metrics
-- [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations
 
 </VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
index 39c9e1c571a..8e83f9dd25c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
@@ -8,21 +8,20 @@ hide_table_of_contents: true
 
 <VersionBlock lastVersion="1.5">
 
-:::info
+import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
-The revamped dbt Semantic Layer is available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
+<UpgradeSL />
 
-To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
 
-:::
+With the Semantic Layer APIs, you can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
 
-Query dbt metrics via external integrations using the dbt Semantic Layer APIs. With the Semantic Layer APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
+The full dbt Semantic Layer lets you create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and MetricFlow — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
 
-By leveraging metrics in dbt Cloud, you can create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and dbt metrics — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+The dbt Semantic Layer generates a [`semantic_manifest.json` artifact file](/docs/use-dbt-semantic-layer/sl-manifest). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
 
-The dbt Semantic Layer generates a [`semantic_manifest.json`](/docs/use-dbt-semantic-layer/sl-manifest) artifact file. This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+To query metrics using the dbt Semantic Lay API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
 
-The dbt Semantic Layer provides the following APIs:
+The dbt Semantic Layer provides the following APIs, which you can refer to for more info:
 
 <div className="grid--3-col">
 
@@ -59,15 +58,15 @@ Requests to the dbt Semantic Layer APIs can be authorized through two types of A
 
 <VersionBlock firstVersion="1.6">
 
-Query dbt metrics via external integrations using the dbt Semantic Layer APIs. With the Semantic Layer APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
+With the Semantic Layer APIs, you can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
 
-By leveraging metrics in dbt Cloud, you can create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and dbt metrics — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+The full dbt Semantic Layer lets you create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and MetricFlow — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
 
-The dbt Semantic Layer generates a [`semantic_manifest.json`](/docs/use-dbt-semantic-layer/sl-manifest) artifact file. This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+The dbt Semantic Layer generates a [`semantic_manifest.json` artifact file](/docs/use-dbt-semantic-layer/sl-manifest). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
 
 To query metrics using the dbt Semantic Lay API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
 
-The dbt Semantic Layer provides the following APIs:
+The dbt Semantic Layer provides the following APIs, which you can refer to for more info:
 
 <div className="grid--3-col">
 
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index 8ed4e162f75..4912b770c74 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,10 +1,10 @@
-:::info Upgrade your dbt version to access the dbt Semantic Layer
+:::info Upgrade your dbt version to access the new dbt Semantic Layer
 
 The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
 
 **What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
 
-**Who does this affect?** The revamped dbt Semantic Layer is available for users on a Team or Enterprise plans on dbt v1.6 and higher. To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+**Who does this affect?** The revamped dbt Semantic Layer is available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) on dbt v1.6 and higher. To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
 
 **What should you do?** We **highly** recommend you upgrade to dbt v1.6 and higher to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
 
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
new file mode 100644
index 00000000000..1595078cd8a
--- /dev/null
+++ b/website/snippets/_new-sl-changes.md
@@ -0,0 +1,8 @@
+
+:::tip Introducing the new dbt Semantic Layer 🎉
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. 
+
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+
+:::
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
new file mode 100644
index 00000000000..fb9c106e3df
--- /dev/null
+++ b/website/snippets/_upgrade-new-sl.md
@@ -0,0 +1,7 @@
+:::info Upgrade to access the new dbt Semantic Layer
+
+The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
+
+To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+
+:::
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index bcd5fd02452..a7b7648fb50 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -6,8 +6,8 @@
 - Use Snowflake and Postgres, BigQuery, Databricks, and Redshift data platform
 - Install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow) package
   * Note &mdash; After installing the package, make sure you run at least one model.
-- Set up the [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
+- Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
+  * Developer accounts will be able to query manually via the CLI using SQL, but will not be able to dynamically query or browse metrics in external tools, which requires access to the Semantic Layer APIs.<br />
 
 </VersionBlock>
 

From 236893b2174831db21c0efc5dc6c387178aaeba0 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 13:14:04 +0100
Subject: [PATCH 022/703] nit

---
 website/snippets/_legacy-sl-callout.md | 2 +-
 website/snippets/_upgrade-new-sl.md    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index 4912b770c74..ab079bcbf77 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -6,7 +6,7 @@ The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.c
 
 **Who does this affect?** The revamped dbt Semantic Layer is available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) on dbt v1.6 and higher. To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
 
-**What should you do?** We **highly** recommend you upgrade to dbt v1.6 and higher to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. 
+**What should you do?** We **highly** recommend you upgrade to dbt v1.6 and higher to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. ALSO ADD LINK TO MIGRATION GUIDE
 
 You can still use and configure the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and won't receive any code fixes. 
 
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
index fb9c106e3df..fe3e6e532c6 100644
--- a/website/snippets/_upgrade-new-sl.md
+++ b/website/snippets/_upgrade-new-sl.md
@@ -2,6 +2,6 @@
 
 The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
 
-To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). ALSO ADD LINK TO MIGRATION GUIDE
 
 :::

From 52b063ded6797b9ac9b3acc2ab74e43cf73c010f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 13:47:16 +0100
Subject: [PATCH 023/703] add migration guide

---
 website/docs/guides/migration/sl-migration.md | 139 ++++++++++++++++++
 website/sidebars.js                           |   1 +
 website/snippets/_legacy-sl-callout.md        |  11 +-
 website/snippets/_upgrade-new-sl.md           |   2 +-
 4 files changed, 147 insertions(+), 6 deletions(-)
 create mode 100644 website/docs/guides/migration/sl-migration.md

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
new file mode 100644
index 00000000000..7e9083ffe74
--- /dev/null
+++ b/website/docs/guides/migration/sl-migration.md
@@ -0,0 +1,139 @@
+---
+title: "dbt Semantic Layer migration guide"
+id: sl-migration
+sidebar_label: "dbt Semantic Layer migration"
+description: "This migration guide will help you migrate from the legacy dbt Semantic Layer to the new one."
+tags: ["semantic-layer"]
+---
+
+WILL CLEAN THIS UP
+
+add who the audience is, what they need to do, and what they need to know
+
+**Technical resources:**
+
+- [Quickstart guide](/docs/build/sl-getting-started)
+- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
+- Configuring SL docs
+- SL API docs
+- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
+- Developer workflow demo
+
+## Intro
+
+The legacy Semantic Layer is supported in dbt v1.5 or lower and will be deprecated.  The new Semantic Layer is in Beta and supported in dbt v1.6 or higher. If you’re a user of the legacy Semantic Layer and want to use the new one, follow this guide. NOTE: **This requires downtime of your Semantic Layer**
+
+There are 3 migration components: 
+
+- Create semantic models and migrate metric definitions
+
+- Setup new Semantic Layer in dbt Cloud
+
+- Update applicable downstream integrations
+
+This guide assumes you have an environment with the legacy Semantic Layer enabled. There are two migration paths available. **Note: Both migration paths involve some downtime:** 
+
+1. Migrating an existing environment to the new Semantic Layer. 
+2. Creating a net new environment running the Semantic Layer.
+
+ 
+
+## Migrate an existing environment
+
+This approach will be a hard cut to the new Semantic Layer. Once you update your metrics to the new spec in 1.6, the legacy Semantic Layer will no longer work. You will need to set up the semantic layer in your deployment environment after upgrading to the new metrics spec to avoid unnecessary downtime. 
+
+### **Step 1: Migrate Metric configs to the new spec.**
+
+The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. We’ve introduced two new objects that MetricFlow expects in order to build a semantic graph: [Semantic Models](/docs/build/semantic-models) and [Metrics](/docs/build/metrics-overview). You will need to migrate your Metrics configs to the new spec. There are two steps to this process:
+
+- Creating net new Semantic Model configs. These will be YAML files in your dbt project.
+- Upgrading the Metrics configs in your project to the new spec.
+
+We’ve built a migration tool that automates this process for you. Check out the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) for installation instructions. Derived metrics are not supported in the migration tool, and will have to be migrated manually.  
+
+To learn how to configure Semantic Models and Metrics, check out our [getting started guide]/docs/build/sl-getting-started). **Note: Developing Metrics and Semantic Models in the IDE is not supported in the Beta release.**
+
+Once you’ve created your Semantic Models and created or updated your Metrics, you can test your updated configs using the MetricFlow CLI tools:
+
+1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`
+2. Run `dbt parse`. This will parse your project and create a semantic_manifest.json file in your target directory.
+3. Run `mf list metrics` to view the metrics in your project.
+4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
+5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
+
+When you have tested your metrics and are satisfied that they are correct, commit your changes and push them to your dbt repository. **When you merge the new configurations, the legacy semantic layer will no longer work.** It is recommended that you complete step 2 as soon as you merge the changes to minimize downtime.
+
+### **Step 2: Switch to the new Semantic Layer in your existing Cloud environment**
+
+The suggested workflow for upgrading your Cloud environment to use the new semantic layer is as follows:
+
+- Upgrade the existing deployment environment for your project to v1.6 or higher. Ensure that all jobs are running correctly with the new dbt version. Make any necessary updates to jobs that are impacted by the version upgrade.
+- To set up the Semantic Layer, you need to run a dbt job in your deployment environment. You can use any dbt command, but it must be successful.
+- To activate the new Semantic Layer, go to the ***Account Settings*** page and then **Project Details.** Select the specific project you want to enable the Semantic Layer for.
+- In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
+- After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. You can use this to connect to the Semantic Layer JDBC interface, which you will use in the next step. This interface allows you to connect and query your metrics in downstream tools.
+- Next, head back to the **Project Details** page and select **Generate Service Token** to create a semantic layer service token. Save this token for later.
+- You’re done! The Semantic Layer should now be enabled for your project! Time to start querying metrics.
+
+## Step 3: Update connection in downstream integrations
+
+Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
+
+**Migration guide for Hex**
+
+…
+
+**Migration guide for Mode**
+
+…
+
+## **Creating a new environment running the new Semantic Layer**
+
+This approach will let you run the legacy Semantic Layer in your existing environment while you stage the metrics spec changes to your dbt project. You’ll set up a brand new deployment environment on dbt v1.6 or higher, which is essentially a clone of your current Semantic Layer environment. When you’re ready to merge your changes, the environment is already set up, and you can safely deprecate your old environment. This approach still involves downtime.
+
+### **Step 1: Setup a new environment running 1.6 and enable the SL**
+
+You will need to create a new environment for the Semantic Layer and migrate your existing jobs to this environment. The workflow is as follows:
+
+- In dbt Cloud, select **Deploy** and then **Environments** to create a new environment.
+- Select **dbt Version 1.6** and enter your deployment credentials.
+- There needs to be a successful run in your new environment to configure the Semantic Layer. We recommend running `dbt ls` as a `dbt build` won’t succeed until you’ve updated your metrics configs.
+- Migrate your jobs from your old deployment environment.
+- To activate the new Semantic Layer, go to the **Account Settings** page and then **Project Details.** Select the specific project you want to enable the Semantic Layer for.
+- In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
+- After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+- Next, head back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
+- You’re done! The semantic layer should now be enabled for your project! The legacy Semantic Layer will still be running in your old environment.
+
+### **Step 2: Migrate metric configs to the new spec.**
+
+The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. We’ve introduced two new objects that MetricFlow expects in order to build a semantic graph: [Semantic Models](/docs/build/semantic-models) and [Metrics](/docs/build/metrics-overview). You will need to migrate your Metrics configs to the new spec. The process broadly looks:
+
+- Creating net new Semantic Model configs. These will be YAML files in your dbt project.
+- Upgrading the Metrics configs in your project to the new spec.
+
+We’ve built a migration tool that automates this process for you. Check out the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) for installation instructions. Derived metrics are not supported in the migration tool, and will have to be migrated manually.  
+
+To learn how to configure Semantic Models and Metrics, check out our [getting started guide](/docs/build/sl-getting-started). **Note: Developing Metrics and Semantic Models in the IDE is not supported in the Beta release.**
+
+Once you’ve created your Semantic Models and created or updated your Metrics, you can test your updated configs using the MetricFlow CLI tools:
+
+1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`
+2. Run `dbt parse` . This will parse your project, and create a semantic_manifest.json file in your target directory.
+3. Run `mf list metrics` to view the metrics in your project.
+4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
+5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
+
+When you have tested your metrics and are satisfied that they are correct, commit your changes and push them to your dbt repository. **When you merge the new configurations, the legacy Semantic Layer will no longer work**
+
+## Step 3: Update connection in downstream integrations
+
+Now that your semantic layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
+
+**Migration guide for Hex**
+
+…
+
+**Migration guide for Mode**
+
+…
diff --git a/website/sidebars.js b/website/sidebars.js
index 2c68ecf443a..c372fd9f2a9 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -968,6 +968,7 @@ const sidebarSettings = {
       type: "category",
       label: "Migration",
       items: [
+        "guides/migration/sl-migration",
         {
           type: "category",
           label: "Versions",
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index ab079bcbf77..eb62b6e3e5b 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,13 +1,14 @@
 :::info Upgrade your dbt version to access the new dbt Semantic Layer
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. This means the legacy Semantic Layer (dbt v1.5 or lower) is no longer supported and won't receive any code fixes.
 
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt. We also introduce improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
 
-**Who does this affect?** The revamped dbt Semantic Layer is available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) on dbt v1.6 and higher. To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). 
+**Who does this affect?** The new and revamped dbt Semantic Layer is available for users on [Team or Enterprise plans](https://www.getdbt.com/pricing/) using dbt v1.6 or higher. 
 
-**What should you do?** We **highly** recommend you upgrade to dbt v1.6 and higher to use the new and most recent version of the dbt Semantic Layer, powered by MetricFlow. ALSO ADD LINK TO MIGRATION GUIDE
+**What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
-You can still use and configure the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, however, these versions are no longer supported and won't receive any code fixes. 
+-is this correct? no right? 
+Although you can still use the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, these versions are no longer supported and won't receive any code fixes. 
 
 :::
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
index fe3e6e532c6..b30f8318068 100644
--- a/website/snippets/_upgrade-new-sl.md
+++ b/website/snippets/_upgrade-new-sl.md
@@ -2,6 +2,6 @@
 
 The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
 
-To learn more about it, make sure you select v1.6 or higher in the docs navigation header and [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud). ALSO ADD LINK TO MIGRATION GUIDE
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
 :::

From 21ee65420ea461d886ffe538c3f385b7f5af41f0 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 11 Jul 2023 11:54:11 -0700
Subject: [PATCH 024/703] Info about admin api v2 vs v3

---
 website/docs/docs/dbt-cloud-apis/admin-cloud-api.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
index 62b13f7aeb5..06d44fcbbc9 100644
--- a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
+++ b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
@@ -10,7 +10,7 @@ The dbt Cloud Administrative API is enabled by default for [Team and Enterprise
 - Manage your dbt Cloud account
 - and more
 
-Check out our dbt Cloud Admin API docs to help you access the API:
+dbt Cloud currently supports two versions of the Administrative API: v2 and v3. In general, v3 is the recommended version to use, but we don't yet have all our v2 routes upgraded to v3. We're currently working on this. If you can't find what you're looking for in our v3 docs, check out the shorter list of v2 endpoints and see if that helps. 
 
 <div className="grid--2-col">
 

From 1e694f60664fa60efa61cffbf42cd894a8596f99 Mon Sep 17 00:00:00 2001
From: Abhishek Singh <abhishek.o.singh@oracle.com>
Date: Tue, 11 Jul 2023 18:10:49 -0700
Subject: [PATCH 025/703] Added documentation for Oracle Python models

---
 .../warehouse-setups/oracle-setup.md          | 156 +++++++++++++++++-
 1 file changed, 154 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/warehouse-setups/oracle-setup.md b/website/docs/reference/warehouse-setups/oracle-setup.md
index 80ec7a39254..fbca33a64d9 100644
--- a/website/docs/reference/warehouse-setups/oracle-setup.md
+++ b/website/docs/reference/warehouse-setups/oracle-setup.md
@@ -215,9 +215,9 @@ Note that Oracle Client versions 21c and 19c are not supported on Windows 7.
 </TabItem>
 </Tabs>
 
-## Configure wallet for Oracle Autonomous Database in Cloud
+## Configure wallet for Oracle Autonomous Database (ADB-S) in Cloud
 
-dbt can connect to Oracle Autonomous Database (ADB) in Oracle Cloud using either TLS (Transport Layer Security) or mutual TLS (mTLS). TLS and mTLS provide enhanced security for authentication and encryption.
+dbt can connect to Oracle Autonomous Database (ADB-S) in Oracle Cloud using either TLS (Transport Layer Security) or mutual TLS (mTLS). TLS and mTLS provide enhanced security for authentication and encryption.
 A database username and password is still required for dbt connections which can be configured as explained in the next section [Connecting to Oracle Database](#connecting-to-oracle-database).
 
 <Tabs
@@ -480,6 +480,157 @@ dbt_test:
 
 </Tabs>
 
+<VersionBlock firstVersion="1.5.1">
+
+## Python Models using Oracle Autonomous Database (ADB-S)
+
+Oracle's Autonomous Database Serverless (ADB-S) users can run dbt-py models using Oracle Machine Learning (OML4PY) which is available without any extra setup required.
+
+### Features
+- User Defined Python function is run in an ADB-S spawned Python 3.10 runtime
+- Import [3rd party Python packages](https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/oml4py-notebook.html#GUID-78225241-CD6B-4588-AD4B-799079FA1784) installed in the default Python runtime
+- Access to Database session in the Python function
+- DataFrame read API to read `TABLES`, `VIEWS` and ad-hoc `SELECT` queries as DataFrames
+- DataFrame write API to write DataFrames as `TABLES`
+- Supports both table and incremental materialization
+- Integration with conda (Coming Soon)
+
+### Setup
+
+#### Required roles
+
+- User must be non-ADMIN to execute the Python function
+- User must be granted the `OML_DEVELOPER` role
+
+#### OML Cloud Service URL
+
+OML Cloud Service URL is of the following format
+```text
+https://tenant1-dbt.adb.us-sanjose-1.oraclecloudapps.com
+```
+In this example, 
+  - `tenant1` is the tenancy ID 
+  - `dbt` is the database name 
+  - `us-sanjose-1` is the datacenter region 
+  - `oraclecloudapps.com` is the root domain
+
+Add `oml_cloud_service_url` to your existing `~/.dbt/profiles.yml`
+
+<File name='~/.dbt/profiles.yml'>
+
+```yaml
+dbt_test:
+   target: dev
+   outputs:
+      dev:
+         type: oracle
+         user: "{{ env_var('DBT_ORACLE_USER') }}"
+         pass: "{{ env_var('DBT_ORACLE_PASSWORD') }}"
+         tns_name: "{{ env_var('DBT_ORACLE_TNS_NAME') }}"
+         schema: "{{ env_var('DBT_ORACLE_SCHEMA') }}"
+         oml_cloud_service_url: "https://tenant1-dbt.adb.us-sanjose-1.oraclecloudapps.com"
+```
+</File>
+
+### Python model configurations
+
+| Configuration | Datatype | Examples                                                                                      |
+|--|--------|-----------------------------------------------------------------------------------------------|
+| Materialization | String | `dbt.config(materialized="incremental")` or `dbt.config(materialized="table")`                |
+| Service | String | `dbt.config(service="HIGH")` or `dbt.config(service="MEDIUM")` or `dbt.config(service="LOW")` |
+| Async Mode | Boolean    | `dbt.config(async_flag=True)`  
+| Timeout in seconds only to be used with **_async_** mode (`min: 1800` and `max: 43200`) | Integer    | `dbt.config(timeout=1800)`  |
+
+In async mode, dbt-oracle will schedule a Python job, poll the job's status and wait for it to complete.
+Without async mode, dbt-oracle will immediately invoke the Python job in a blocking manner. Use async mode for long running Python job
+
+### Python model examples
+
+#### Refer other model
+
+Use `dbt.ref(model_name)` to refer either SQL or Python model
+
+```python
+def model(dbt, session):
+    # Must be either table or incremental (view is not currently supported)
+    dbt.config(materialized="table")
+    # returns oml.core.DataFrame referring a dbt model
+    s_df = dbt.ref("sales_cost")
+    return s_df
+```
+
+#### Refer a source
+
+Use `dbt.source(source_schema, table_name)`
+
+```python
+def model(dbt, session):
+    # Must be either table or incremental (view is not currently supported)
+    dbt.config(materialized="table")
+    # oml.core.DataFrame representing a datasource
+    s_df = dbt.source("sh_database", "channels")
+    return s_df
+
+```
+
+#### Incremental materialization
+
+```python
+def model(dbt, session):
+    # Must be either table or incremental
+    dbt.config(materialized="incremental")
+    # oml.DataFrame representing a datasource
+    sales_cost_df = dbt.ref("sales_cost")
+
+    if dbt.is_incremental:
+        cr = session.cursor()
+        result = cr.execute(f"select max(cost_timestamp) from {dbt.this.identifier}")
+        max_timestamp = result.fetchone()[0]
+        # filter new rows
+        sales_cost_df = sales_cost_df[sales_cost_df["COST_TIMESTAMP"] > max_timestamp]
+
+    return sales_cost_df
+```
+
+#### Concatenate a new column in Dataframe
+
+```python
+
+def model(dbt, session):
+    dbt.config(materialized="table")
+    dbt.config(async_flag=True)
+    dbt.config(timeout=1800)
+
+    sql = f"""SELECT customer.cust_first_name,
+       customer.cust_last_name,
+       customer.cust_gender,
+       customer.cust_marital_status,
+       customer.cust_street_address,
+       customer.cust_email,
+       customer.cust_credit_limit,
+       customer.cust_income_level
+    FROM sh.customers customer, sh.countries country
+    WHERE country.country_iso_code = ''US''
+    AND customer.country_id = country.country_id"""
+
+    # session.sync(query) will run the sql query and returns a oml.core.DataFrame
+    us_potential_customers = session.sync(query=sql)
+
+    # Compute an ad-hoc anomaly score on the credit limit
+    median_credit_limit = us_potential_customers["CUST_CREDIT_LIMIT"].median()
+    mean_credit_limit = us_potential_customers["CUST_CREDIT_LIMIT"].mean()
+    anomaly_score = (us_potential_customers["CUST_CREDIT_LIMIT"] - median_credit_limit)/(median_credit_limit - mean_credit_limit)
+
+    # Add a new column "CUST_CREDIT_ANOMALY_SCORE"
+    us_potential_customers = us_potential_customers.concat({"CUST_CREDIT_ANOMALY_SCORE": anomaly_score.round(3)})
+
+    # Return potential customers dataset as a oml.core.DataFrame
+    return us_potential_customers
+
+```
+
+</VersionBlock>
+
 
 ## Supported Features
 
@@ -495,6 +646,7 @@ dbt_test:
 - Exposures
 - Document generation
 - Serve project documentation as a website
+- Python Models (from dbt-oracle version 1.5.1)
 - All dbt commands are supported
 
 ## Not Supported features

From e223a0c6690da14cfd3ebe307dfbd168adde38e3 Mon Sep 17 00:00:00 2001
From: schottj <julia.schottenstein@fishtownanalytics.com>
Date: Tue, 11 Jul 2023 18:49:38 -0700
Subject: [PATCH 026/703] Update job-scheduler.md

recent change to run slots
---
 website/docs/docs/deploy/job-scheduler.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-scheduler.md b/website/docs/docs/deploy/job-scheduler.md
index 479dfc7bab2..2c06980d429 100644
--- a/website/docs/docs/deploy/job-scheduler.md
+++ b/website/docs/docs/deploy/job-scheduler.md
@@ -31,7 +31,7 @@ Familiarize yourself with these useful terms to help you understand how the job
 | Over-scheduled job | A situation when a cron-scheduled job's run duration becomes longer than the frequency of the job’s schedule, resulting in a job queue that will grow faster than the scheduler can process the job’s runs. |
 | Prep time | The time dbt Cloud takes to create a short-lived environment to execute the job commands in the user's cloud data platform. Prep time varies most significantly at the top of the hour when the dbt Cloud Scheduler experiences a lot of run traffic. |
 | Run | A single, unique execution of a dbt job. |
-| Run slot | Run slots control the number of jobs that can run concurrently. Each account has a fixed number of run slots, depending on the plan tier, that are shared across projects in the account. Each running job occupies a run slot for the duration of the run, so purchasing more run slots enables more jobs to execute in parallel. |
+| Run slot | Run slots control the number of jobs that can run concurrently. Developer and team plan accounts have a fixed number of run slots; whereas, enterprise customers have unlimited run slots. Each running job occupies a run slot for the duration of the run. |
 | Threads | When dbt builds a project's DAG, it tries to parallelize the execution by using threads. The [thread](/docs/running-a-dbt-project/using-threads) count is the maximum number of paths through the DAG that dbt can work on simultaneously. The default thread count in a job is 4. |
 | Wait time | Amount of time that dbt Cloud waits before running a job, either because there are no available slots or because a previous run of the same job is still in progress. |
 

From c3323c3d4a13a31ccb5c6824dbdddb718feb0875 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 12 Jul 2023 10:30:17 +0100
Subject: [PATCH 027/703] Update website/docs/docs/deploy/job-scheduler.md

---
 website/docs/docs/deploy/job-scheduler.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-scheduler.md b/website/docs/docs/deploy/job-scheduler.md
index 2c06980d429..eae556309ba 100644
--- a/website/docs/docs/deploy/job-scheduler.md
+++ b/website/docs/docs/deploy/job-scheduler.md
@@ -31,7 +31,7 @@ Familiarize yourself with these useful terms to help you understand how the job
 | Over-scheduled job | A situation when a cron-scheduled job's run duration becomes longer than the frequency of the job’s schedule, resulting in a job queue that will grow faster than the scheduler can process the job’s runs. |
 | Prep time | The time dbt Cloud takes to create a short-lived environment to execute the job commands in the user's cloud data platform. Prep time varies most significantly at the top of the hour when the dbt Cloud Scheduler experiences a lot of run traffic. |
 | Run | A single, unique execution of a dbt job. |
-| Run slot | Run slots control the number of jobs that can run concurrently. Developer and team plan accounts have a fixed number of run slots; whereas, enterprise customers have unlimited run slots. Each running job occupies a run slot for the duration of the run. |
+| Run slot | Run slots control the number of jobs that can run concurrently. Developer and Team plan accounts have a fixed number of run slots, and Enterprise users have [unlimited run slots](/docs/dbt-versions/release-notes/July-2023/faster-run#unlimited-job-concurrency-for-enterprise-accounts). Each running job occupies a run slot for the duration of the run. |
 | Threads | When dbt builds a project's DAG, it tries to parallelize the execution by using threads. The [thread](/docs/running-a-dbt-project/using-threads) count is the maximum number of paths through the DAG that dbt can work on simultaneously. The default thread count in a job is 4. |
 | Wait time | Amount of time that dbt Cloud waits before running a job, either because there are no available slots or because a previous run of the same job is still in progress. |
 

From e320f08d49bed910841ae4097c35015aa65306db Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 12 Jul 2023 11:54:19 +0100
Subject: [PATCH 028/703] adding roxi's updates

---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 28 +++++++++----------
 .../docs/use-dbt-semantic-layer/setup-sl.md   | 28 ++++++++++++++++---
 website/snippets/_legacy-sl-callout.md        |  2 +-
 website/snippets/_v2-sl-prerequisites.md      |  3 +-
 4 files changed, 40 insertions(+), 21 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index f252c2d55de..0329bad15fb 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -11,22 +11,20 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-The dbt Semantic Layer, powered by MetricFlow, allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
+The dbt Semantic Layer, powered by MetricFlow, allows data teams to centrally define critical business metrics like `revenue`, in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and other data applications. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice. It automatically handles joins across your data.
 
 The result? You have less duplicate coding for data teams and more consistency for data consumers. 
 
-The dbt Semantic Layer has four main parts:
+The dbt Semantic Layer has the following major components:
 
-- Define your metrics in version-controlled dbt project code using MetricFlow
-- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
-- Query your metric data using the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC driver, GraphQL API, and the Discovery API)
-- Explore and analyze your metrics in downstream tools
-
-ADD ARCHITECTURE IMAGE
+- Use [MetricFlow](/docs/build/build-metrics-intro) to define and test your semantic models and metrics in version-controlled dbt project code 
+- Use [dbt Semantic Layer interfaces] to explore and analyze these metrics in downstream tools
 
 ### What makes the dbt Semantic Layer different?
 
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
+The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
+
+To read more about why you need a universal Semantic Layer, see this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
 
 ## Prerequisites
@@ -62,11 +60,11 @@ The dbt Semantic Layer product architecture includes four primary components:
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
 | **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)**  | Use the Semantic Layer APIS, which includes the Discovery, JDBC, and GraphQL API, to access metric definitions using integrations. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
+| **MetricFlow Server Server** | A proprietary server that takes metric requests and turns.... | ✅ | ✅ | ✅ | BSL |
+| **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)**  | Use the Semantic Layer APIS, which includes the Discovery, JDBC, and GraphQL API, to access metric definitions using integrations. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
-ADD COMPONENT IMAGE
+ADD ARCHITECTURE IMAGE
 
 dbt Semantic Layer integrations will:
 
@@ -114,9 +112,9 @@ To read about best practices on structuring and organizing your metrics, review
 ## Related questions
 
 <details>
-  <summary>How do I migrate from the old Semantic Layer to the new one?</summary>
+  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
   <div>
-    <div>ADD INFO HERE AND MIGRATION SCRIPT.</div>
+    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
   </div>
 </details>
     
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 8e1f04db105..60b592074e0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -29,20 +29,34 @@ import SetUp from '/snippets/_v2-sl-prerequisites.md';
 
 You can set up the dbt Semantic Layer in dbt Cloud at the environment and  project level by following these steps:
 
+1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
+2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
+3. In the **Project Details** page, navigate to **Configure Semantic Layer**.
+4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
+    * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
+5. Select the deployment environment you want for the Semantic Layer
+6. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
+8. You’ve configured the semantic layer 🎉! You can now define your [semantic models and metrics](/docs/build/build-metrics-intro) in your project.
+
+:::tip
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+:::
 
+<!--
 1. Create a new environment in dbt Cloud by selecting **Deploy** and then **Environments**.
 2. Select **dbt Version 1.6** (or the latest) and enter your deployment credentials.
 3. To configure the new Semantic Layer, you must have a successful run in your new environment. We recommend running `dbt ls` since `dbt build` won’t succeed until you’ve created and defined semantic models and metrics.
 4. To enable the dbt Semantic Layer, go to the **Account Settings** page and then select the specific project you want to enable the Semantic Layer for.
 5. In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
-6. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+6. After you’ve entered your credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
 7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
 8. You’re done 🎉! The semantic layer should is now enabled for your project. 
+-->
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" width="85%" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
 
 
-
 </VersionBlock>
 
 <VersionBlock lastVersion="1.5">
@@ -65,6 +79,8 @@ Before you set up the dbt Semantic Layer, make sure you meet the following:
 
 ## Set up dbt Semantic Layer
 
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+
 Before you continue with the following steps, you **must** have a multi-tenant dbt Cloud account hosted in North America. 
  * Team and Enterprise accounts can set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated partner tool to import metric definitions. 
  * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
@@ -99,6 +115,10 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
 
-
-
 </VersionBlock>
+
+- [Build your metrics](/docs/build/build-metrics-intro)
+- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
+- [Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [Migrate your legacy Semantic Layer](/guides/migration/sl-migration)
+- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index eb62b6e3e5b..bd0c374dff5 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -8,7 +8,7 @@ The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.c
 
 **What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
--is this correct? no right? 
+-is this correct? no right?-->
 Although you can still use the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, these versions are no longer supported and won't receive any code fixes. 
 
 :::
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index a7b7648fb50..e8027f19c1b 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -3,7 +3,8 @@
 
 - Have a [multi-tenant dbt Cloud](/docs/deploy/regions) instance, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
-- Use Snowflake and Postgres, BigQuery, Databricks, and Redshift data platform
+- Use Snowflake, BigQuery, Databricks, and Redshift data platform
+- A successful run in the environment where your Semantic Layer is configured
 - Install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow) package
   * Note &mdash; After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 

From 280df6032f0f4a4808ab010a07adc648dcb0a5b6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 12 Jul 2023 11:54:35 +0100
Subject: [PATCH 029/703] remove space

---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 60b592074e0..eaf7a611304 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -112,7 +112,6 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 - Add the items above to the relevant fields in your integration tool
 
 
-
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
 
 </VersionBlock>

From 176b75730c5e27954da79956ad1c94b6b04eed91 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 12 Jul 2023 13:46:02 +0100
Subject: [PATCH 030/703] Update
 website/docs/docs/core/connect-data-platform/oracle-setup.md

---
 website/docs/docs/core/connect-data-platform/oracle-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/oracle-setup.md b/website/docs/docs/core/connect-data-platform/oracle-setup.md
index b29658e6808..f601709654b 100644
--- a/website/docs/docs/core/connect-data-platform/oracle-setup.md
+++ b/website/docs/docs/core/connect-data-platform/oracle-setup.md
@@ -543,7 +543,7 @@ dbt_test:
 | Timeout in seconds only to be used with **_async_** mode (`min: 1800` and `max: 43200`) | Integer    | `dbt.config(timeout=1800)`  |
 
 In async mode, dbt-oracle will schedule a Python job, poll the job's status and wait for it to complete.
-Without async mode, dbt-oracle will immediately invoke the Python job in a blocking manner. Use async mode for long running Python job
+Without async mode, dbt-oracle will immediately invoke the Python job in a blocking manner. Use async mode for long-running Python jobs.
 
 ### Python model examples
 

From 704403017207a9ae4d5c61b989fde5c6baa712f2 Mon Sep 17 00:00:00 2001
From: Owen <74567580+owenprough-sift@users.noreply.github.com>
Date: Wed, 12 Jul 2023 09:43:06 -0400
Subject: [PATCH 031/703] Update incremental-models.md

Address 2606
---
 website/docs/docs/build/incremental-models.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index 15b24520711..7af29e9d903 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -57,6 +57,7 @@ from raw_app_data.events
 {% if is_incremental() %}
 
   -- this filter will only be applied on an incremental run
+  -- (uses > to include records whose timestamp occurred since the last run of this model)
   where event_time > (select max(event_time) from {{ this }})
 
 {% endif %}
@@ -137,6 +138,7 @@ from raw_app_data.events
 {% if is_incremental() %}
 
   -- this filter will only be applied on an incremental run
+  -- (uses >= to include records arriving later on the same day as the last run of this model)
   where date_day >= (select max(date_day) from {{ this }})
 
 {% endif %}

From 6a675e2e70bff9547e6201802e6208d964bad67b Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Wed, 12 Jul 2023 15:58:00 +0200
Subject: [PATCH 032/703] Semantic Model attribute is `defaults` not `default`

Called defaults not default
---
 website/docs/docs/build/semantic-models.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index 043973ac154..c4b14d703fb 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -31,7 +31,7 @@ semantic_models:
   - name: transaction # A semantic model with the name Transactions
     model: ref('fact_transactions') # References the dbt model named `fact_transactions`
     description: "Transaction fact table at the transaction level. This table contains one row per transaction and includes the transaction timestamp."
-    default:
+    defaults:
       agg_time_dimension: transaction_date
 
     entities: # Entities included in the table are defined here. MetricFlow will use these columns as join keys.

From 7ed0db1d7cd39713bfcf12733386351b532c9aad Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 12 Jul 2023 10:27:58 -0400
Subject: [PATCH 033/703] update workflow name

---
 .github/workflows/links.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
index 48409555d70..19b0449669d 100644
--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@@ -1,4 +1,4 @@
-name: Check Docs Links
+name: Docs Link Checker
 
 on: [push]
   # schedule:

From 04f0265f04da2be8ec5256b8d499513cd105d7f0 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 12 Jul 2023 10:48:42 -0400
Subject: [PATCH 034/703] update to run at 12:00 UTC every day

---
 .github/workflows/links.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
index 19b0449669d..7e0dd52b60c 100644
--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@@ -1,9 +1,9 @@
 name: Docs Link Checker
 
-on: [push]
-  # schedule:
-  #   # Run every Monday morning at 6:00AM UTC
-  #   - cron: '0 6 * * 1'
+on:
+  schedule:
+    # Run every day at 12:00 UTC
+    - cron: '0 12 * * *'
 
 jobs:
   markdown-link-check:

From 68cd06bfb319d4930535dd8bcfd838b1b51105e8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 12 Jul 2023 15:57:30 +0100
Subject: [PATCH 035/703] update to metadata only token

updating from discovery token to metadata only token per [slack thread[https://dbt-labs.slack.com/archives/C017GDLAF7D/p1689149029221619)
---
 website/docs/docs/deploy/dashboard-status-tiles.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/dashboard-status-tiles.md b/website/docs/docs/deploy/dashboard-status-tiles.md
index 361813c526c..67aa1a93c33 100644
--- a/website/docs/docs/deploy/dashboard-status-tiles.md
+++ b/website/docs/docs/deploy/dashboard-status-tiles.md
@@ -22,7 +22,7 @@ First, be sure to enable [source freshness](/docs/deploy/source-freshness) in th
 
 In order to set up your dashboard status tile, here is what you need:
 
-1. **Discovery Token.**  You can learn how to set up a Discovery only token [here](/docs/dbt-cloud-apis/service-tokens).
+1. **Metadata Only token.**  You can learn how to set up a Metadata-Only token [here](/docs/dbt-cloud-apis/service-tokens).
 
 2. **Exposure name.** You can learn more about how to set up exposures [here](/docs/build/exposures).
 

From 6619cf5864e3df1635abeeaf2699a5fcfaebc29a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 12 Jul 2023 17:55:47 +0100
Subject: [PATCH 036/703] change to defaults

---
 website/docs/docs/build/about-metricflow.md | 2 +-
 website/docs/docs/build/dimensions.md       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index f35bed24044..6ec7ecfe4b5 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -129,7 +129,7 @@ semantic_models:
     description: "A record for every transaction that takes place. Carts are considered multiple transactions for each SKU."
     owners: support@getdbt.com
     model: (ref('transactions'))
-    default:
+    defaults:
         agg_time_dimension: metric_time
 
   # --- entities ---
diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index ec92f7595b2..ce63a0f0ed9 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -254,7 +254,7 @@ semantic_models:
   - name: sales_person_tiers
     description: SCD Type II table of tiers for sales people 
     model: {{ref(sales_person_tiers)}}
-    default:
+    defaults:
       agg_time_dimension: tier_start
 
     dimensions:

From 0e86164e4f52bdf5db63efb5895088646979767e Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 12 Jul 2023 12:05:33 -0700
Subject: [PATCH 037/703] Update redshift-setup.md

---
 .../docs/core/connect-data-platform/redshift-setup.md | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index ed01b28501d..1f7f130b8e7 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -52,7 +52,7 @@ pip is the easiest way to install the adapter:
 | ------------- | ------- | ------------ |
 | `type` | redshift | The type of data warehouse you are connecting to|
 | `host` | hostname.region.redshift.amazonaws.com| Host of cluster |
-| `port`  | 5439 |   |
+| `port`  | 5439 |  Optional. Defaults 5439 |
 | `dbname`  | my_db | Database name|
 | `schema`  | my_schema | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
@@ -63,7 +63,6 @@ pip is the easiest way to install the adapter:
 | `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
 | `autocommit`  | true | Optional, default True. Enables autocommit after each statement| 
 | `retries`  | 1 | Number of retries | 
-| `region`  | us-east-1 | Optional | 
 
 
 ## Authentication Parameters
@@ -111,10 +110,12 @@ company-name:
       host: hostname.region.redshift.amazonaws.com
       user: username
       password: password1
-      port: 5439
       dbname: analytics
       schema: analytics
       connect_timeout: None
+
+      # Optional Redshift configs:
+      port: 5439
       sslmode: prefer
       role: None
       ra3_node: true 
@@ -148,6 +149,8 @@ please refer to the official AWS documentation on [Configuration and credential
 | `iam_profile` | analyst | dbt will use the specified profile from your ~/.aws/config file |
 | `cluster_id` | CLUSTER_ID| Required for IAM |
 | `user`   | username | Account user to log into your cluster |
+| `region`  | us-east-1 | Required for IAM autentication | 
+
 
 <br/>
 
@@ -170,7 +173,7 @@ please refer to the official AWS documentation on [Configuration and credential
       autocreate: true  
       db_groups: ['ANALYSTS']
 
-      # Other Redshift configs:
+      # Optional Redshift configs:
       port: 5439
       dbname: analytics
       schema: analytics

From 7647e6cb526d9313d478226e6be976f51ebc686c Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 12 Jul 2023 13:32:03 -0700
Subject: [PATCH 038/703] Update redshift-setup.md

---
 .../core/connect-data-platform/redshift-setup.md    | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 1f7f130b8e7..3e358a3c352 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -57,7 +57,7 @@ pip is the easiest way to install the adapter:
 | `schema`  | my_schema | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 
 | `sslmode`  | prefer | optional, set the sslmode to connect to the database. Default prefer, which will use 'verify-ca' to connect. For more information on `sslmode`, see Redshift note below| 
-| `role`  | None | Optional| 
+| `role`  | None | Optional, user identifier of the current session| 
 | `autocreate`  | false | Optional, default false. Creates user if they do not exist | 
 | `db_groups`  | ['ANALYSTS'] | Optional. A list of existing database group names that the DbUser joins for the current session | 
 | `ra3_node`  | true | Optional, default False. Enables cross-database sources| 
@@ -112,7 +112,6 @@ company-name:
       password: password1
       dbname: analytics
       schema: analytics
-      connect_timeout: None
 
       # Optional Redshift configs:
       port: 5439
@@ -121,6 +120,8 @@ company-name:
       ra3_node: true 
       autocommit: true 
       threads: 4
+      connect_timeout: None
+
 ```
 
 </File>
@@ -170,13 +171,11 @@ please refer to the official AWS documentation on [Configuration and credential
       host: hostname.region.redshift.amazonaws.com
       user: alice
       iam_profile: analyst
-      autocreate: true  
-      db_groups: ['ANALYSTS']
+      dbname: analytics
+      schema: analytics
 
       # Optional Redshift configs:
       port: 5439
-      dbname: analytics
-      schema: analytics
       threads: 4
       connect_timeout: None 
       [retries](#retries): 1 
@@ -185,6 +184,8 @@ please refer to the official AWS documentation on [Configuration and credential
       ra3_node: true  
       autocommit: true  
       region: us-east-1
+      autocreate: true  
+      db_groups: ['ANALYSTS']
 
 ```
 

From 21d60d75b488e095af8c65b82f9b18d9e78661c9 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 12 Jul 2023 15:05:31 -0700
Subject: [PATCH 039/703] Update redshift-setup.md

---
 website/docs/docs/core/connect-data-platform/redshift-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 3e358a3c352..c95af0a778e 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -52,7 +52,7 @@ pip is the easiest way to install the adapter:
 | ------------- | ------- | ------------ |
 | `type` | redshift | The type of data warehouse you are connecting to|
 | `host` | hostname.region.redshift.amazonaws.com| Host of cluster |
-| `port`  | 5439 |  Optional. Defaults 5439 |
+| `port`  | 5439 |  |
 | `dbname`  | my_db | Database name|
 | `schema`  | my_schema | Schema name| 
 | `connect_timeout`  | `None` or 30 | Number of seconds before connection times out| 

From 1b92f8c8b1b95320992c5196f6f64594655bf1b0 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Wed, 12 Jul 2023 15:50:36 -0700
Subject: [PATCH 040/703] Update redshift-setup.md

---
 .../docs/docs/core/connect-data-platform/redshift-setup.md    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index c95af0a778e..e4588bb670d 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -112,9 +112,9 @@ company-name:
       password: password1
       dbname: analytics
       schema: analytics
+      port: 5439
 
       # Optional Redshift configs:
-      port: 5439
       sslmode: prefer
       role: None
       ra3_node: true 
@@ -173,9 +173,9 @@ please refer to the official AWS documentation on [Configuration and credential
       iam_profile: analyst
       dbname: analytics
       schema: analytics
+      port: 5439
 
       # Optional Redshift configs:
-      port: 5439
       threads: 4
       connect_timeout: None 
       [retries](#retries): 1 

From 8fc38a4ef202c6602a59cc72ec8bc22731151f47 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Wed, 12 Jul 2023 17:35:19 -0600
Subject: [PATCH 041/703] `merge` is not (yet) supported in dbt-redshift for
 v1.6

---
 website/docs/docs/build/incremental-models.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index 15b24520711..39b2188ff24 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -253,7 +253,7 @@ to build incremental models.
 
 Click the name of the adapter in the below table for more information about supported incremental strategies.
 
-The `merge` strategy is available in dbt-postgres and dbt-redshift beginning in dbt v1.6.
+The `merge` strategy is available in dbt-postgres beginning in dbt v1.6.
 
 <VersionBlock lastVersion="1.5">
 
@@ -276,7 +276,7 @@ The `merge` strategy is available in dbt-postgres and dbt-redshift beginning in
 | data platform adapter  | default strategy | additional supported strategies  |
 | :----------------- | :----------------| : ---------------------------------- |
 | [dbt-postgres](/reference/resource-configs/postgres-configs#incremental-materialization-strategies) | `append`         | `merge` , `delete+insert`                  |
-| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | `append`         | `merge`, `delete+insert`                  |
+| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | `append`         | `delete+insert`                  |
 | [dbt-bigquery](/reference/resource-configs/bigquery-configs#merge-behavior-incremental-models)      | `merge`          | `insert_overwrite`                       |
 | [dbt-spark](/reference/resource-configs/spark-configs#incremental-models)                           | `append`         | `merge` (Delta only)  `insert_overwrite` |
 | [dbt-databricks](/reference/resource-configs/databricks-configs#incremental-models)                 | `append`         | `merge` (Delta only) `insert_overwrite`  |

From 0f9645bc50b9ef15d3a39a5c5189da70f7569f0c Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Thu, 13 Jul 2023 16:24:44 +1200
Subject: [PATCH 042/703] Fix incorrect markdown link syntax

---
 website/docs/reference/commands/list.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/list.md b/website/docs/reference/commands/list.md
index a3bb3804c6f..6084b3dec70 100644
--- a/website/docs/reference/commands/list.md
+++ b/website/docs/reference/commands/list.md
@@ -5,7 +5,7 @@ description: "Read this guide on how dbt's ls (list) command can be used to list
 id: "list"
 ---
 
-The `dbt ls` command lists resources in your dbt project. It accepts selector arguments that are similar to those provided in [dbt run](/reference/commands/run). `dbt list` is an alias for `dbt ls`. While `dbt ls` will read your [connection profile]/docs/core/connect-data-platform/connection-profiles to resolve [`target`](/reference/dbt-jinja-functions/target)-specific logic, this command will not connect to your database or run any queries.
+The `dbt ls` command lists resources in your dbt project. It accepts selector arguments that are similar to those provided in [dbt run](/reference/commands/run). `dbt list` is an alias for `dbt ls`. While `dbt ls` will read your [connection profile](/docs/core/connect-data-platform/connection-profiles) to resolve [`target`](/reference/dbt-jinja-functions/target)-specific logic, this command will not connect to your database or run any queries.
 
 ### Usage
 ```

From e4466e12a55c3c4e4b638b51b5a211335b3804fb Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Thu, 13 Jul 2023 16:26:23 +1200
Subject: [PATCH 043/703] Add missing line to resolve markdown rendering issue

---
 website/docs/reference/artifacts/manifest-json.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/artifacts/manifest-json.md b/website/docs/reference/artifacts/manifest-json.md
index 0a151b42aa4..c71c073d842 100644
--- a/website/docs/reference/artifacts/manifest-json.md
+++ b/website/docs/reference/artifacts/manifest-json.md
@@ -49,6 +49,7 @@ sidebar_label: "Manifest"
 **Produced by:** [`build`](commands/build) [`compile`](commands/compile) [`docs generate`](commands/cmd-docs) [`list`](commands/list) [`parse`](commands/parse) [`run`](commands/run) [`run-operation`](commands/run-operation) [`seed`](commands/seed) [`show`](commands/show) [`snapshot`](commands/snapshot) [`source freshness`](commands/source) [`test`](commands/test) 
 
 </VersionBlock>
+
 This single file contains a full representation of your dbt project's resources (models, tests, macros, etc), including all node configurations and resource properties. Even if you're only running some models or tests, all resources will appear in the manifest (unless they are disabled) with most of their properties. (A few node properties, such as `compiled_sql`, only appear for executed nodes.)
 
 Today, dbt uses this file to populate the [docs site](/docs/collaborate/documentation), and to perform [state comparison](/reference/node-selection/syntax#about-node-selection). Members of the community have used this file to run checks on how many models have descriptions and tests.

From 3cc3bfc999c6ff44e04241099888f0e34bd14fec Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 13 Jul 2023 14:41:59 +0100
Subject: [PATCH 044/703] nits

---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     |  18 +-
 .../docs/docs/use-dbt-semantic-layer/jdbc.md  | 170 +++++++++++++++++-
 .../use-dbt-semantic-layer/sl-api-overview.md |  70 ++------
 website/static/img/sl-architecture.jpg        | Bin 0 -> 76397 bytes
 4 files changed, 188 insertions(+), 70 deletions(-)
 create mode 100644 website/static/img/sl-architecture.jpg

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 0329bad15fb..674656a39c1 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -18,7 +18,7 @@ The result? You have less duplicate coding for data teams and more consistency f
 The dbt Semantic Layer has the following major components:
 
 - Use [MetricFlow](/docs/build/build-metrics-intro) to define and test your semantic models and metrics in version-controlled dbt project code 
-- Use [dbt Semantic Layer interfaces] to explore and analyze these metrics in downstream tools
+- Use [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) to explore and analyze these metrics in downstream tools
 
 ### What makes the dbt Semantic Layer different?
 
@@ -39,15 +39,15 @@ import SetUp from '/snippets/_v2-sl-prerequisites.md';
 
 ## Public Preview
 
-The dbt Semantic Layer is currently available for Public Preview, which means:
+## Public Preview
 
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](#product-architecture) for more info on plan availability.
+The dbt Semantic Layer is currently available as a Public Beta, which means:
+
+&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during [Public Preview](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture).
 
-- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
-partner tool to import metric definition.
 - Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
 
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backward compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
     
 &mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
 
@@ -60,11 +60,11 @@ The dbt Semantic Layer product architecture includes four primary components:
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
 | **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **MetricFlow Server Server** | A proprietary server that takes metric requests and turns.... | ✅ | ✅ | ✅ | BSL |
+| **[MetricFlow Server]**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)**  | Use the Semantic Layer APIS, which includes the Discovery, JDBC, and GraphQL API, to access metric definitions using integrations. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
-ADD ARCHITECTURE IMAGE
+<Lightbox src="/img/sl-architecture.jpg" width="70%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. dbt Cloud helps you transform data and define metrics. Query metrics using integration tools for consistent organization-wide metrics." />
 
 dbt Semantic Layer integrations will:
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
index c7189794aa3..d64b43bc977 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -7,16 +7,174 @@ tags: ["semantic-layer, apis"]
 
 <VersionBlock lastVersion="1.5">
 
-import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+:::info Upgrade to access the new dbt Semantic Layer
 
-<UpgradeSL />
+The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
 
-ADD JDBC CONTENT HERE
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+
+:::
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.6">
+The dbt Semantic Layer JDBC driver allows users to query their metrics and dimensions using the JDBC protocol. It also exposes standard metadata like any JDBC driver.
+* The Semantic Layer JDBC API uses the ArrowFlight SQL protocol, and we utilize the open-source JDBC driver.
+* The driver can be downloaded from [Maven](https://www.google.com/url?q=https://search.maven.org/remotecontent?filepath%3Dorg/apache/arrow/flight-sql-jdbc-driver/12.0.0/flight-sql-jdbc-driver-12.0.0.jar&sa=D&source=docs&ust=1689208856858323&usg=AOvVaw2JfXh7rcmsxYkkdVNWo8PI)
+* The dbt Semantic Layer supports ArrowFlight SQL driver version 12.0.0 and higher.
+* You can embed this driver into your application stack however you wish, and we have an example project for your reference [here](https://www.google.com/url?q=https://github.com/dbt-labs/example-semantic-layer-clients&sa=D&source=docs&ust=1689208936518700&usg=AOvVaw141BhBSfphJ10M7b5ZhYs-)
+
+## Installation
+
+- Install an AWS root CA to the Java Trust Store [documentation](https://www.amazontrust.com/repository/).
+
+## Connection parameters
+The JDBC connection requires a few different connection parameters. We provide the full JDBC string that you can connect with as well as the individual components required.
+
+This is an example of a URL connection string: 
+
+
+```
+jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=202339&token=SERVICE_TOKEN
+```
+
+The components of the JDBC string are as follows:
+
+- `jdbc:arrow-flight-sql://` &mdash; The protocol for the JDBC driver.
+- `dbt Cloud access URL` &mdash; The [access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your account's dbt Cloud region. You must always add the `semantic-layer` prefix before the access URL. For example, `semantic-layer.cloud.getdbt.com`
+- `environmentId` &mdash; The unique identifier for the dbt environment, you can retrieve this from the dbt Cloud URL when you navigate to your environment under Deployments. 
+- `SERVICE_TOKEN` &mdash; dbt Cloud Service Token with “Semantic Layer Only” permissions at least. Customer can get the service token by navigating to your account settings and creating a new Service Token. The value must be encoded prior to being put into the string.
+
+
+## Querying the API
+The Semantic Layer JDBC API has built-in metadata calls that can provide a user with information about their metrics and dimensions.
+
+**Metadata Commands and Examples**
+
+
+Fetching all metrics defined
+
+```
+select * from {{ 
+	semantic_layer.metrics() 
+}}
+```
+
+
+Fetching all dimensions for a metric
+
+```
+select * from {{ 
+	semantic_layer.dimensions(metrics=['food_order_amount'])}}
+```
+Required arguments: `metrics` list with one or multiple metrics in it.
+
+
+Fetching dimension values for one or multiple metrics and single dimension
+
+```
+select * from {{ 
+semantic_layer.dimension_values(metrics=["food_order_amount"], group_by="customer__customer_name")}}
+```
+Required arguments: `metrics` list with one or multiple metrics in it, a single dimension
+
+
+## Querying Parameters
+
+| Parameter | Description                                                                                                                                                                                                                    | Example                                                                         | Required or Optional |
+|-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|----------------------|
+| metrics   | The metric name as defined in your dbt metric configuration                                                                                                                                                                    | `metrics=[revenue]`                                                             | Required             |
+| group_by  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=[user__country, metric_time]`                                         | Optional             |
+| grain     | A paremeter specific to any time dimension and changes the grain of the data from the default for the metric.                                                                                                                  | `group_by=[`Dimension('metric_time').grain('week\|day\|month\|quarter\|year')]` | Optional             |
+| where     | A where clause that allows you to filter on dimensions                                                                                                                                                                         | `where="metric_time >= '2022-03-08'"`                                           | Optional             |
+| limit     | Limit the data returned                                                                                                                                                                                                        | `limit=10`                                                                      | Optional             |
+| order     | Order the data returned                                                                                                                                                                                                        | `order_by=['-order_gross_profit']` (remove `-` for ascending order)             | Optional             |
+| explain   | If true, returns generated SQL for the data platform but does not execute it.                                                                                                                                                  | `explain=True`                                                                  | Optional             |
+
+
+
+## Examples
+
+**Fetching Metadata for Metrics. You can filter/add any SQL outside of the templating** 
+
+```
+select name, dimensions from {{ 
+	semantic_layer.metrics() 
+	}}
+WHERE name='food_order_amount'
+``` 
+
+
+**Selecting Common Dimensions for multiple metrics**
+
+```
+select * from {{ 
+semantic_layer.dimensions(metrics=['food_order_amount', 'order_gross_profit'])
+}}
+``` 
+
+
+**Querying Metrics**
+Revenue and new customers grouped by time
+
+```
+select * from {{
+semantic_layer.query(metrics=['food_order_amount','order_gross_profit'],									    group_by=['metric_time'])
+    }}
+``` 
+
+**Multiple metrics and adding granularity** 
+
+```
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+	group_by=[Dimension('metric_time').grain('month')])
+    }}
+```
+
+**Grouping by a categorical dimension**
+
+```
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+group_by=[Dimension('metric_time').grain('month'), 'customer__customer_type'])
+    }}
+``` 
+
+
+**Where Filter Example**
+
+``` 
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
+where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')")}}
+``` 
+
+**Limit & Order**
+
+```
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+  group_by=[Dimension('metric_time')],
+  limit=10,
+  order_by=['order_gross_profit'])}}
+``` 
+
+**Explain a query**
+
+```
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+		group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
+		where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')",
+		explain=True)
+}}
+```
+
+## Remarks
+
+* You may notice that sometimes we use dimensions on their own – like metric_time, and sometimes we use the object syntax you see like Dimension(’metric_time’). When we are selecting the item alone (e.g., metric_time), we don’t require the “Dimension” syntax, but when we are operating on the object (e.g., adding granularity), the object syntax is required.
+* You may see some dimensions include the following syntax with a double underscore "__" . This denotes a mapping from an entity to a dimension, and this signifies where the dimension in question lives — example (user__country), which indicates someone is looking at the country dimension from the user table.
+* The default output for when you add granularity is {time_dimension_name}__{granularity_level}. If you are doing yearly granularity and the time dimension name is ds, you can expect ds__year.
 
-ADD JDBC CONTENT HERE
 
-</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
index 8e83f9dd25c..85b99bfafd7 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
@@ -1,7 +1,7 @@
 ---
-title: "Semantic Layer APIs"
+title: "Semantic Layer API"
 id: sl-api-overview
-description: "Integrate and query using the Semantic Layer APIs."
+description: "Integrate and query using the Semantic Layer API."
 tags: ["semantic-layer, apis"]
 hide_table_of_contents: true
 ---
@@ -12,61 +12,26 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
 <UpgradeSL />
 
-
-With the Semantic Layer APIs, you can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
-
-The full dbt Semantic Layer lets you create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and MetricFlow — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
-
-The dbt Semantic Layer generates a [`semantic_manifest.json` artifact file](/docs/use-dbt-semantic-layer/sl-manifest). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
-
-To query metrics using the dbt Semantic Lay API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
-
-The dbt Semantic Layer provides the following APIs, which you can refer to for more info:
-
-<div className="grid--3-col">
-
-<Card
-    title="JDBC API"
-    body="Use a JDBC driver to query metrics in downstream tools."
-    link="/docs/use-dbt-semantic-layer/jdbc"
-    icon="dbt-bit"/>
-
-<Card
-    title="GraphQL API"
-    body="Use GraphQL to query metrics in downstream tools."
-    link="/docs/use-dbt-semantic-layer/graphql"
-    icon="dbt-bit"/>
-
-<Card
-    title="Discovery API"
-    body="Uses the Discovery API to query metrics in downstream tools using metadata details about your project’s models, sources, and other nodes along with their execution results."
-    link="/docs/dbt-cloud-apis/discovery-api"
-    icon="dbt-bit"/>
-
-</div>
-
-## Authentication
-
-Requests to the dbt Semantic Layer APIs can be authorized through two types of API tokens: 
-
-- [User tokens](/docs/dbt-cloud-apis/user-tokens)  
-- [Service account tokens](/docs/dbt-cloud-apis/service-tokens)
-
-
 </VersionBlock>
 
+The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads.
 
-<VersionBlock firstVersion="1.6">
+The dbt Semantic Layer allows users to define metrics in code & dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that use your product make more efficient and trustworthy decisions with their data.
 
-With the Semantic Layer APIs, you can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
+The dbt Semantic Layer can be used for a variety of tools and applications of data. Here are some common use cases:
 
-The full dbt Semantic Layer lets you create systems for data consumption, automated reporting, easily feed metrics to downstream tools, and more. This can help you connect to a wide variety of data applications across the modern data stack and natively integrate with the dbt Semantic Layer and MetricFlow — from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+* Business intelligence (BI), reporting, and analytics
+* Data quality and monitoring
+* Governance and privacy
+* Data discovery and cataloging
+* Machine learning and data science
 
-The dbt Semantic Layer generates a [`semantic_manifest.json` artifact file](/docs/use-dbt-semantic-layer/sl-manifest). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
 
-To query metrics using the dbt Semantic Lay API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
+You can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
 
-The dbt Semantic Layer provides the following APIs, which you can refer to for more info:
+To query metrics using the dbt Semantic Layer API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
+
+The dbt Semantic Layer provides the following APIs which you can use to query your metrics and build and integration, you can refer to for more info:
 
 <div className="grid--3-col">
 
@@ -92,9 +57,4 @@ The dbt Semantic Layer provides the following APIs, which you can refer to for m
 
 ## Authentication
 
-Requests to the dbt Semantic Layer APIs can be authorized through two types of API tokens: 
-
-- [User tokens](/docs/dbt-cloud-apis/user-tokens)  
-- [Service account tokens](/docs/dbt-cloud-apis/service-tokens)
-
-</VersionBlock>
+dbt Cloud authorizes requests to the dbt Semantic Layer APIs. You need to provide an environment ID, host, and [service account tokens](/docs/dbt-cloud-apis/service-tokens).
diff --git a/website/static/img/sl-architecture.jpg b/website/static/img/sl-architecture.jpg
new file mode 100644
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literal 0
HcmV?d00001


From 519088ea8df2f5d0d1fd6a323fd86c659e0ea81a Mon Sep 17 00:00:00 2001
From: Will Sargent <109557847+will-sargent-dbtlabs@users.noreply.github.com>
Date: Thu, 13 Jul 2023 09:57:58 -0600
Subject: [PATCH 045/703] Update connect-github.md

Adds the "webhooks" permission to match the updated July changes, and reformats the layout to make it more clear that there are 7 permissions required.
---
 website/docs/docs/cloud/git/connect-github.md | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/git/connect-github.md b/website/docs/docs/cloud/git/connect-github.md
index 6113e3ccb42..e79ba16fefa 100644
--- a/website/docs/docs/cloud/git/connect-github.md
+++ b/website/docs/docs/cloud/git/connect-github.md
@@ -40,7 +40,12 @@ To connect your dbt Cloud account to your GitHub account:
 
 5. Assign the dbt Cloud GitHub App the following permissions:
    - Read access to metadata
-   - Read and write access to checks, code, commit statuses, pull requests, and workflows
+   - Read and write access to Checks
+   - Read and write access to Commit statuses
+   - Read and write access to Contents (Code)
+   - Read and write access to Pull requests
+   - Read and write access to Webhooks
+   - Read and write access to Workflows
 
 6. Once you grant access to the app, you will be redirected back to dbt Cloud and shown a linked account success state. You are now personally authenticated. 
 7. Ask your team members to [personally authenticate](/docs/cloud/git/connect-github#personally-authenticate-with-github) by connecting their GitHub profiles.

From b3e1187d231a4a3c0678e3073b3f2d8c892ce547 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 13 Jul 2023 17:32:34 +0100
Subject: [PATCH 046/703] nit

---
 website/docs/docs/build/measures.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index c96761ecb1b..febdb991b03 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -64,7 +64,7 @@ semantic_models:
  - name: transactions
     description: A record for every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
-    default:
+    defaults:
       agg_time_dimensions:
 
 # --- entities ---
@@ -166,7 +166,7 @@ semantic_models:
   - name: subscription_table
     description: A subscription table with one row per date for each active user and their subscription plans. 
     model: ref('your_schema.subscription_table')
-    default:
+    defaults:
       agg_time_dimension: metric_time 
 
     entities:

From c350865a4bae02c96caef4851c2ff2fa8fa4d1a5 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 09:57:14 -0700
Subject: [PATCH 047/703] Update AdapterVersion log message

---
 website/docs/docs/dbt-versions/core-versions.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index 328b6cf4166..d485f8fb1e6 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -96,12 +96,11 @@ That means that patch version numbers will likely differ between `dbt-core` and
 For example, you may find you're using `dbt-core==1.2.3` with `dbt-snowflake==1.2.0`. It is critical that you're using the latest patch available for both core and the adapter (v1.2.x). Use the `dbt --version` command to see which versions you have installed:
 ```
 $ dbt --version
-installed version: 1.2.3
-   latest version: 1.2.3
-
-Up to date!
+Core:
+  - installed: 1.5.3
+  - latest:    1.5.3 - Up to date!
 
 Plugins:
-  - snowflake: 1.2.0 - Up to date!
+  - snowflake: 1.5.2 - Up to date!
 ```
 It's likely that newer patches have become available since then, so it's always important to check and make sure you're up to date!

From f65b840fd80b44b14f994fd239182f72c3dbe16b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 13 Jul 2023 10:09:26 -0700
Subject: [PATCH 048/703] Update
 website/docs/docs/dbt-versions/core-versions.md

---
 website/docs/docs/dbt-versions/core-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index d485f8fb1e6..edfaa4299ca 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -97,7 +97,7 @@ For example, you may find you're using `dbt-core==1.2.3` with `dbt-snowflake==1.
 ```
 $ dbt --version
 Core:
-  - installed: 1.5.3
+  - installed: 1.6.0
   - latest:    1.5.3 - Up to date!
 
 Plugins:

From b6e036853d9d8936a8f242cf8508aa771dad9a6c Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 13 Jul 2023 10:09:50 -0700
Subject: [PATCH 049/703] Update
 website/docs/docs/dbt-versions/core-versions.md

---
 website/docs/docs/dbt-versions/core-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index edfaa4299ca..ceb94763fd6 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -98,7 +98,7 @@ For example, you may find you're using `dbt-core==1.2.3` with `dbt-snowflake==1.
 $ dbt --version
 Core:
   - installed: 1.6.0
-  - latest:    1.5.3 - Up to date!
+  - latest:    1.6.0 - Up to date!
 
 Plugins:
   - snowflake: 1.5.2 - Up to date!

From 4b17b25b3808b55225333aad30ba4514d6a0305f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 13 Jul 2023 10:11:15 -0700
Subject: [PATCH 050/703] Update
 website/docs/docs/dbt-versions/core-versions.md

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 website/docs/docs/dbt-versions/core-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index ceb94763fd6..73b1e99dcbd 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -101,6 +101,6 @@ Core:
   - latest:    1.6.0 - Up to date!
 
 Plugins:
-  - snowflake: 1.5.2 - Up to date!
+  - snowflake: 1.6.0 - Up to date!
 ```
 It's likely that newer patches have become available since then, so it's always important to check and make sure you're up to date!

From e2c201936735402a04f040d31cb4ad24f7ddddf2 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 10:36:53 -0700
Subject: [PATCH 051/703] Added registered version message

---
 website/docs/docs/dbt-versions/core-versions.md | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index 73b1e99dcbd..b73b2bdc04b 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -93,7 +93,7 @@ When you use dbt, you use a combination of `dbt-core` and an adapter plugin spec
 
 That means that patch version numbers will likely differ between `dbt-core` and the adapter plugin(s) you have installed. However, major and minor version numbers should always match.
 
-For example, you may find you're using `dbt-core==1.2.3` with `dbt-snowflake==1.2.0`. It is critical that you're using the latest patch available for both core and the adapter (v1.2.x). Use the `dbt --version` command to see which versions you have installed:
+For example, you may find you're using `dbt-core==1.6.0` with `dbt-snowflake==1.6.0`. It is critical that you're using the latest patch available for both core and the adapter. Use the `dbt --version` command to see which versions you have installed:
 ```
 $ dbt --version
 Core:
@@ -103,4 +103,10 @@ Core:
 Plugins:
   - snowflake: 1.6.0 - Up to date!
 ```
+
+When running `dbt debug`, you can see which version of the registered adapter version that's being invoked. Below is an example of the message in the `logs/dbt.log` file: 
+```
+[0m13:13:48.572182 [info ] [MainThread]: Registered adapter: snowflake=1.6.0
+```
+
 It's likely that newer patches have become available since then, so it's always important to check and make sure you're up to date!

From e722318d00a17a7f0d2ab7e52ee1092c3d444f1c Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 13 Jul 2023 10:39:27 -0700
Subject: [PATCH 052/703] Update
 website/docs/docs/dbt-versions/core-versions.md

---
 website/docs/docs/dbt-versions/core-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index b73b2bdc04b..8edf2993124 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -104,7 +104,7 @@ Plugins:
   - snowflake: 1.6.0 - Up to date!
 ```
 
-When running `dbt debug`, you can see which version of the registered adapter version that's being invoked. Below is an example of the message in the `logs/dbt.log` file: 
+When running `dbt debug`, you can see which version of the registered adapter that's being invoked. Below is an example of the message in the `logs/dbt.log` file: 
 ```
 [0m13:13:48.572182 [info ] [MainThread]: Registered adapter: snowflake=1.6.0
 ```

From 71b1ff26b7231b28fa079d84aefdf01deb5ef376 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 13 Jul 2023 11:05:24 -0700
Subject: [PATCH 053/703] Update
 website/docs/docs/dbt-versions/core-versions.md

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 website/docs/docs/dbt-versions/core-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/core-versions.md b/website/docs/docs/dbt-versions/core-versions.md
index 8edf2993124..bf33e5e6bd5 100644
--- a/website/docs/docs/dbt-versions/core-versions.md
+++ b/website/docs/docs/dbt-versions/core-versions.md
@@ -104,7 +104,7 @@ Plugins:
   - snowflake: 1.6.0 - Up to date!
 ```
 
-When running `dbt debug`, you can see which version of the registered adapter that's being invoked. Below is an example of the message in the `logs/dbt.log` file: 
+You can see which version of the registered adapter that's being invoked in the [logs](/reference/global-configs/logs). Below is an example of the message in the `logs/dbt.log` file: 
 ```
 [0m13:13:48.572182 [info ] [MainThread]: Registered adapter: snowflake=1.6.0
 ```

From 05e380d4e989616cadf20e55cb523b6d4c6abb0b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 11:53:28 -0700
Subject: [PATCH 054/703] Placement of command options

---
 .../guides/migration/versions/02-upgrading-to-v1.5.md | 11 ++++++++++-
 website/docs/reference/project-configs/log-path.md    |  7 ++++++-
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 811b57e6a33..911d9a6b24e 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -56,7 +56,16 @@ models:
     tests: [] # todo! add tests later
     config: ...
 ```
-Some options that could previously be specified before a sub-command can now only be specified afterward. For example, `dbt --profiles-dir . run` isn't valid anymore, and instead, you need to use `dbt run --profiles-dir .`
+Some options that could previously be specified _before_ a sub-command can now only be specified afterward. For example, `dbt --profiles-dir . run` isn't valid anymore, and instead, you need to use `dbt run --profiles-dir .`. The list of affected commands are:
+
+- 
+- 
+
+Also, there are some options that could be previously specified _after_ a sub-command can now only be specified before. For example, `dbt run --log-path foo` isn't valid anymore, and instead, you need to use `dbt --log-path foo run`. The list of affected commands are: 
+- 
+- 
+
+
 
 Finally: The [built-in `generate_alias_name` macro](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/get_custom_name/get_custom_alias.sql) now includes logic to handle versioned models. If your project has reimplemented the `generate_alias_name` macro with custom logic, and you want to start using [model versions](/docs/collaborate/govern/model-versions), you will need to update the logic in your macro. Note that, while this is **not** a prerequisite for upgrading to v1.5—only for using the new feature—we recommmend that you do this during your upgrade, whether you're planning to use model versions tomorrow or far in the future.
 
diff --git a/website/docs/reference/project-configs/log-path.md b/website/docs/reference/project-configs/log-path.md
index daab17c5f10..dc41346cd8a 100644
--- a/website/docs/reference/project-configs/log-path.md
+++ b/website/docs/reference/project-configs/log-path.md
@@ -47,7 +47,7 @@ The precedence order is: CLI flag > env var > `dbt_project.yml`
 </VersionBlock>
 
 ## Examples
-### Write logs to a subdirectory named `dbt_logs` instead of `logs`
+### Specify subdirectory using the project config file
 
 <File name='dbt_project.yml'>
 
@@ -55,4 +55,9 @@ The precedence order is: CLI flag > env var > `dbt_project.yml`
 log-path: dbt_logs
 ```
 
+### Specify subdirectory from the command line 
+
+```bash
+dbt --log-path dbt_logs run
+```
 </File>

From e7e430b39bddcfe1ca0e2e01b0e1dcabb6fe0c12 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 12:20:31 -0700
Subject: [PATCH 055/703] Add VersionBlock

---
 website/docs/reference/project-configs/log-path.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/project-configs/log-path.md b/website/docs/reference/project-configs/log-path.md
index dc41346cd8a..29cad35d120 100644
--- a/website/docs/reference/project-configs/log-path.md
+++ b/website/docs/reference/project-configs/log-path.md
@@ -54,10 +54,14 @@ The precedence order is: CLI flag > env var > `dbt_project.yml`
 ```yml
 log-path: dbt_logs
 ```
+</File>
+
+<VersionBlock firstVersion="1.5">
 
 ### Specify subdirectory from the command line 
 
 ```bash
 dbt --log-path dbt_logs run
 ```
-</File>
+
+</VersionBlock>

From fdc9e43997167a328244d57e951b9bde5c8c6051 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 13 Jul 2023 12:53:30 -0700
Subject: [PATCH 056/703] Update constraints.md

---
 .../resource-properties/constraints.md        | 56 ++++++++++++++++++-
 1 file changed, 55 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index b25893729e5..e52755355c1 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -350,7 +350,61 @@ models:
 
 </File>
 
-Expected DDL to enforce constraints:
+### Column-level constraint on nested column:
+
+<File name='models/nested_column_constraints_example.sql'>
+
+```sql
+{{
+  config(
+    materialized = "table"
+  )
+}}
+
+select
+  'string' as a,
+  struct(
+    1 as id,
+    'name' as name,
+    struct(2 as id, struct('test' as again, '2' as even_more) as another) as double_nested
+  ) as b
+```
+
+</File>
+
+<File name='models/nested_fields.yml'>
+
+```yml
+version: 2
+
+models:
+  - name: double_nested_fields
+    config:
+      contract: {enforced: true}
+      materialized: table
+    columns:
+      - name: a
+        data_type: string
+      - name: b.id
+        data_type: integer
+        constraints:
+          - type: not_null
+      - name: b.name
+        description: test description
+        data_type: string
+      - name: b.double_nested.id
+        data_type: integer
+      - name: b.double_nested.another.again
+        data_type: string
+      - name: b.double_nested.another.even_more
+        data_type: integer
+        constraints: 
+          - type: not_null
+```
+
+</File>
+### Expected DDL to enforce constraints:
+
 <File name='target/run/.../constraints_example.sql'>
 
 ```sql

From ec35c737be4632e0d449637e6a8f3ebed81d73b1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 13 Jul 2023 21:02:00 +0100
Subject: [PATCH 057/703] updates

---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 109 +++++++-----------
 .../use-dbt-semantic-layer/quickstart-sl.md   |   2 +-
 .../docs/use-dbt-semantic-layer/setup-sl.md   |   3 +-
 website/snippets/_new-sl-changes.md           |   2 +-
 4 files changed, 44 insertions(+), 72 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 674656a39c1..64027db9bbe 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -11,47 +11,60 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-The dbt Semantic Layer, powered by MetricFlow, allows data teams to centrally define critical business metrics like `revenue`, in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and other data applications. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice. It automatically handles joins across your data.
 
-The result? You have less duplicate coding for data teams and more consistency for data consumers. 
-
-The dbt Semantic Layer has the following major components:
-
-- Use [MetricFlow](/docs/build/build-metrics-intro) to define and test your semantic models and metrics in version-controlled dbt project code 
-- Use [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) to explore and analyze these metrics in downstream tools
+The dbt Semantic Layer, powered by MetricFlow, simplifies the process of defining and using critical business metrics, like like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
 
-### What makes the dbt Semantic Layer different?
-
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
+By leveraging the dbt Semantic Layer, data teams achieve reduced code duplication and increased consistency in business metrics across different applications. Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 
-To read more about why you need a universal Semantic Layer, see this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
+To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
+The dbt Semantic Layer has the following major components and you can refer to [product architecture](#product-architecture) for more details:
 
-## Prerequisites
+- [MetricFlow](/docs/build/build-metrics-intro) allows you to define and test your semantic models and metrics in version-controlled dbt project code.
+- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) enables you to explore and analyze these metrics in downstream tools.
 
-To use the dbt Semantic Layer, you’ll need to meet the following:
+## Explore the dbt Semantic Layer
 
-import SetUp from '/snippets/_v2-sl-prerequisites.md';
+The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](#public-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
 
-<SetUp />
+<div className="grid--2-col">
 
-<Snippet src="sl-considerations-banner" />
+<Card
+    title="Get started with MetricFlow and the dbt Semantic Layer"
+    body="Build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
+    link="/docs/use-dbt-semantic-layer/quickstart-sl"
+    icon="dbt-bit"/>
 
-## Public Preview
+<Card
+    title="Set up the dbt Semantic Layer"
+    body="Set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
+    link="/docs/use-dbt-semantic-layer/setup-sl"
+    icon="dbt-bit"/>
 
-## Public Preview
+<Card
+    title="Available integrations"
+    body="Review a wide range of partners you can integrate and query with the dbt Semantic Layer."
+    link="/docs/use-dbt-semantic-layer/avail-sl-integrations"
+    icon="dbt-bit"/>
+    
+<Card
+    title="Semantic Layer API"
+    body="Use the Semantic Layer APIs to query metrics in downstream tools."
+    link="/docs/use-dbt-semantic-layer/sl-api-overview"
+    icon="dbt-bit"/>
 
-The dbt Semantic Layer is currently available as a Public Beta, which means:
+</div>
 
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during [Public Preview](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture).
 
-- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
+## Public beta
 
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-    
-&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+The dbt Semantic Layer is currently available as a public beta, which means:
 
-&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
+- **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during public beta. 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Semantic Layer and Discovery APIs. 
+- **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+- **When?**  Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+- **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
 
 ## Product architecture 
 
@@ -60,54 +73,12 @@ The dbt Semantic Layer product architecture includes four primary components:
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
 | **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **[MetricFlow Server]**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
-<Lightbox src="/img/sl-architecture.jpg" width="70%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. dbt Cloud helps you transform data and define metrics. Query metrics using integration tools for consistent organization-wide metrics." />
-
-dbt Semantic Layer integrations will:
-
-- Leverage the Semantic Layer APIs to fetch a list of objects and their attributes, like metrics
-- Generate a dbt-SQL statement
-- Then query the SQL proxy to evaluate the results of this statement
-
-
-## Manage metrics
-
-:::tip 
-New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
-:::
-
-If you're not sure whether to define a metric in dbt or not, ask yourself the following: 
-
-> *Is this something our teams consistently need to report on?*  
-
-An important business metric should be:
-
-- Well-defined (the definition is agreed upon throughout the entire organization)
-- Time-bound (able to be compared across time)
-
-A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
-
-- ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
-- ❌ `1-off experimental metric`
-
-
-### Build metrics
+<Lightbox src="/img/sl-architecture.jpg" width="85%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. dbt Cloud helps you transform data and define metrics. Query metrics using integration tools for consistent organization-wide metrics." />
 
-Use [MetricFlow](/docs/build/build-metrics-intro) in dbt to centrally build and define your metrics. MetricFlow is a key component of the dbt Semantic Layer, handles SQL query construction, and defines the specification for dbt semantic models and metrics.
-
-With MetricFlow, you can use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
-
-You can define your metrics in `.yml` files nested under a semantic model key to building your own metrics in your dbt project.
-
-To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a dbt Cloud Team or Enterprise account.
-
-To begin building and defining metrics, refer to the detailed [MetricFlow](/docs/build/build-metrics-intro) docs for more info. 
-
-**Design metrics**
-To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.  
 
 ## Related questions
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 9c5411ad3c3..3738a7c48b4 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -1,7 +1,7 @@
 ---
 title: "Get started with the dbt Semantic Layer"
 id: quickstart-sl
-description: "Go through this get started guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
+description: "Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
 sidebar_label: "Getting started"
 ---
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index eaf7a611304..53e3e2e31c7 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -27,7 +27,7 @@ import SetUp from '/snippets/_v2-sl-prerequisites.md';
 
 ## Set up dbt Semantic Layer
 
-You can set up the dbt Semantic Layer in dbt Cloud at the environment and  project level by following these steps:
+You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. To configure the new Semantic Layer, you must have a successful run in your new environment.
 
 1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
 2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
@@ -39,6 +39,7 @@ You can set up the dbt Semantic Layer in dbt Cloud at the environment and  proje
 7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
 8. You’ve configured the semantic layer 🎉! You can now define your [semantic models and metrics](/docs/build/build-metrics-intro) in your project.
 
+
 :::tip
 If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 :::
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index 1595078cd8a..122154070c1 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -1,7 +1,7 @@
 
 :::tip Introducing the new dbt Semantic Layer 🎉
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. 
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/use-dbt-semantic-layer/dbt-sl#public-beta). 
 
 **What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
 

From 551913b4b2a6df7e58423f3bdc3a2248d6265702 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 13 Jul 2023 13:06:32 -0700
Subject: [PATCH 058/703] Update
 website/docs/reference/resource-properties/constraints.md

---
 website/docs/reference/resource-properties/constraints.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index e52755355c1..de5b3ef7284 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -403,6 +403,7 @@ models:
 ```
 
 </File>
+
 ### Expected DDL to enforce constraints:
 
 <File name='target/run/.../constraints_example.sql'>

From 8fc06d32a262929bac994a71896beb516e0d305d Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 13 Jul 2023 22:26:35 +0100
Subject: [PATCH 059/703] updates

---
 website/docs/docs/build/sl-getting-started.md |  1 +
 .../avail-sl-integrations.md                  | 11 ++--
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 13 ++++-
 .../docs/docs/use-dbt-semantic-layer/jdbc.md  | 13 +++--
 .../use-dbt-semantic-layer/quickstart-sl.md   | 51 +++++++++----------
 .../docs/use-dbt-semantic-layer/setup-sl.md   | 30 +++--------
 .../use-dbt-semantic-layer/sl-api-overview.md | 11 ++--
 website/snippets/_new-sl-setup.md             | 20 ++++++++
 website/snippets/_upgrade-new-sl.md           |  2 +-
 9 files changed, 80 insertions(+), 72 deletions(-)
 create mode 100644 website/snippets/_new-sl-setup.md

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index ff0e6006921..d1275352a21 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -126,6 +126,7 @@ ANY COMMON TROUBLESHOOTING QUESTIONS?-->
 
 ## Related docs
 
+- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [The dbt Semantic Layer: what’s next](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) blog post
 - [About MetricFlow](/docs/build/about-metricflow)
 - [Semantic models](/docs/build/semantic-models)
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 977f05bac2d..bd41380ac54 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -9,9 +9,9 @@ sidebar_label: "Available integrations"
 
 import NewSLChanges from '/snippets/_new-sl-changes.md';
 
-<NewSLChanges />>
+<NewSLChanges />
 
-Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (Discovery, JDBC, and GraphQL API). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 A wide variety of data applications across the modern data stack natively integrate with the dbt Semantic Layer, powered by MetricFlow &mdash; from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
 
@@ -24,12 +24,12 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 
 ## Related docs
 
-- [dbt Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) to learn how to integrate with the Discovery, JDBC, and GraphQL API to query your metrics in downstream tools.
+- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) to learn how to integrate with the JDBC, GraphQL, and Discovery APIs to query your metrics in downstream tools.
 
 </VersionBlock>
 
@@ -45,8 +45,7 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
-
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Related docs
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 64027db9bbe..e9dd131f696 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -56,7 +56,9 @@ The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and
 </div>
 
 
-## Public beta
+## Public beta 
+
+MOVING THIS TO RELEASE NOTE SECTION BECAUSE I DONT THINK THIS BELONGS HERE. ANYTHING THAT SAYS PUBLIC BETA CAN LINK TO RN
 
 The dbt Semantic Layer is currently available as a public beta, which means:
 
@@ -77,7 +79,7 @@ The dbt Semantic Layer product architecture includes four primary components:
 | **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
-<Lightbox src="/img/sl-architecture.jpg" width="85%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. dbt Cloud helps you transform data and define metrics. Query metrics using integration tools for consistent organization-wide metrics." />
+<Lightbox src="/img/sl-architecture.jpg" width="85%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
 
 
 ## Related questions
@@ -229,6 +231,13 @@ Review our helpful metrics video below, which explains what metrics are, why the
 <LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
 
 ## Related questions
+
+<details>
+  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
+  <div>
+    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
+  </div>
+</details>
     
 <details>
   <summary>How are you storing my data?</summary>
diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
index d64b43bc977..244363861c2 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -17,11 +17,14 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 </VersionBlock>
 
-The dbt Semantic Layer JDBC driver allows users to query their metrics and dimensions using the JDBC protocol. It also exposes standard metadata like any JDBC driver.
-* The Semantic Layer JDBC API uses the ArrowFlight SQL protocol, and we utilize the open-source JDBC driver.
-* The driver can be downloaded from [Maven](https://www.google.com/url?q=https://search.maven.org/remotecontent?filepath%3Dorg/apache/arrow/flight-sql-jdbc-driver/12.0.0/flight-sql-jdbc-driver-12.0.0.jar&sa=D&source=docs&ust=1689208856858323&usg=AOvVaw2JfXh7rcmsxYkkdVNWo8PI)
-* The dbt Semantic Layer supports ArrowFlight SQL driver version 12.0.0 and higher.
-* You can embed this driver into your application stack however you wish, and we have an example project for your reference [here](https://www.google.com/url?q=https://github.com/dbt-labs/example-semantic-layer-clients&sa=D&source=docs&ust=1689208936518700&usg=AOvVaw141BhBSfphJ10M7b5ZhYs-)
+The dbt Semantic Layer Java Database Connectivity (JDBC) driver enables users to query metrics and dimensions using the JDBC protocol, while also providing standard metadata functionality. 
+
+A JDBC driver is a software component enabling a Java application to interact with a data platform. Here's some more information about the JDBC:
+
+- The Semantic Layer JDBC API utilizes the open-source JDBC driver with ArrowFlight SQL protocol.
+- You can download the JDBC driver from [Maven](https://search.maven.org/remotecontent?filepath=org/apache/arrow/flight-sql-jdbc-driver/12.0.0/flight-sql-jdbc-driver-12.0.0.jar). 
+- The dbt Semantic Layer supports ArrowFlight SQL driver version 12.0.0 and higher. 
+- You can embed the driver into your application stack as needed, and you can use dbt Labs' [example project](https://github.com/dbt-labs/example-semantic-layer-clients) for reference.
 
 ## Installation
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 3738a7c48b4..6a3ba6912e6 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -11,30 +11,31 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-This getting started page recommends a workflow to help you get started creating your first metrics. Here are the following steps you'll take:
+The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies defining and using critical business metrics. It centralizes metric definitions, eliminates duplicate coding, and ensures consistent self-service access to metrics in downstream tools.  MetricFlow helps define and manage metric logic, offering efficient retrieval of metric datasets for data consumers.
 
-- [Create a semantic model](#create-a-semantic-model)
-- [Create your metrics](#create-your-metrics)
-- [Run your production job](#run-your-production-job)
-- [Set up dbt Semantic Layer](#setup)
+Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
+
+- [Create a semantic model](#create-a-semantic-model) with MetricFlow
+- [Create your metrics](#create-your-metrics) with MetricFlow
+- [Run your production job](#run-your-production-job) in dbt Cloud
+- [Set up dbt Semantic Layer](#setup) in dbt Cloud
 - [Connect to the Semantic Layer APIs](#connect-to-apis)
 - [Query your metrics using partner integrations](#query-metrics)
 
+
 ## Prerequisites
 
 import SetUp from '/snippets/_v2-sl-prerequisites.md';
 
 <SetUp />
 
-<Snippet src="sl-considerations-banner" />
-
 :::tip 
 New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
 :::
 
 ## Create a semantic model
 
-In MetricFlow, which powers the dbt Semantic Layer, there are two main objects: 
+Before you begin, we recommend you learn about MetricFlow and its key concepts. There are two main objects: 
 
 - [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
 - [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
@@ -110,7 +111,7 @@ metrics:
 2. Run `mf validate-configs` to validate the changes before committing them.
 3. Commit and merge the code changes that contain the metric definitions.
 
-Review the [MetricFlow](/docs/build/build-metrics-intro) documents to further build your own metrics. 
+If you'd lke to build out your metrics, read the [MetricFlow](/docs/build/build-metrics-intro) docs for more info.
 
 ## Run your production job
 
@@ -120,32 +121,31 @@ Once you’ve defined metrics in your dbt project, you can perform a job run in
 2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.
 3. Your metric should appear as a red node in the dbt Cloud IDE and dbt directed acyclic graphs (DAG).
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/metrics_red_nodes.png" title="DAG with metrics appearing as a red node" />
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/metrics_red_nodes.png" width="85%" title="DAG with metrics appearing as a red node" />
 
 
-**What’s happening internally?**
+<details>
 
-- Merging the code into your main branch allows dbt Cloud to pull those changes and builds the definition in the manifest produced by the run.
-- Re-running the job in the deployment environment helps materialize the models, which the metrics depend on, in the data platform. It also makes sure that the manifest is up to date.
-- Your dbt Discovery API pulls in the most recent manifest and allows your integration information to extract metadata from it.
+<summary>What’s happening internally?</summary>
+- Merging the code into your main branch allows dbt Cloud to pull those changes and builds the definition in the manifest produced by the run. <br />
+- Re-running the job in the deployment environment helps materialize the models, which the metrics depend on, in the data platform. It also makes sure that the manifest is up to date.<br />
+- The Semantic Layer APIs pulls in the most recent manifest and allows your integration information to extract metadata from it.
+</details>
 
 ## Set up dbt Semantic Layer
-    
-<Snippet src="sl-set-up-steps" />
 
-## Connect to the Semantic Layer APIs
+import SlSetUp from '/snippets/_new-sl-setup.md';  
 
-add content here
+<SlSetUp/>
 
-## Query your metrics using partner integrations
 
-add content
+## Connect to the APIs
 
-:::info 📌 
+add content here
 
-**Note:** You will need access to dbt Cloud and the dbt Semantic Layer from your integrated partner tool of choice. 
+## Query your metrics 
 
-:::
+add content
 
 ## FAQs
 
@@ -175,12 +175,11 @@ During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (De
    
 ## Next steps
 
-Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer: 
+Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer.
 
 - [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
 - [About MetricFlow](/docs/build/about-metricflow)
-- [Semantic models](/docs/build/semantic-models)
-- [Metrics](/docs/build/metrics-overview)
+- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
 
 </VersionBlock>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 53e3e2e31c7..68062d80245 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -19,30 +19,13 @@ Before you set up the dbt Semantic Layer, make sure you meet the following:
 
 import SetUp from '/snippets/_v2-sl-prerequisites.md';
 
-<SetUp />
-
-
-<Snippet src="sl-considerations-banner" />
-
+<SetUp/>
 
 ## Set up dbt Semantic Layer
 
-You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. To configure the new Semantic Layer, you must have a successful run in your new environment.
-
-1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
-2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
-3. In the **Project Details** page, navigate to **Configure Semantic Layer**.
-4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
-    * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
-5. Select the deployment environment you want for the Semantic Layer
-6. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
-7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
-8. You’ve configured the semantic layer 🎉! You can now define your [semantic models and metrics](/docs/build/build-metrics-intro) in your project.
+import SlSetUp from '/snippets/_new-sl-setup.md';  
 
-
-:::tip
-If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
-:::
+<SlSetUp/>
 
 <!--
 1. Create a new environment in dbt Cloud by selecting **Deploy** and then **Environments**.
@@ -74,14 +57,13 @@ Before you set up the dbt Semantic Layer, make sure you meet the following:
 
 <Snippet src="sl-prerequisites" />
 
-
-<Snippet src="sl-considerations-banner" />
-
-
 ## Set up dbt Semantic Layer
 
+:::tip
 If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
+:::
+
 Before you continue with the following steps, you **must** have a multi-tenant dbt Cloud account hosted in North America. 
  * Team and Enterprise accounts can set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated partner tool to import metric definitions. 
  * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
index 85b99bfafd7..afe872ec087 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
@@ -16,9 +16,9 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
 The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads.
 
-The dbt Semantic Layer allows users to define metrics in code & dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that use your product make more efficient and trustworthy decisions with their data.
+The dbt Semantic Layer allows users to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that uses your product make more efficient and trustworthy decisions with their data. It also helps avoid duplicative coding, optimizes development workflow, ensures data governance, and guarantee consistency for data consumers.  
 
-The dbt Semantic Layer can be used for a variety of tools and applications of data. Here are some common use cases:
+You can use the dbt Semantic Layer for a variety of tools and applications of data. Here are some common use cases:
 
 * Business intelligence (BI), reporting, and analytics
 * Data quality and monitoring
@@ -26,12 +26,7 @@ The dbt Semantic Layer can be used for a variety of tools and applications of da
 * Data discovery and cataloging
 * Machine learning and data science
 
-
-You can seamlessly query the metrics you built with [MetricFlow](/docs/build/about-metricflow) to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.  
-
-To query metrics using the dbt Semantic Layer API, accounts must be on a [Team or Enterprise plans](https://www.getdbt.com/pricing/). 
-
-The dbt Semantic Layer provides the following APIs which you can use to query your metrics and build and integration, you can refer to for more info:
+The dbt Semantic Layer APIs are available for accounts on [Team or Enterprise plans](https://www.getdbt.com/pricing/), allowing them to query metrics and build integrations.
 
 <div className="grid--3-col">
 
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
new file mode 100644
index 00000000000..804b0d4b529
--- /dev/null
+++ b/website/snippets/_new-sl-setup.md
@@ -0,0 +1,20 @@
+You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. To configure the new Semantic Layer, you must have a successful run in your new environment.
+
+:::tip
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+:::
+
+1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
+2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
+3. In the **Project Details** page, navigate to **Configure Semantic Layer**.
+4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
+    * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
+5. Select the deployment environment you want for the Semantic Layer
+6. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
+8. Great job, you've configured the Semantic Layer 🎉! 
+
+
+
+
+
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
index b30f8318068..b22b690d046 100644
--- a/website/snippets/_upgrade-new-sl.md
+++ b/website/snippets/_upgrade-new-sl.md
@@ -1,4 +1,4 @@
-:::info Upgrade to access the new dbt Semantic Layer
+:::tip Upgrade to access the new dbt Semantic Layer
 
 The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
 

From af1bad2253e11181b8c21f4e5f2c84a9b93eb659 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 14:52:01 -0700
Subject: [PATCH 060/703] New empty mode for indirect selection

---
 .../node-selection/test-selection-examples.md | 82 ++++++++++++++++++-
 .../node-selection/yaml-selectors.md          | 54 +++++++++++-
 2 files changed, 130 insertions(+), 6 deletions(-)

diff --git a/website/docs/reference/node-selection/test-selection-examples.md b/website/docs/reference/node-selection/test-selection-examples.md
index 6a884b2035d..7e79754ed31 100644
--- a/website/docs/reference/node-selection/test-selection-examples.md
+++ b/website/docs/reference/node-selection/test-selection-examples.md
@@ -58,7 +58,7 @@ The "cautious" mode can be useful in environments when you're only building a su
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.4">
+<VersionBlock firstVersion="1.4" lastVersion="1.4">
 
 There are three modes to configure the behavior when performing indirect selection (with `eager` as the default):
 
@@ -72,6 +72,21 @@ The "buildable" and "cautious" modes can be useful in environments when you're o
 
 </VersionBlock>
 
+<VersionBlock firstVersion="1.5" >
+
+There are three modes to configure the behavior when performing indirect selection (with `eager` as the default):
+
+1. `eager` (default) - include ANY test that references the selected nodes
+1. `cautious` - restrict to tests that ONLY refer to selected nodes
+1. `buildable` -  restrict to tests that ONLY refer to selected nodes (or their ancestors)
+1. `empty` - restrict to tests that are only for the selected node and ignore all tests from the attached nodes 
+
+Note that test exclusion is always greedy: if ANY parent is explicitly excluded, the test will be excluded as well.
+
+The "buildable", "cautious", and "empty" modes can be useful in environments when you're only building a subset of your DAG, and you want to avoid test failures in "eager" mode caused by unbuilt resources. (Another way to achieve this is with [deferral](/reference/node-selection/defer)).
+
+</VersionBlock>
+
 <!--tabs for eager mode, cautious mode, and buildable mode -->
 
 <VersionBlock lastVersion="1.3">
@@ -109,7 +124,57 @@ $ dbt build --select orders --indirect-selection=cautious
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.4">
+<VersionBlock firstVersion="1.4" lastVersion="1.4">
+
+<Tabs queryString="indirect-selection-mode">
+<TabItem value="eager" label="Eager mode (default)">
+
+By default, a test will run when ANY parent is selected; we call this "eager" indirect selection. In this example, that would include any test that references orders, even if it references other models as well.
+
+In this mode, any test that depends on unbuilt resources will raise an error.
+
+```shell
+$ dbt test --select orders
+$ dbt build --select orders
+```
+
+</TabItem>
+
+<TabItem value="cautious" label="Cautious mode">
+
+It is possible to prevent tests from running if one or more of its parents is unselected (and therefore unbuilt); we call this "cautious" indirect selection.
+
+It will only include tests whose references are each within the selected nodes.
+
+Put another way, it will prevent tests from running if one or more of its parents is unselected.
+
+```shell
+$ dbt test --select orders --indirect-selection=cautious
+$ dbt build --select orders --indirect-selection=cautious
+```
+
+</TabItem>
+
+<TabItem value="buildable" label="Buildable mode">
+
+This mode is similarly conservative like "cautious", but is slightly more inclusive.
+
+It will only include tests whose references are each within the selected nodes (or their ancestors).
+
+This is useful in the same scenarios as "cautious", but also includes when a test depends on a model **and** a direct ancestor of that model (like confirming an aggregation has the same totals as its input).
+
+```shell
+$ dbt test --select orders --indirect-selection=buildable
+$ dbt build --select orders --indirect-selection=buildable
+```
+
+</TabItem>
+
+</Tabs>
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.5">
 
 <Tabs queryString="indirect-selection-mode">
 <TabItem value="eager" label="Eager mode (default)">
@@ -155,11 +220,22 @@ $ dbt build --select orders --indirect-selection=buildable
 
 </TabItem>
 
+<TabItem value="empty" label="Empty mode">
+
+This mode will only include tests whose references are each within the selected nodes and will ignore all tests from attached nodes.
+
+```shell
+$ dbt test --select orders --indirect-selection=empty
+$ dbt build --select orders --indirect-selection=empty
+```
+
+</TabItem>
+
 </Tabs>
 
 </VersionBlock>
 
-<!--End of tabs for eager mode, cautious mode, and buildable mode -->
+<!--End of tabs for eager mode, cautious mode, buildable mode, and empty mode -->
 
 ### Syntax examples
 
diff --git a/website/docs/reference/node-selection/yaml-selectors.md b/website/docs/reference/node-selection/yaml-selectors.md
index 95c3c791b53..78342e32779 100644
--- a/website/docs/reference/node-selection/yaml-selectors.md
+++ b/website/docs/reference/node-selection/yaml-selectors.md
@@ -78,7 +78,7 @@ definition:
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.4">
+<VersionBlock firstVersion="1.4" lastVersion="1.4">
 
 ```yml
 definition:
@@ -95,7 +95,29 @@ definition:
 
   childrens_parents: true | false     # @ operator
 
-  indirect_selection: eager | cautious | buildable  # include all tests selected indirectly? eager by default
+  indirect_selection: eager | cautious | buildable # include all tests selected indirectly? eager by default
+```
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.5">
+
+```yml
+definition:
+  method: tag
+  value: nightly
+
+  # Optional keywords map to the `+` and `@` graph operators:
+
+  children: true | false
+  parents: true | false
+
+  children_depth: 1    # if children: true, degrees to include
+  parents_depth: 1     # if parents: true, degrees to include
+
+  childrens_parents: true | false     # @ operator
+
+  indirect_selection: eager | cautious | buildable | empty # include all tests selected indirectly? eager by default
 ```
 
 </VersionBlock>
@@ -168,7 +190,7 @@ If provided, a YAML selector's `indirect_selection` value will take precedence o
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.4">
+<VersionBlock firstVersion="1.4" lastVersion="1.4">
 
 As a general rule, dbt will indirectly select _all_ tests if they touch _any_ resource that you're selecting directly. We call this "eager" indirect selection. You can optionally switch the indirect selection mode to "cautious" or "buildable" by setting `indirect_selection` for a specific criterion:
 
@@ -191,6 +213,32 @@ If provided, a YAML selector's `indirect_selection` value will take precedence o
 
 </VersionBlock>
 
+<VersionBlock firstVersion="1.5">
+
+As a general rule, dbt will indirectly select _all_ tests if they touch _any_ resource that you're selecting directly. We call this "eager" indirect selection. You can optionally switch the indirect selection mode to "cautious", "buildable", or "empty" by setting `indirect_selection` for a specific criterion:
+
+```yml
+- union:
+    - method: fqn
+      value: model_a
+      indirect_selection: eager  # default: will include all tests that touch model_a
+    - method: fqn
+      value: model_b
+      indirect_selection: cautious  # will not include tests touching model_b
+                        # if they have other unselected parents
+    - method: fqn
+      value: model_c
+      indirect_selection: buildable  # will not include tests touching model_c
+                        # if they have other unselected parents (unless they have an ancestor that is selected)
+    - method: fqn
+      value: model_d
+      indirect_selection: empty  # will include tests for only the selected node and ignore all tests attached to model_d
+```
+
+If provided, a YAML selector's `indirect_selection` value will take precedence over the CLI flag `--indirect-selection`. Because `indirect_selection` is defined separately for _each_ selection criterion, it's possible to mix eager/cautious/buildable/empty modes within the same definition, to achieve the exact behavior that you need. Remember that you can always test out your critiera with `dbt ls --selector`.
+
+</VersionBlock>
+
 See [test selection examples](/reference/node-selection/test-selection-examples) for more details about indirect selection.
 
 ## Example

From f2c0b626db4e009065620fdb8126676398045bce Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Thu, 13 Jul 2023 14:52:14 -0700
Subject: [PATCH 061/703] add initial clone updates

---
 website/dbt-versions.js                  | 6 +++++-
 website/docs/reference/commands/clone.md | 7 +++++++
 website/docs/reference/dbt-commands.md   | 2 ++
 website/sidebars.js                      | 1 +
 4 files changed, 15 insertions(+), 1 deletion(-)
 create mode 100644 website/docs/reference/commands/clone.md

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 52b68d6c084..4f12f82f958 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -31,6 +31,10 @@ exports.versions = [
 ]
 
 exports.versionedPages = [
+  {
+    "page": "reference/commands/clone",
+    "firstVersion": "1.6",
+  },
   {
     "page": "reference/resource-properties/deprecation_date", 
     "firstVersion": "1.6",
@@ -47,7 +51,7 @@ exports.versionedPages = [
     "page": "docs/collaborate/govern/model-contracts",
     "firstVersion": "1.5",
   },
-    {
+  {
     "page": "reference/commands/show",
     "firstVersion": "1.5",
   },
diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
new file mode 100644
index 00000000000..657a74c54f8
--- /dev/null
+++ b/website/docs/reference/commands/clone.md
@@ -0,0 +1,7 @@
+---
+title: "About dbt clone command"
+sidebar_label: "clone"
+id: "clone"
+---
+
+blah blah blah
\ No newline at end of file
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 116618e29e5..edeb58711eb 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -20,6 +20,7 @@ Select the tabs that are relevant to the your development workflow. For example,
 Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) and use the `dbt` prefix. For example, to run the `test` command, type `dbt test`.
 
 - [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
+- [clone](/reference/commands/clone): clone selected models from specified state (requires dbt 1.6 or higher)
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project
@@ -39,6 +40,7 @@ Use the following dbt commands in the [CLI](/docs/core/about-the-cli) and use th
 
 - [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
 - [clean](/reference/commands/clean): deletes artifacts present in the dbt project
+- [clone](/reference/commands/clone): clone selected models from specified state (requires dbt 1.6 or higher)
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [debug](/reference/commands/debug): debugs dbt connections and projects
 - [deps](/reference/commands/deps): downloads dependencies for a project
diff --git a/website/sidebars.js b/website/sidebars.js
index 3198d95e0f3..4aaa45ae335 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -734,6 +734,7 @@ const sidebarSettings = {
           items: [
             "reference/commands/build",
             "reference/commands/clean",
+            "reference/commands/clone",
             "reference/commands/cmd-docs",
             "reference/commands/compile",
             "reference/commands/debug",

From b62768cb9e40bf7503d1752108bf51149cc7937b Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Fri, 14 Jul 2023 00:09:29 +0200
Subject: [PATCH 062/703] cross-project `ref` + model `access` +
 `dependencies.yml` (#3577)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

resolves #3550
resolves #3632
resolves #3574

## What are you changing in this pull request and why?

Create a new page for "cross-project `ref` under `collaborate >
govern`. I've decided to call the page "Project dependencies," and use
it as an opportunity to highlight the differences between project +
package dependencies.

I started tackling two closely related issues, since we should be
thematically consistent across all of them:
- `enforce_access` for packages <> model access
- `packages` can be configured in a file named `dependencies.yml`

## Previews

- [Project
dependencies](https://deploy-preview-3577--docs-getdbt-com.netlify.app/docs/collaborate/govern/project-dependencies)
- [Packages: How do I add a package to my
project?](https://deploy-preview-3577--docs-getdbt-com.netlify.app/docs/build/packages#how-do-i-add-a-package-to-my-project)
- [Model access: How do I ref a model from another
project?](https://deploy-preview-3577--docs-getdbt-com.netlify.app/docs/collaborate/govern/model-access#how-do-i-ref-a-model-from-another-project)
- [dbt_project.yml:
restrict-access](https://deploy-preview-3577--docs-getdbt-com.netlify.app/reference/dbt_project.yml)
- [Upgrading to
v1.6](https://deploy-preview-3577--docs-getdbt-com.netlify.app/guides/migration/versions/upgrading-to-v1.6)

## Checklist
- [x] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [x] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [x] Add page to `website/sidebars.js`
- [x] Provide a unique filename for the new page

---------

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/dbt-versions.js                       |  4 +
 website/docs/docs/build/packages.md           |  4 +-
 .../docs/collaborate/govern/model-access.md   | 42 ++++++--
 .../govern/project-dependencies.md            | 96 +++++++++++++++++++
 .../versions/01-upgrading-to-v1.6.md          |  2 +
 website/docs/reference/dbt_project.yml.md     |  1 +
 website/sidebars.js                           |  1 +
 7 files changed, 141 insertions(+), 9 deletions(-)
 create mode 100644 website/docs/docs/collaborate/govern/project-dependencies.md

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 52b68d6c084..44008bf98bf 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -31,6 +31,10 @@ exports.versions = [
 ]
 
 exports.versionedPages = [
+  {
+    "page": "docs/collaborate/govern/project-dependencies",
+    "firstVersion": "1.6",
+  },
   {
     "page": "reference/resource-properties/deprecation_date", 
     "firstVersion": "1.6",
diff --git a/website/docs/docs/build/packages.md b/website/docs/docs/build/packages.md
index b34ef23aea1..d4cebc7a6f0 100644
--- a/website/docs/docs/build/packages.md
+++ b/website/docs/docs/build/packages.md
@@ -30,10 +30,10 @@ Defining and installing dbt packages is different from [defining and installing
 :::
 
 ## How do I add a package to my project?
-1. Add a `packages.yml` file to your dbt project. This should be at the same level as your `dbt_project.yml` file.
+1. Add a file named <VersionBlock firstVersion="1.6"> `dependencies.yml` or </VersionBlock> `packages.yml` to your dbt project. This should be at the same level as your `dbt_project.yml` file.
 2. Specify the package(s) you wish to add using one of the supported syntaxes, for example:
 
-<File name='packages.yml'>
+<File>
 
 ```yaml
 packages:
diff --git a/website/docs/docs/collaborate/govern/model-access.md b/website/docs/docs/collaborate/govern/model-access.md
index 95928110862..970f25ef87f 100644
--- a/website/docs/docs/collaborate/govern/model-access.md
+++ b/website/docs/docs/collaborate/govern/model-access.md
@@ -163,16 +163,44 @@ Of course, dbt can facilitate this by means of [the `grants` config](/reference/
 
 As we continue to develop multi-project collaboration, `access: public` will mean that other teams are allowed to start taking a dependency on that model. This assumes that they've requested, and you've granted them access, to select from the underlying dataset.
 
-### What about referencing models from a package?
+### How do I ref a model from another project?
 
-For historical reasons, it is possible to `ref` a protected model from another project, _if that protected model is installed as a package_. This is useful for packages containing models for a common data source; you can install the package as source code, and run the models as if they were your own.
+<VersionBlock lastVersion="1.5">
+
+In dbt Core v1.5 (and earlier versions), the only way to reference a model from another project is by installing that project as a package, including its full source code. It is not possible to restrict references across projects based on model `access`.
+
+For more control over per-model access across projects, select v1.6 (or newer) from the version dropdown.
+
+</VersionBlock>
+
+<VersionBlock firstVersion="1.6">
+
+You can `ref` a model from another project in two ways:
+1. [Project dependency](/docs/collaborate/govern/project-dependencies): In dbt Cloud Enterprise, you can use project dependencies to `ref`  a model. dbt Cloud uses a behind-the-scenes metadata service to resolve the reference, enabling efficient collaboration across teams and at scale.
+2. ["Package" dependency](/docs/build/packages): Another way to `ref` a model from another project is to treat the other project as a package dependency. This requires installing the other project as a package, including its full source code, as well as its upstream dependencies.
+
+### How do I restrict access to models defined in a package?
+
+Source code installed from a package becomes part of your runtime environment. You can call macros and run models as if they were macros and models that you had defined in your own project.
+
+For this reason, model access restrictions are "off" by default for models defined in packages. You can reference models from that package regardless of their `access` modifier.
+
+The project being installed as a package can optionally restrict external `ref` access to just its public models. The package maintainer does this by setting a `restrict-access` config to `True` in `dbt_project.yml`.
+
+By default, the value of this config is `False`. This means that:
+- Models in the package with `access: protected` may be referenced by models in the root project, as if they were defined in the same project
+- Models in the package with `access: private` may be referenced by models in the root project, so long as they also have the same `group` config
+
+When `restrict-access: True`:
+- Any `ref` from outside the package to a protected or private model in that package will fail.
+- Only models with `access: public` can be referenced outside the package.
+
+<File name="dbt_project.yml">
 
-dbt Core v1.6 will introduce a new kind of `project` dependency, distinct from a `package` dependency, defined in `dependencies.yml`:
 ```yml
-projects:
-  - project: jaffle_finance
+restrict-access: True  # default is False
 ```
 
-Unlike installing a package, the models in the `jaffle_finance` project will not be pulled down as source code, or selected to run during `dbt run`. Instead, `dbt-core` will expect stateful input that enables it to resolve references to those public models.
+</File>
 
-Models referenced from a `project`-type dependency must use [two-argument `ref`](#two-argument-variant), including the project name. Only public models can be accessed in this way. That holds true even if the `jaffle_finance` project is _also_ installed as a package (pulled down as source code), such as in a coordinated deployment. If `jaffle_finance` is listed under the `projects` in `dependencies.yml`, dbt will raise an error if a protected model is referenced from outside its project.
+</VersionBlock>
diff --git a/website/docs/docs/collaborate/govern/project-dependencies.md b/website/docs/docs/collaborate/govern/project-dependencies.md
new file mode 100644
index 00000000000..3469ec1a7b6
--- /dev/null
+++ b/website/docs/docs/collaborate/govern/project-dependencies.md
@@ -0,0 +1,96 @@
+---
+title: "Project dependencies"
+id: project-dependencies
+sidebar_label: "Project dependencies"
+description: "Reference public models across dbt projects"
+---
+
+:::info
+"Project" dependencies and cross-project `ref` is currently in closed beta and are features of dbt Cloud Enterprise. To access these features, please contact your account team.
+:::
+
+For a long time, dbt has supported code reuse and extension by installing other projects as [packages](/docs/build/packages). When you install another project as a package, you are pulling in its full source code, and adding it to your own. This enables you to call macros and run models defined in that other project.
+
+While this is a great way to reuse code, share utility macros, and establish a starting point for common transformations, it's not a great way to enable collaboration across teams and at scale, especially at larger organizations.
+
+This year, dbt Labs is introducing an expanded notion of `dependencies` across multiple dbt projects:
+- **Packages** &mdash; Familiar and pre-existing type of dependency. You take this dependency by installing the package's full source code (like a software library).
+- **Projects** &mdash; A _new_ way to take a dependency on another project. Using a metadata service that runs behind the scenes, dbt Cloud resolves references on-the-fly to public models defined in other projects. You don't need to parse or run those upstream models yourself. Instead, you treat your dependency on those models as an API that returns a dataset. The maintainer of the public model is responsible for guaranteeing its quality and stability.
+
+## Example
+
+As an example, let's say you work on the Marketing team at the Jaffle Shop. The name of your team's project is `jaffle_marketing`:
+
+<File name="dbt_project.yml">
+
+```yml
+name: jaffle_marketing
+```
+
+</File>
+
+As part of your modeling of marketing data, you need to take a dependency on two other projects:
+- `dbt_utils` as a [package](#packages-use-case): An collection of utility macros that you can use while writing the SQL for your own models. This package is, open-source public, and maintained by dbt Labs.
+- `jaffle_finance` as a [project use-case](#projects-use-case): Data models about the Jaffle Shop's revenue. This project is private and maintained by your colleagues on the Finance team. You want to select from some of this project's final models, as a starting point for your own work.
+
+<File name="dependencies.yml">
+
+```yml
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.1.1
+
+projects:
+  - name: jaffle_finance  # matches the 'name' in their 'dbt_project.yml'
+```
+
+</File>
+
+What's happening here?
+
+The `dbt_utils` package &mdash; When you run `dbt deps`, dbt will pull down this package's full contents (100+ macros) as source code and add them to your environment. You can then call any macro from the package, just as you can call macros defined in your own project.
+
+The `jaffle_finance` projects &mdash; This is a new scenario. Unlike installing a package, the models in the `jaffle_finance` project will _not_ be pulled down as source code and parsed into your project. Instead, dbt Cloud provides a metadata service that resolves references to [**public models**](/docs/collaborate/govern/model-access) defined in the `jaffle_finance` project.
+
+### Advantages
+
+When you're building on top of another team's work, resolving the references in this way has several advantages:
+- You're using an intentional interface designated by the model's maintainer with `access: public`.
+- You're keeping the scope of your project narrow, and avoiding unnecessary resources and complexity. This is faster for you and faster for dbt.
+- You don't need to mirror any conditional configuration of the upstream project such as `vars`, environment variables, or `target.name`. You can reference them directly wherever the Finance team is building their models in production. Even if the Finance team makes changes like renaming the model, changing the name of its schema, or [bumping its version](/docs/collaborate/govern/model-versions), your `ref` would still resolve successfully.
+- You eliminate the risk of accidentally building those models with `dbt run` or `dbt build`. While you can select those models, you can't actually build them. This prevents unexpected warehouse costs and permissions issues. This also ensures proper ownership and cost allocation for each team's models.
+
+### Usage
+
+**Writing `ref`:** Models referenced from a `project`-type dependency must use [two-argument `ref`](/reference/dbt-jinja-functions/ref#two-argument-variant), including the project name:
+
+<File name="models/marts/roi_by_channel.sql">
+
+```sql
+with monthly_revenue as (
+  
+    select * from {{ ref('jaffle_finance', 'monthly_revenue') }}
+
+),
+
+...
+
+```
+
+</File>
+
+**Cycle detection:** Currently, "project" dependencies can only go in one direction, meaning that the `jaffle_finance` project could not add a new model that depends, in turn, on `jaffle_marketing.roi_by_channel`. dbt will check for cycles across projects and raise errors if any are detected. We are considering support for this pattern in the future, whereby dbt would still check for node-level cycles while allowing cycles at the project level.
+
+### Comparison
+
+If you were to instead install the `jaffle_finance` project as a `package` dependency, you would instead be pulling down its full source code and adding it to your runtime environment. This means:
+- dbt needs to parse and resolve more inputs (which is slower)
+- dbt expects you to configure these models as if they were your own (with `vars`, env vars, etc)
+- dbt will run these models as your own unless you explicitly `--exclude` them
+- You could be using the project's models in a way that their maintainer (the Finance team) hasn't intended
+
+There are a few cases where installing another internal project as a package can be a useful pattern:
+- Unified deployments &mdash; In a production environment, if the central data platform team of Jaffle Shop wanted to schedule the deployment of models across both `jaffle_finance` and `jaffle_marketing`,  they could use dbt's [selection syntax](/reference/node-selection/syntax) to create a new "passthrough" project that installed both projects as packages.
+- Coordinated changes &mdash; In development, if you wanted to test the effects of a change to a public model in an upstream project (`jaffle_finance.monthly_revenue`) on a downstream model (`jaffle_marketing.roi_by_channel`) _before_ introducing changes to a staging or production environment, you can install the `jaffle_finance` package as a package within `jaffle_marketing`.  The installation can point to a specific git branch, however, if you find yourself frequently needing to perform end-to-end testing across both projects, we recommend you re-examine if this represents a stable interface boundary. 
+
+These are the exceptions, rather than the rule. Installing another team's project as a package adds complexity, latency, and risk of unnecessary costs. By defining clear interface boundaries across teams, by serving one team's public models as "APIs" to another, and by enabling practitioners to develop with a more narrowly-defined scope, we can enable more people to contribute, with more confidence, while requiring less context upfront.
diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index ab831e8b760..52850239a53 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -36,6 +36,8 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 
 [**Namespacing:**](/faqs/Models/unique-model-names) Model names can be duplicated across different namespaces (packages/projects), so long as they are unique within each package/project. We strongly encourage using [two-argument `ref`](/reference/dbt-jinja-functions/ref#two-argument-variant) when referencing a model from a different package/project.
 
+[**Project dependencies**](/docs/collaborate/govern/project-dependencies): Introduces `dependencies.yml` and dependent `projects` as a feature of dbt Cloud Enterprise. Allows enforcing model access (public vs. protected/private) across project/package boundaries. Enables cross-project `ref` of public models, without requiring the installation of upstream source code.
+
 ### Quick hits
 
 More consistency and flexibility around packages! Resources defined in a package will respect variable and global macro definitions within the scope of that package.
diff --git a/website/docs/reference/dbt_project.yml.md b/website/docs/reference/dbt_project.yml.md
index bebf1a713c8..59541a81256 100644
--- a/website/docs/reference/dbt_project.yml.md
+++ b/website/docs/reference/dbt_project.yml.md
@@ -83,6 +83,7 @@ vars:
   - macro_namespace: packagename
     search_order: [packagename]
 
+[restrict-access](/docs/collaborate/govern/model-access): true | false
 ```
 
 </File>
diff --git a/website/sidebars.js b/website/sidebars.js
index 3198d95e0f3..e10ebd513c2 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -397,6 +397,7 @@ const sidebarSettings = {
             "docs/collaborate/govern/model-access",
             "docs/collaborate/govern/model-contracts",
             "docs/collaborate/govern/model-versions",
+            "docs/collaborate/govern/project-dependencies",
           ],
         },
       ],

From 36eee926c855c6eff7866e87c0288c386330952a Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 13 Jul 2023 15:46:15 -0700
Subject: [PATCH 063/703] Changes to the collect_freshness macro

---
 .../docs/guides/migration/versions/02-upgrading-to-v1.5.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 811b57e6a33..3f8a910461c 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -58,6 +58,13 @@ models:
 ```
 Some options that could previously be specified before a sub-command can now only be specified afterward. For example, `dbt --profiles-dir . run` isn't valid anymore, and instead, you need to use `dbt run --profiles-dir .`
 
+
+The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object, otherwise, some of your dbt commands will never finish. For example:
+
+```sql
+{{ return(load_result('collect_freshness')) }}
+```
+
 Finally: The [built-in `generate_alias_name` macro](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/get_custom_name/get_custom_alias.sql) now includes logic to handle versioned models. If your project has reimplemented the `generate_alias_name` macro with custom logic, and you want to start using [model versions](/docs/collaborate/govern/model-versions), you will need to update the logic in your macro. Note that, while this is **not** a prerequisite for upgrading to v1.5—only for using the new feature—we recommmend that you do this during your upgrade, whether you're planning to use model versions tomorrow or far in the future.
 
 ### For consumers of dbt artifacts (metadata)

From d4e1bca881e4e290d5e9d55d3f04ecb45f9cc287 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 13 Jul 2023 16:08:29 -0700
Subject: [PATCH 064/703] Update the command reference to include retry for
 Cloud (#3739)

## What are you changing in this pull request and why?
Per this [internal
conversation](https://dbt-labs.slack.com/archives/C02NCQ9483C/p1689284760338829),
it looks like `dbt retry` works in dbt Cloud when it has 1.6 or higher.


## Checklist

- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page
---
 website/docs/reference/dbt-commands.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 116618e29e5..0ecac561766 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -23,6 +23,7 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project
+- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
 - [run](/reference/commands/run): runs the models in a project
 - [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
 - [seed](/reference/commands/seed): loads CSV files into the database

From 0305e429934aa7dbb4dae9ead688659834498aed Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Thu, 13 Jul 2023 16:29:11 -0700
Subject: [PATCH 065/703] default --> deafults

---
 website/docs/docs/build/dimensions.md | 4 ++--
 website/docs/docs/build/measures.md   | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index ce63a0f0ed9..abe769e54a1 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -21,7 +21,7 @@ semantic_models:
   - name: transactions
     description: A record for every transaction that takes place. Carts are considered multiple transactions for each SKU. 
     model: {{ ref("fact_transactions") }}
-    default:
+    defaults:
       agg_time_dimension: metric_time
 # --- entities ---
   entities: 
@@ -292,7 +292,7 @@ semantic_models:
       every transaction. There is only one transaction id per 
       transaction. The `metric_time` or date is reflected in UTC.
     model: {{ ref(fact_transactions) }}
-    default:
+    defaults:
       agg_time_dimension: metric_time
 
     entities:
diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index c96761ecb1b..febdb991b03 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -64,7 +64,7 @@ semantic_models:
  - name: transactions
     description: A record for every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
-    default:
+    defaults:
       agg_time_dimensions:
 
 # --- entities ---
@@ -166,7 +166,7 @@ semantic_models:
   - name: subscription_table
     description: A subscription table with one row per date for each active user and their subscription plans. 
     model: ref('your_schema.subscription_table')
-    default:
+    defaults:
       agg_time_dimension: metric_time 
 
     entities:

From eb71a3bada7cd824cb4e93d9bbb356a439eab39f Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Thu, 13 Jul 2023 16:57:57 -0700
Subject: [PATCH 066/703] added initial clone command page

---
 website/docs/reference/commands/clone.md | 28 +++++++++++++++++++++++-
 website/docs/reference/dbt-commands.md   |  2 +-
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 657a74c54f8..e0e92d52bc7 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -4,4 +4,30 @@ sidebar_label: "clone"
 id: "clone"
 ---
 
-blah blah blah
\ No newline at end of file
+The `dbt clone` command clones selected nodes from the specified state to the target schema(s). This command makes use of the `clone` materialization:
+- If using a data warehouses that support zero-copy cloning of tables, selected models that are tables will be created as clones
+- For views and warehouses that do not support zero-copy cloning, selected models will be created as simple `select * from ...` pointer views
+
+Note: 
+- The state to clone from is based on the location of nodes in the manifest provided to `--state`.
+- By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 
+- You may want to specify a higher number of [threads](website/docs/docs/running-a-dbt-project/using-threads.md) to decrease execution time since individual clone statements are independent of one another.
+
+The `clone` command is useful for:
+- blue/green deployment
+- cloning current production state into development schema(s)
+- handling incremental models in Slim CI dbt Cloud jobs
+
+```bash
+# clone all of my models from specified state to my target schema(s)
+dbt clone --state path/to/artifacts
+
+# clone one_specific_model of my models from specified state to my target schema(s)
+dbt clone --select one_specific_model --state path/to/artifacts
+
+# clone all of my models from specified state to my target schema(s) and recreate all pre-exisiting relations in the current target
+dbt clone --state path/to/artifacts --full-refresh
+
+# clone all of my models from specified state to my target schema(s), running up to 50 clone statements in parallel
+dbt clone --state path/to/artifacts --threads 50
+```
\ No newline at end of file
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index edeb58711eb..1a45ea47a3d 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -20,7 +20,7 @@ Select the tabs that are relevant to the your development workflow. For example,
 Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) and use the `dbt` prefix. For example, to run the `test` command, type `dbt test`.
 
 - [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
-- [clone](/reference/commands/clone): clone selected models from specified state (requires dbt 1.6 or higher)
+- [clone](/reference/commands/clone): clone selected nodes from specified state (requires dbt 1.6 or higher)
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project

From db5b2cc92dc104df79ce4bdb8d4079e669ba3ff9 Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Thu, 13 Jul 2023 17:01:45 -0700
Subject: [PATCH 067/703] update project state page

---
 website/docs/reference/node-selection/syntax.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/node-selection/syntax.md b/website/docs/reference/node-selection/syntax.md
index 2e983f9d08f..7c55ee6e6b6 100644
--- a/website/docs/reference/node-selection/syntax.md
+++ b/website/docs/reference/node-selection/syntax.md
@@ -91,8 +91,9 @@ dbt can leverage artifacts from a prior invocation as long as their file path is
 - [The `state:` selector](/reference/node-selection/methods#the-state-method), whereby dbt can identify resources that are new or modified
 by comparing code in the current project against the state manifest.
 - [Deferring](/reference/node-selection/defer) to another environment, whereby dbt can identify upstream, unselected resources that don't exist in your current environment and instead "defer" their references to the environment provided by the state manifest.
+- The [`dbt clone` command](website/docs/reference/commands/clone.md), whereby dbt can clone nodes based on their location in the manifest provided to the `--state` flag.
 
-Together, these two features enable ["slim CI"](/guides/legacy/best-practices#run-only-modified-models-to-test-changes-slim-ci). We expect to add more features in future releases that can leverage artifacts passed to the `--state` flag.
+Together, the `state:` selector and deferral enable ["slim CI"](/guides/legacy/best-practices#run-only-modified-models-to-test-changes-slim-ci). We expect to add more features in future releases that can leverage artifacts passed to the `--state` flag.
 
 ### Establishing state
 

From 4d3f9ddefc8acc888578b33bcefd449b56de404d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 14 Jul 2023 09:47:06 +0100
Subject: [PATCH 068/703] replace screenshot with code block

replacing screenshot with code block to improve accessibility and ux.

also removed image file
---
 website/docs/reference/commands/compile.md | 35 +++++++++++++++++++---
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 7b3f18a9c15..d4262fc9998 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -33,13 +33,40 @@ dbt compile --select stg_payments
 dbt compile --inline "select * from {{ ref('raw_orders') }}"
 ```
 
-<Lightbox src="/img/docs/reference/dbt-compile.png" title="dbt compile --select stg_payments"/>
+```
+dbt compile --select stg_orders
+21:17:09 Running with dbt=1.5.0-b5
+21:17:09 Found 5 models, 20 tests, 0 snapshots, 0 analyses, 425 macros, 0 operations, 3 seed files, 0 sources, 0 exposures, 0 metrics, 0 groups
+21:17:09
+21:17:09 Concurrency: 24 threads (target='dev')
+21:17:09
+21:17:09 Compiled node 'stg_orders' is:
+with source as (
+    select * from "jaffle_shop"."main"."raw_orders"
+
+),
+
+renamed as (
+
+    select
+        id as order_id
+        user_id as customer_id
+        order_date
+        status
+
+    from source
+
+)
+
+select * from renamed
+
+```
 
 </VersionBlock>
 
-The command accesses the data platform to cache related metadata, and to run introspective queries. Use the flags:
-- `--no-populate-cache` to disable initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query.
-- `--no-introspect` to disable instrospective queries. dbt will raise an error if a model's definition requires running one.
+The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
+- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query.
+- `--no-introspect` to disable introspective queries. dbt will raise an error if a model's definition requires running one.
 
 
 ### FAQs

From 450f02a2ef3e021543d135806b26955e00af2ee9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 14 Jul 2023 09:48:22 +0100
Subject: [PATCH 069/703] Delete dbt-compile.png

remove image
---
 .../static/img/docs/reference/dbt-compile.png  | Bin 777401 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 website/static/img/docs/reference/dbt-compile.png

diff --git a/website/static/img/docs/reference/dbt-compile.png b/website/static/img/docs/reference/dbt-compile.png
deleted file mode 100644
index f10b5a9252c9e9a41c36b6051441f4074d68f405..0000000000000000000000000000000000000000
GIT binary patch
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From 4081b7bc9fdf6e0c45a3d6b0f541dbd215a0d3b0 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 10:04:16 +0100
Subject: [PATCH 070/703] add code block

---
 website/docs/reference/commands/show.md       |  46 +++++++++++++++++-
 .../docs/reference/dbt-show-failing-test.png  | Bin 310293 -> 0 bytes
 .../static/img/docs/reference/dbt-show.png    | Bin 625039 -> 0 bytes
 3 files changed, 44 insertions(+), 2 deletions(-)
 delete mode 100644 website/static/img/docs/reference/dbt-show-failing-test.png
 delete mode 100644 website/static/img/docs/reference/dbt-show.png

diff --git a/website/docs/reference/commands/show.md b/website/docs/reference/commands/show.md
index 832f32ac38a..5bdcfacc1e8 100644
--- a/website/docs/reference/commands/show.md
+++ b/website/docs/reference/commands/show.md
@@ -23,8 +23,50 @@ or
 dbt show --inline "select * from {{ ref('model_name') }}"
 ```
 
-<Lightbox src="/img/docs/reference/dbt-show.png" title="dbt show preview"/>
+The following is an example of `dbt show` output for a model named `stg_orders`:
+
+```bash
+dbt show --select stg_orders
+21:17:38 Running with dbt=1.5.0-b5
+21:17:38 Found 5 models, 20 tests, 0 snapshots, 0 analyses, 425 macros, 0 operations, 3 seed files, 0 sources, 0 exposures, 0 metrics, 0 groups
+21:17:38
+21:17:38 Concurrency: 24 threads (target='dev')
+21:17:38
+21:17:38 Previewing node 'stg_orders' :
+| order_id | customer_id | order_date | status    |
+|----------+-------------+------------+--------   |
+| 1        |           1 | 2023-01-01 | returned  |
+| 2        |           3 | 2023-01-02 | completed |
+| 3        |          94 | 2023-01-03 | completed |
+| 4        |          50 | 2023-01-04 | completed |
+| 5        |          64 | 2023-01-05 | completed |
+
+```
 
 For example, if you've just built a model that has a failing test, you can quickly preview the test failures right in the terminal, to find values of `id` that are duplicated:
 
-<Lightbox src="/img/docs/reference/dbt-show-failing-test.png" title="dbt show failing test"/>
+```bash
+$ dbt build -s my_model_with_duplicates
+13:22:47 Running with dbt=1.5.0
+...
+13:22:48 Completed with 1 error and 0 warnings:
+13:22:48
+13:22:48 Failure in test unique_my_model_with_duplicates (models/schema.yml)
+13:22:48   Got 1 result, configured to fail if not 0
+13:22:48
+13:22:48   compiled code at target/compiled/my_dbt_project/models/schema.yml/unique_my_model_with_duplicates_id.sql
+13:22:48
+13:22:48 Done. PASS=1 WARN=0 ERROR=1 SKIP=0 TOTAL=2
+
+$ dbt show -s unique_my_model_with_duplicates_id
+13:22:53 Running with dbt=1.5.0
+13:22:53 Found 4 models, 2 tests, 0 snapshots, 0 analyses, 309 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metrics, 0 groups
+13:22:53
+13:22:53 Concurrency: 5 threads (target='dev')
+13:22:53
+13:22:53 Previewing node 'unique_my_model_with_duplicates_id':
+| unique_field | n_records |
+| ------------ | --------- |
+|            1 |         2 |
+
+```
diff --git a/website/static/img/docs/reference/dbt-show-failing-test.png b/website/static/img/docs/reference/dbt-show-failing-test.png
deleted file mode 100644
index 53d001cc2cb4010ee589909cfe01b43e1ef82344..0000000000000000000000000000000000000000
GIT binary patch
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From a57ad704585a19d18183e0e1c56d3ba7dcc06f4b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 10:09:58 +0100
Subject: [PATCH 071/703] adding code block

---
 website/docs/reference/commands/compile.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index d4262fc9998..c91d6a7db8a 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -33,7 +33,10 @@ dbt compile --select stg_payments
 dbt compile --inline "select * from {{ ref('raw_orders') }}"
 ```
 
-```
+returns the following:
+
+
+```bash
 dbt compile --select stg_orders
 21:17:09 Running with dbt=1.5.0-b5
 21:17:09 Found 5 models, 20 tests, 0 snapshots, 0 analyses, 425 macros, 0 operations, 3 seed files, 0 sources, 0 exposures, 0 metrics, 0 groups
@@ -59,7 +62,6 @@ renamed as (
 )
 
 select * from renamed
-
 ```
 
 </VersionBlock>

From 1ccd3824ee36cd7b0323b2fbb50e3843c33df529 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 10:49:32 +0100
Subject: [PATCH 072/703] RN

---
 .../docs/dbt-versions/release-notes/06-July-2023/faster-run.md  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/faster-run.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/faster-run.md
index 0f88f1d2fa8..ba82234c0b5 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/faster-run.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/faster-run.md
@@ -2,7 +2,7 @@
 title: "Enhancement: Faster run starts and unlimited job concurrency"
 description: "We have enhanced the dbt Cloud Scheduler by reducing prep time for all accounts and provided unlimited job concurrency for Enterprise accounts."
 sidebar_label: "Enhancement: Faster run starts and unlimited job concurrency"
-tags: [07-2023, scheduler]
+tags: [July-2023, scheduler]
 date: 2023-07-06
 sidebar_position: 10
 ---

From 1696c5b2e5ce50b15647da38e7928a924f873083 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 10:50:09 +0100
Subject: [PATCH 073/703] RELEASE

---
 .../06-July-2023/sl-revamp-beta.md            | 51 +++++++++++++++++++
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 17 +------
 website/snippets/_new-sl-changes.md           |  2 +-
 3 files changed, 54 insertions(+), 16 deletions(-)
 create mode 100644 website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
new file mode 100644
index 00000000000..16330dfbe40
--- /dev/null
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -0,0 +1,51 @@
+---
+title: "Enhancement: Revamped dbt Semantic Layer available in public beta"
+description: "July 2023: The revamped dbt Semantic Layer, now available in public beta, introduces new semantic components and evolves the semantic layer's capability."
+sidebar_label: "Enhancement: Revamped dbt Semantic Layer in public beta"
+tags: [July-2023, dbt Semantic Layer]
+date: 2023-07-31
+sidebar_position: 9
+---
+
+We are thrilled to announce the re-release of the dbt Semantic Layer, now available in [public beta](#public-beta). It revolutionizes data analysis by introducing new semantic components that lay the foundation for effective organization and interaction with data. With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
+
+<Lightbox src="/img/sl-architecture.jpg" width="95%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
+
+## Why the dbt Semantic Layer?
+
+What sets the dbt Semantic Layer apart is its ability to centralize logic and data applications, streamlining processes and enabling efficient management and utilization of data models. It provides a consistent view of data, incorporating context and historical information, simplifying complex tasks, and reducing costs.
+
+We are excited to present several important capabilities with the enhanced dbt Semantic Layer:
+
+- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
+
+- **Improved governance**: The dbt Semantic Layer ensures proper governance and auditing of data changes, providing an auditable record of modifications and clear ownership. This saves time by making it clear who can create and manage new metrics, ensuring accountability and data integrity.
+
+- **Reduce costs**: The dbt Semantic Layer simplifies complex tasks, such as bridging entities across a semantic graph. Often users duplicate slices and dice of data and make them available in a data platform, making it difficult to manage and causing high computation. The dbt Semantic Layer minimizes duplication of work and reducing computational costs - allowing users to focus on analyzing data rather than navigating intricate technical processes or duplicating work. 
+
+- **Enhanced efficiency**: With the dbt Semantic Layer, data teams can create and update metrics in one central location, saving time and reducing the risk of errors and downstream impacts. The agnostic approach allows seamless integration with various data platforms and downstream BI tools, making migration easier and more flexible.
+
+- **Accessible data**: Without the dbt Semantic Layer, there would be limited access to critical information which stifles effective communication. An accessible semantic layer allows all users to have equal opportunities to leverage data insights, fostering collaboration and driving innovation. 
+
+By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows. 
+
+## Public beta 
+
+THINKING OF TURNING INTO A TABLE ONCE I HAVE THE ACCURATE INFO AND MOVING ABOVE AFTER INTO (BEFORE 'WHY SL')
+
+The dbt Semantic Layer is currently available as a public beta, which means:
+
+- **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during public beta. 
+  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Semantic Layer and Discovery APIs. 
+- **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
+- **When?**  Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
+- **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user
+
+## Next steps
+
+To experience the universal dbt Semantic Layer and its enhanced beta capabilities, check out:
+
+- [Revamped dbt Semantic Layer documents](/docs/use-dbt-semantic-layer/dbt-sl)
+- [dbt Semantic Layer get started guide](/docs/use-dbt-semantic-layer/quickstart-sl)
+- [Build your metrics with MetricFlow](/docs/build/build-metrics-intro)
+- BLOG announcement link.
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index e9dd131f696..0aa486cd8a8 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -25,7 +25,7 @@ The dbt Semantic Layer has the following major components and you can refer to [
 
 ## Explore the dbt Semantic Layer
 
-The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](#public-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
+The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
 
 <div className="grid--2-col">
 
@@ -55,19 +55,6 @@ The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and
 
 </div>
 
-
-## Public beta 
-
-MOVING THIS TO RELEASE NOTE SECTION BECAUSE I DONT THINK THIS BELONGS HERE. ANYTHING THAT SAYS PUBLIC BETA CAN LINK TO RN
-
-The dbt Semantic Layer is currently available as a public beta, which means:
-
-- **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during public beta. 
-  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Semantic Layer and Discovery APIs. 
-- **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-- **When?**  Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
-- **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
-
 ## Product architecture 
 
 The dbt Semantic Layer product architecture includes four primary components:
@@ -79,7 +66,7 @@ The dbt Semantic Layer product architecture includes four primary components:
 | **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
-<Lightbox src="/img/sl-architecture.jpg" width="85%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
+<Lightbox src="/img/sl-architecture.jpg" width="95%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
 
 
 ## Related questions
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index 122154070c1..02bea244a29 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -1,7 +1,7 @@
 
 :::tip Introducing the new dbt Semantic Layer 🎉
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/use-dbt-semantic-layer/dbt-sl#public-beta). 
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta). 
 
 **What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
 

From af364b728aa9daf68a2fd0edc2c72f15e4fcc12e Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 11:20:34 +0100
Subject: [PATCH 074/703] adding new prod arch page

---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 112 +++---------------
 .../use-dbt-semantic-layer/sl-architecture.md |  82 +++++++++++++
 website/sidebars.js                           |   1 +
 website/snippets/_new-sl-changes.md           |   2 +-
 4 files changed, 99 insertions(+), 98 deletions(-)
 create mode 100644 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 0aa486cd8a8..8149d52b125 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -3,6 +3,7 @@ title: "dbt Semantic Layer"
 id: dbt-sl
 description: "Introducing the improved dbt Semantic Layer, which allows data teams to centrally define and query metrics."
 sidebar_label: "About the dbt Semantic Layer"
+hide_table_of_contents: true
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -18,26 +19,23 @@ By leveraging the dbt Semantic Layer, data teams achieve reduced code duplicatio
 
 To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
-The dbt Semantic Layer has the following major components and you can refer to [product architecture](#product-architecture) for more details:
-
-- [MetricFlow](/docs/build/build-metrics-intro) allows you to define and test your semantic models and metrics in version-controlled dbt project code.
-- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) enables you to explore and analyze these metrics in downstream tools.
+<Lightbox src="/img/sl-architecture.jpg" width="70%" title="Define and query metrics with the dbt Semantic Layer and seamlessly connect with integration tools for reliable organization-wide metrics." />
 
 ## Explore the dbt Semantic Layer
 
-The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
+The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
 
-<div className="grid--2-col">
+<div className="grid--3-col">
 
 <Card
-    title="Get started with MetricFlow and the dbt Semantic Layer"
-    body="Build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
+    title="Get started with the dbt Semantic Layer and MetricFlow"
+    body="Build and define metrics with MetricFlow, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
     link="/docs/use-dbt-semantic-layer/quickstart-sl"
     icon="dbt-bit"/>
 
 <Card
     title="Set up the dbt Semantic Layer"
-    body="Set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
+    body="Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
     link="/docs/use-dbt-semantic-layer/setup-sl"
     icon="dbt-bit"/>
 
@@ -48,63 +46,18 @@ The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and
     icon="dbt-bit"/>
     
 <Card
-    title="Semantic Layer API"
-    body="Use the Semantic Layer APIs to query metrics in downstream tools."
+    title="Semantic Layer APIs"
+    body="Use the Semantic Layer APIs to query metrics in downstream tools for consistent, reliable data metrics."
     link="/docs/use-dbt-semantic-layer/sl-api-overview"
     icon="dbt-bit"/>
 
-</div>
-
-## Product architecture 
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
-| **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-
-<Lightbox src="/img/sl-architecture.jpg" width="95%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
-
-
-## Related questions
-
-<details>
-  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
-  <div>
-    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
-  </div>
-</details>
-    
-<details>
-  <summary>How are you storing my data?</summary>
-  <div>
-    <div>The dbt Semantic Layer does not store, cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
-  </div>
-</details>
-<details>
-  <summary>Is the dbt Semantic Layer open source?</summary>
-  <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
-      
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+<Card
+    title="Product architecture"
+    body="Learn about the powerful components that make up the dbt Semantic Layer."
+    link="/docs/use-dbt-semantic-layer/sl-architecture"
+    icon="dbt-bit"/>
 
-<ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
-    </ul></div> </div>
-</details>
-<details>
-    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
-  <div>
-    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
-    </div>
-  </div>
-</details>
-      <br></br>
+</div>
 
 </VersionBlock>
 
@@ -141,41 +94,6 @@ To use the dbt Semantic Layer, you’ll need to meet the following:
 
 <Snippet src="sl-considerations-banner" />
 
-## Public Preview
-
-The dbt Semantic Layer is currently available for Public Preview, which means:
-
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more info on plan availability.
-
-- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
-partner tool to import metric definition.
-- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
-
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-    
-&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
-
-&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
-
-## Product architecture 
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
-    
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
-
-dbt Semantic Layer integrations will:
-
-- Leverage the Discovery API to fetch a list of objects and their attributes, like metrics
-- Generate a dbt-SQL statement
-- Then query the SQL proxy to evaluate the results of this statement
-
 
 ## Manage metrics
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
new file mode 100644
index 00000000000..eee196ef535
--- /dev/null
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -0,0 +1,82 @@
+---
+title: "dbt Semantic Layer product architecture"
+id: sl-architecture
+description: "dbt Semantic Layer product architecture and related questions."
+sidebar_label: "Product architecture"
+---
+
+<VersionBlock firstVersion="1.6">
+
+The dbt Semantic Layer product architecture includes four primary components:
+
+| Components | Information | Developer plans | Team plans | Enterprise plans | License |
+| --- | --- | :---: | :---: | :---: | --- |
+| **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
+| **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+
+
+
+
+## Related questions
+
+<details>
+  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
+  <div>
+    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
+  </div>
+</details>
+    
+<details>
+  <summary>How are you storing my data?</summary>
+  <div>
+    <div>The dbt Semantic Layer does not store, cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
+  </div>
+</details>
+<details>
+  <summary>Is the dbt Semantic Layer open source?</summary>
+  <div>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
+      
+During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+
+<ul>
+<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
+<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
+<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
+    </ul></div> </div>
+</details>
+<details>
+    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
+  <div>
+    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
+    </div>
+  </div>
+</details>
+      <br></br>
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+## Product architecture 
+
+The dbt Semantic Layer product architecture includes four primary components:
+
+| Components | Information | Developer plans | Team plans | Enterprise plans | License |
+| --- | --- | :---: | :---: | :---: | --- |
+| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
+| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
+| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
+    
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
+
+dbt Semantic Layer integrations will:
+
+- Leverage the Discovery API to fetch a list of objects and their attributes, like metrics
+- Generate a dbt-SQL statement
+- Then query the SQL proxy to evaluate the results of this statement
+
+</VersionBlock>
diff --git a/website/sidebars.js b/website/sidebars.js
index 03b150529d1..028c0543f7d 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -410,6 +410,7 @@ const sidebarSettings = {
         "docs/use-dbt-semantic-layer/quickstart-sl",
         "docs/use-dbt-semantic-layer/setup-sl",
         "docs/use-dbt-semantic-layer/avail-sl-integrations",
+        "docs/use-dbt-semantic-layer/sl-architecture",
         {
           type: "category",
           label: "Semantic Layer APIs",
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index 02bea244a29..9d6ee48bc08 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -1,7 +1,7 @@
 
 :::tip Introducing the new dbt Semantic Layer 🎉
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. The new dbt Semantic Layer will lay the foundation for effective organization and interaction with data. The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta). 
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and available open to all dbt Cloud plans. It introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. 
 
 **What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
 

From 35e141acced55d93c0702a8edf3666014666ddf6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 11:55:29 +0100
Subject: [PATCH 075/703] ux and readability changes

---
 .../docs/docs/build/build-metrics-intro.md    | 35 +++++++++++++------
 .../avail-sl-integrations.md                  |  6 ++--
 .../docs/use-dbt-semantic-layer/dbt-sl.md     |  4 +--
 .../use-dbt-semantic-layer/quickstart-sl.md   | 22 +++++++-----
 .../docs/use-dbt-semantic-layer/setup-sl.md   |  9 +++--
 5 files changed, 46 insertions(+), 30 deletions(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 617b3d1bc4a..10febfa5dfc 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -25,31 +25,44 @@ Before you start, keep the following considerations in mind:
   * Note: Support for BigQuery, Databricks, and Redshift coming soon.
 - dbt Labs is working with [integration partners](https://www.getdbt.com/product/semantic-layer-integrations) to develop updated integrations for the new Semantic Layer, powered by MetricFlow, in addition to introducing other consumption methods like Python and JDBC. <br /><br />
 
-<div className="grid--4-col">
+<div className="grid--3-col">
 
 <Card
     title="About MetricFlow"
     body="Understand MetricFlow's core concepts, key principles, and how to use this powerful tool."
     link="/docs/build/about-metricflow"
-    icon="rocket"/>
-
-<Card
-    title="Get started with MetricFlow"
-    body="Learn how to create a semantic model, a metric, and test and upload your metric using MetricFlow."
-    link="/docs/build/sl-getting-started"
-    icon="rocket"/>
+    icon="dbt-bit"/>
 
   <Card
     title="Semantic model"
-    body="Use Semantic models as the basis for defining data. They act as nodes in the semantic graph, with entities connecting them."
+    body="Use semantic models as the basis for defining data. They act as nodes in the semantic graph, with entities connecting them."
     link="/docs/build/semantic-models"
-    icon="rocket"/>
+    icon="dbt-bit"/>
 
   <Card
     title="Metrics"
     body="Define metrics through the powerful combination of measures, constraints, or functions, effortlessly organized in either YAML files or separate files."
     link="/docs/build/metrics-overview"
-    icon="rocket"/>   
+    icon="dbt-bit"/>
+
+ <Card
+    title="Get started with the dbt Semantic Layer"
+    body="Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs"
+    link="/docs/use-dbt-semantic-layer/quickstart-sl"
+    icon="dbt-bit"/>
+
+  <Card
+    title="About the dbt Semantic Layer"
+    body="Introducing the dbt Semantic Layer, the universal process that allows data teams to centrally define and query metrics"
+    link="/docs/use-dbt-semantic-layer/dbt-sl"
+    icon="dbt-bit"/>
+
+  <Card
+    title="Available integrations"
+    body="Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem."
+    link="/docs/use-dbt-semantic-layer/avail-sl-integrations"
+    icon="dbt-bit"/>
+  
 
 </div> <br />
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index bd41380ac54..4519b217dc0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -1,7 +1,7 @@
 ---
 title: "Available integrations"
 id: avail-sl-integrations
-description: "Review a wide range of partners you can integrate and query with the dbt Semantic Layer."
+description: "Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem."
 sidebar_label: "Available integrations"
 ---
 
@@ -11,9 +11,9 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+Discover the diverse range of data applications that seamlessly integrate with the powerful dbt Semantic Layer, powered by MetricFlow &mdash; from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-A wide variety of data applications across the modern data stack natively integrate with the dbt Semantic Layer, powered by MetricFlow &mdash; from Business Intelligence tools to notebooks, spreadsheets, data catalogs, and more.
+Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 8149d52b125..b354f0bb1fe 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -13,9 +13,9 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 <NewSLChanges />
 
 
-The dbt Semantic Layer, powered by MetricFlow, simplifies the process of defining and using critical business metrics, like like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
+The dbt Semantic Layer, powered by MetricFlow, simplifies the process of defining and using critical business metrics, like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
 
-By leveraging the dbt Semantic Layer, data teams achieve reduced code duplication and increased consistency in business metrics across different applications. Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
+Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 
 To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 6a3ba6912e6..735c4ce554d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -2,7 +2,7 @@
 title: "Get started with the dbt Semantic Layer"
 id: quickstart-sl
 description: "Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
-sidebar_label: "Getting started"
+sidebar_label: "Get started with the dbt Semantic Layer"
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -11,7 +11,9 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies defining and using critical business metrics. It centralizes metric definitions, eliminates duplicate coding, and ensures consistent self-service access to metrics in downstream tools.  MetricFlow helps define and manage metric logic, offering efficient retrieval of metric datasets for data consumers.
+The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies defining and using critical business metrics. It centralizes metric definitions, eliminates duplicate coding, and ensures consistent self-service access to metrics in downstream tools. 
+
+MetricFlow is a powerful component within the dbt Semantic Layer that helps users define and manage company metrics efficiently. It provides flexible abstractions and SQL query generation, and also allows data consumers to retrieve metric datasets quickly and easily from a data platform.
 
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 
@@ -23,6 +25,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - [Query your metrics using partner integrations](#query-metrics)
 
 
+
 ## Prerequisites
 
 import SetUp from '/snippets/_v2-sl-prerequisites.md';
@@ -35,7 +38,7 @@ New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/
 
 ## Create a semantic model
 
-Before you begin, we recommend you learn about MetricFlow and its key concepts. There are two main objects: 
+Before you begin, we recommend you learn about more about [MetricFlow](/docs/build/about-metricflow) and its key concepts. There are two main objects: 
 
 - [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
 - [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
@@ -91,7 +94,7 @@ group by metric_time_day, country  -- dimensions
   ```
 :::
 
-## Create your metrics
+## Define metrics
 
 Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). You can define metrics in the same YAML files as your semantic models, or create a new file.
 
@@ -107,13 +110,13 @@ metrics:
 ```
  
 1. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
-* Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
-2. Run `mf validate-configs` to validate the changes before committing them.
-3. Commit and merge the code changes that contain the metric definitions.
+2. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
+3. Run `mf validate-configs` to validate the changes before committing them.
+4. Commit and merge the code changes that contain the metric definitions.
 
-If you'd lke to build out your metrics, read the [MetricFlow](/docs/build/build-metrics-intro) docs for more info.
+To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
-## Run your production job
+## Run a production job
 
 Once you’ve defined metrics in your dbt project, you can perform a job run in your deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
 
@@ -178,6 +181,7 @@ During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (De
 Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer.
 
 - [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
+- [Build your metrics](/docs/build/build-metrics-intro)
 - [About MetricFlow](/docs/build/about-metricflow)
 - [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 68062d80245..402e6a0691b 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -1,8 +1,8 @@
 ---
 title: "Set up the dbt Semantic Layer"
 id: setup-sl
-description: "You can set up the dbt Semantic Layer in dbt Cloud."
-sidebar_label: "Set up the dbt Semantic Layer"
+description: "Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive nagivation."
+sidebar_label: "Set up your Semantic Layer"
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -38,9 +38,6 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 8. You’re done 🎉! The semantic layer should is now enabled for your project. 
 -->
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/configure_sl.png" width="85%" title="Set up dbt Semantic Layer in dbt Cloud" /><br />
-
-
 </VersionBlock>
 
 <VersionBlock lastVersion="1.5">
@@ -99,6 +96,8 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 
 </VersionBlock>
 
+## Related docs
+
 - [Build your metrics](/docs/build/build-metrics-intro)
 - [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
 - [Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)

From 01e59038f7f3bee4baaef23d22b5f85b07facd1a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lutz=20K=C3=BCnneke?=
 <40922444+lutzkuen@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:35:28 +0200
Subject: [PATCH 076/703] Update trino-configs.md

Fix typo perperty -> property
---
 website/docs/reference/resource-configs/trino-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/trino-configs.md b/website/docs/reference/resource-configs/trino-configs.md
index 97b1aa7e0c6..21df13feac4 100644
--- a/website/docs/reference/resource-configs/trino-configs.md
+++ b/website/docs/reference/resource-configs/trino-configs.md
@@ -191,7 +191,7 @@ select * from {{ ref('events') }}
 
 Use the `+on_schema_change` property to define how dbt-trino should handle column changes. For more details about this property, see [column changes](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#what-if-the-columns-of-my-incremental-model-change). 
 
-If your connector doesn't support views, set the `+views_enabled` perperty to `false`.
+If your connector doesn't support views, set the `+views_enabled` property to `false`.
 
 #### append strategy
 

From 48e9fdb986c042ba8bc387d4e04947a9c51165af Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 13:45:42 +0100
Subject: [PATCH 077/703] jdbc rework

---
 .../docs/docs/use-dbt-semantic-layer/jdbc.md  | 158 +++++++++++-------
 1 file changed, 96 insertions(+), 62 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
index 244363861c2..3b9f28b58a5 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -31,153 +31,187 @@ A JDBC driver is a software component enabling a Java application to interact wi
 - Install an AWS root CA to the Java Trust Store [documentation](https://www.amazontrust.com/repository/).
 
 ## Connection parameters
-The JDBC connection requires a few different connection parameters. We provide the full JDBC string that you can connect with as well as the individual components required.
 
-This is an example of a URL connection string: 
+The JDBC connection requires a few different connection parameters. We provide the full JDBC string that you can connect with as well as the individual components required.
 
+This is an example of a URL connection string and the individual components: 
 
 ```
 jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=202339&token=SERVICE_TOKEN
 ```
 
-The components of the JDBC string are as follows:
-
-- `jdbc:arrow-flight-sql://` &mdash; The protocol for the JDBC driver.
-- `dbt Cloud access URL` &mdash; The [access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your account's dbt Cloud region. You must always add the `semantic-layer` prefix before the access URL. For example, `semantic-layer.cloud.getdbt.com`
-- `environmentId` &mdash; The unique identifier for the dbt environment, you can retrieve this from the dbt Cloud URL when you navigate to your environment under Deployments. 
-- `SERVICE_TOKEN` &mdash; dbt Cloud Service Token with “Semantic Layer Only” permissions at least. Customer can get the service token by navigating to your account settings and creating a new Service Token. The value must be encoded prior to being put into the string.
+| JDBC parameter | Description | Example |
+| -------------- | ----------- | ------- |
+| `jdbc:arrow-flight-sql://` | The protocol for the JDBC driver.  | `jdbc:arrow-flight-sql://` |
+| `semantic-layer.cloud.getdbt.com` | The [access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your account's dbt Cloud region. You must always add the `semantic-layer` prefix before the access URL.  | For dbt Cloud deployment hosted in North America, use `semantic-layer.cloud.getdbt.com`  |
+| `environmentId` | The unique identifier for the dbt production environment, you can retrieve this from the dbt Cloud URL <br /> when you navigate to **Environments** under **Deploy**. | If your URL ends with `.../environments/222222`, your `environmentId` is `222222`<br /><br />   |
+| `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” permission. Create a new service token in your **Account Settings** page. Encode the value before inserting it into the string.  | `token=SERVICE_TOKEN` |
 
+*Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable into the JDBC URL `&disableCertificateVerification=true`
 
 ## Querying the API
-The Semantic Layer JDBC API has built-in metadata calls that can provide a user with information about their metrics and dimensions.
 
-**Metadata Commands and Examples**
+The Semantic Layer JDBC API has built-in metadata calls which can provide a user with information about their metrics and dimensions. Here are some metadata commands and examples:
 
+<Tabs>
 
-Fetching all metrics defined
+<TabItem value="allmetrics" label="Fetch all defined metrics">
 
-```
+Use this query to fetch all defined metrics in your dbt project:
+
+```bash
 select * from {{ 
 	semantic_layer.metrics() 
 }}
 ```
+</TabItem>
 
+<TabItem value="alldimensions" label="Fetch all dimensions for a metric">
 
-Fetching all dimensions for a metric
+Use this query to fetch all dimensions for a metric. 
 
-```
+Note, `metrics` is a required argument that lists with one or multiple metrics in it.
+
+```bash
 select * from {{ 
 	semantic_layer.dimensions(metrics=['food_order_amount'])}}
 ```
-Required arguments: `metrics` list with one or multiple metrics in it.
 
 
-Fetching dimension values for one or multiple metrics and single dimension
+</TabItem>
 
-```
+<TabItem value="dimensionvalueformetrics" label="Fetch dimension values metrics">
+
+Use this query to fetch dimension values for one or multiple metrics and single dimension. 
+
+Note, `metrics` is a required argument that lists with one or multiple metrics in it, and a single dimension. 
+
+```bash
 select * from {{ 
 semantic_layer.dimension_values(metrics=["food_order_amount"], group_by="customer__customer_name")}}
 ```
-Required arguments: `metrics` list with one or multiple metrics in it, a single dimension
 
+</TabItem>
+
+</Tabs>
 
 ## Querying Parameters
 
-| Parameter | Description                                                                                                                                                                                                                    | Example                                                                         | Required or Optional |
-|-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|----------------------|
-| metrics   | The metric name as defined in your dbt metric configuration                                                                                                                                                                    | `metrics=[revenue]`                                                             | Required             |
-| group_by  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=[user__country, metric_time]`                                         | Optional             |
-| grain     | A paremeter specific to any time dimension and changes the grain of the data from the default for the metric.                                                                                                                  | `group_by=[`Dimension('metric_time').grain('week\|day\|month\|quarter\|year')]` | Optional             |
-| where     | A where clause that allows you to filter on dimensions                                                                                                                                                                         | `where="metric_time >= '2022-03-08'"`                                           | Optional             |
-| limit     | Limit the data returned                                                                                                                                                                                                        | `limit=10`                                                                      | Optional             |
-| order     | Order the data returned                                                                                                                                                                                                        | `order_by=['-order_gross_profit']` (remove `-` for ascending order)             | Optional             |
-| explain   | If true, returns generated SQL for the data platform but does not execute it.                                                                                                                                                  | `explain=True`                                                                  | Optional             |
+Use the following query parameters to filter and sort data, along with examples:
 
+| Parameter | Description  | Example    | Type |
+| --------- | -----------| ------------ | -------------------- |
+| `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=[revenue]` | Required    |
+| `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=[user__country, metric_time]`     | Optional   |
+| `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | ```group_by=[`Dimension('metric_time').``` <br/> ```grain('week\|day\|month\|quarter\|year')]``` | Optional     |
+| `where`     | A where clause that allows you to filter on dimensions   | `where="metric_time >= '2022-03-08'"`   | Optional   |
+| `limit`   | Limit the data returned    | `limit=10` | Optional  |
+|`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
+| `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |
 
+## Additional examples
 
-## Examples
+Use the following examples to help you get started with the JDBC API
 
-**Fetching Metadata for Metrics. You can filter/add any SQL outside of the templating** 
+### Fetch metadata for metrics
 
-```
+You can filter/add any SQL outside of the templating syntax. For example, you can use the following query to fetch the name and dimensions for a metric: 
+
+```bash
 select name, dimensions from {{ 
 	semantic_layer.metrics() 
 	}}
-WHERE name='food_order_amount'
+	WHERE name='food_order_amount'
 ``` 
 
+### Query common dimensions
 
-**Selecting Common Dimensions for multiple metrics**
+You can select common dimensions for multiple metrics. Use the following query to fetch the name and dimensions for multiple metrics: 
 
-```
+```bash
 select * from {{ 
-semantic_layer.dimensions(metrics=['food_order_amount', 'order_gross_profit'])
-}}
+	semantic_layer.dimensions(metrics=['food_order_amount', 'order_gross_profit'])
+	}}
 ``` 
 
+### Query grouped by time
 
-**Querying Metrics**
-Revenue and new customers grouped by time
+Use the following example query to fetch revenue and new customers grouped by time: 
 
-```
+```bash
 select * from {{
-semantic_layer.query(metrics=['food_order_amount','order_gross_profit'],									    group_by=['metric_time'])
-    }}
+	semantic_layer.query(metrics=['food_order_amount','order_gross_profit'], 
+	group_by=['metric_time'])
+	}}
 ``` 
 
-**Multiple metrics and adding granularity** 
+### Query with a time grain
 
-```
+Use the following example query to fetch multiple metrics with a change time dimension granularities:
+
+```bash
 select * from {{
-semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+	semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'], 
 	group_by=[Dimension('metric_time').grain('month')])
-    }}
+	}}
 ```
 
-**Grouping by a categorical dimension**
+### Group by categorical dimension
 
-```
+Use the following query to group by a categorical dimension:
+
+```bash
 select * from {{
-semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
-group_by=[Dimension('metric_time').grain('month'), 'customer__customer_type'])
-    }}
+	semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'], 
+	group_by=[Dimension('metric_time').grain('month'), 'customer__customer_type'])
+	}}
 ``` 
 
+### Query with a filter
 
-**Where Filter Example**
+Use the following example to query using a `where` filter:
 
-``` 
+```bash
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')")}}
+where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')")
+}}
 ``` 
 
-**Limit & Order**
+### Query with a limit and order_by
 
-```
+Use the following example to query using a `limit` or `order_by` clauses:
+
+```bash
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
   group_by=[Dimension('metric_time')],
   limit=10,
-  order_by=['order_gross_profit'])}}
+  order_by=['order_gross_profit'])
+  }}
 ``` 
+### Query with explain keyword
 
-**Explain a query**
+Use the following example to query using a `explain` keyword:
 
-```
+```bash
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 		group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
 		where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')",
 		explain=True)
-}}
+		}}
 ```
 
-## Remarks
+## FAQs
+
+- **Why do some dimensions use different syntax, like `metric_time` versus `[Dimension('metric_time')`?**<br />
+	When you select a dimension on its own, such as `metric_time` you don’t need the “Dimension” syntax. However, when you perform operations on the dimension, such as adding granularity, the object syntax (`[Dimension('metric_time')`) is required. 
 
-* You may notice that sometimes we use dimensions on their own – like metric_time, and sometimes we use the object syntax you see like Dimension(’metric_time’). When we are selecting the item alone (e.g., metric_time), we don’t require the “Dimension” syntax, but when we are operating on the object (e.g., adding granularity), the object syntax is required.
-* You may see some dimensions include the following syntax with a double underscore "__" . This denotes a mapping from an entity to a dimension, and this signifies where the dimension in question lives — example (user__country), which indicates someone is looking at the country dimension from the user table.
-* The default output for when you add granularity is {time_dimension_name}__{granularity_level}. If you are doing yearly granularity and the time dimension name is ds, you can expect ds__year.
+- **What does the double underscore `"__"` syntax in dimensions mean?**<br />
+	The double underscore `"__"` syntax indicates a mapping from an entity to a dimension, as well as where the dimension is located. For example, `user__country` means someone is looking at the `country` dimension from the `user` table.
 
+- **What is the default output when adding granularity?**<br />
+	The default output follows the format `{time_dimension_name}__{granularity_level}`. So for example, if the time dimension name is `ds` and the granularity level is yearly, the output is `ds__year`.
 

From 1455c2777228c5a1adb5c3fd23583836814e7576 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 07:54:58 -0600
Subject: [PATCH 078/703] Initial documentation for `thread_id` context
 variable (#3729)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

resolves #3614


[Preview](https://deploy-preview-3729--docs-getdbt-com.netlify.app/reference/dbt-jinja-functions/thread_id)

## What are you changing in this pull request and why?

https://github.com/dbt-labs/dbt-core/pull/7942 /
https://github.com/dbt-labs/dbt-core/issues/7941

This new `thread_id` context variable works in query tags because it's
set by the (Python) worker thread.

### Utility

[Comment](https://github.com/dbt-labs/dbt-core/pull/7942#issuecomment-1605237587):
> @NiallRees you're my hero.
>
> https://getdbt.slack.com/archives/C2JRRQDTL/p1656203829761049

## 🎩

<img width="700" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/11ab64e8-f257-4981-b072-aac0b7b434ed">

## Checklist
- [x] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Confirm that the bullet points are actually true
- [x] ~Add page to `website/sidebars.js`~ didn't do this because it
appeared unecessary
- [x] Provide a unique filename for the new page

---------

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/dbt-versions.js                          |  6 +++++-
 .../reference/dbt-jinja-functions/thread_id.md   | 16 ++++++++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)
 create mode 100644 website/docs/reference/dbt-jinja-functions/thread_id.md

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 44008bf98bf..d80521b19d5 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -36,7 +36,11 @@ exports.versionedPages = [
     "firstVersion": "1.6",
   },
   {
-    "page": "reference/resource-properties/deprecation_date", 
+    "page": "reference/dbt-jinja-functions/thread_id",
+    "firstVersion": "1.6",
+  },
+  {
+    "page": "reference/resource-properties/deprecation_date",
     "firstVersion": "1.6",
   },
   {
diff --git a/website/docs/reference/dbt-jinja-functions/thread_id.md b/website/docs/reference/dbt-jinja-functions/thread_id.md
new file mode 100644
index 00000000000..84eb32d5bff
--- /dev/null
+++ b/website/docs/reference/dbt-jinja-functions/thread_id.md
@@ -0,0 +1,16 @@
+---
+title: "About thread_id"
+sidebar_label: "thread_id"
+id: "thread_id"
+description: "The `thread_id` outputs an identifier for the current Python thread."
+---
+
+The `thread_id` outputs an identifier for the current Python thread that is executing a node, like `Thread-1`. 
+
+This value is useful when auditing or analyzing dbt invocation metadata. It corresponds to the `thread_id` within the [`Result` object](/reference/dbt-classes#result-objects) and [`run_results.json`](/reference/artifacts/run-results-json).
+
+If available, the `thread_id` is:
+- available in the compilation context of [`query-comment`](/reference/project-configs/query-comment)
+- included in the `info` dictionary in dbt [events and logs](/reference/events-logging#info)
+- included in the `metadata` dictionary in [dbt artifacts](/reference/artifacts/dbt-artifacts#common-metadata)
+- included as a label in all BigQuery jobs that dbt originates

From 2273ce480c0770be17624172413e74c41f2fa829 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 08:00:19 -0600
Subject: [PATCH 079/703] Stub for `graph_summary.json`

---
 website/docs/reference/artifacts/other-artifacts.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index 24950910688..1767b11f7f1 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -22,3 +22,9 @@ This file is used to store a compressed representation of files dbt has parsed.
 **Produced by:** commands supporting [node selection](/reference/node-selection/syntax)
 
 Stores the networkx representation of the dbt resource DAG.
+
+### graph_summary.json
+
+**Produced by:** TODO
+
+TODO

From 5a79e8846d07806f6953d3a9d62c981e83b20866 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 15:14:30 +0100
Subject: [PATCH 080/703] migration guide changes

---
 website/docs/guides/migration/sl-migration.md | 118 ++++++++++--------
 website/static/_redirects                     |   2 +
 2 files changed, 67 insertions(+), 53 deletions(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 7e9083ffe74..0c833006d13 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -1,99 +1,101 @@
 ---
-title: "dbt Semantic Layer migration guide"
+title: "Legacy dbt Semantic Layer migration guide"
 id: sl-migration
-sidebar_label: "dbt Semantic Layer migration"
+sidebar_label: "Legacy dbt Semantic Layer migration"
 description: "This migration guide will help you migrate from the legacy dbt Semantic Layer to the new one."
 tags: ["semantic-layer"]
 ---
 
-WILL CLEAN THIS UP
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and supported on dbt v1.6 or higher. It's available to all dbt Cloud plans (Developer, Team, and Enterprise) and introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
 
-add who the audience is, what they need to do, and what they need to know
+This guide is for users who have the legacy dbt Semantic Layer set up and would like to migrate to the new and revamped  [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). The legacy dbt Semantic Layer is supported on dbt v1.5 or lower, uses the dbt_metrics package, and will be deprecated. 
 
-**Technical resources:**
+**Note**: Migrating to the new semantic layer will require downtime and this guide will explain how to minimize it.
 
-- [Quickstart guide](/docs/build/sl-getting-started)
-- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
-- Configuring SL docs
-- SL API docs
-- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
-- Developer workflow demo
+## Migration components
 
-## Intro
+In order to migrate from the legacy semantic layer to the new one, there are three migration components you'll need to complete: 
 
-The legacy Semantic Layer is supported in dbt v1.5 or lower and will be deprecated.  The new Semantic Layer is in Beta and supported in dbt v1.6 or higher. If you’re a user of the legacy Semantic Layer and want to use the new one, follow this guide. NOTE: **This requires downtime of your Semantic Layer**
+- Create semantic models and migrate metric definitions
+- Setup the new dbt Semantic Layer in dbt Cloud
+- Update any applicable downstream integrations
 
-There are 3 migration components: 
+This guide assumes you have an environment with the legacy Semantic Layer enabled. There are two migration options for you to choose from, **both of which involve some downtime**:
 
-- Create semantic models and migrate metric definitions
+1. [Migrating an existing environment](#migrate-an-existing-environment) over to the new dbt Semantic Layer
+2. [Creating and configure a brand new environment](#creating-a-new-environment-running-the-new-semantic-layer) for the new dbt Semantic Layer
 
-- Setup new Semantic Layer in dbt Cloud
 
-- Update applicable downstream integrations
+## Migrate an existing environment
 
-This guide assumes you have an environment with the legacy Semantic Layer enabled. There are two migration paths available. **Note: Both migration paths involve some downtime:** 
+Switching to th new semantic layer requires a complete transition. This is because the legacy semantic layer will no longer work once you update your metrics to the new specifications in dbt v1.6 or higher. 
 
-1. Migrating an existing environment to the new Semantic Layer. 
-2. Creating a net new environment running the Semantic Layer.
+To avoid any disruptions or downtime, you _must_ set up the new semantic layer in your deployment environment after upgrading to the new metrics spec.
 
- 
+### Step 1: Migrate metrics
 
-## Migrate an existing environment
+Starting from dbt v1.6, the metrics specification dbt Core has been updated to support the integration of MetricFlow. This introduces two new components that are essential for building a semantic graph:
 
-This approach will be a hard cut to the new Semantic Layer. Once you update your metrics to the new spec in 1.6, the legacy Semantic Layer will no longer work. You will need to set up the semantic layer in your deployment environment after upgrading to the new metrics spec to avoid unnecessary downtime. 
+- [Semantic Models](/docs/build/semantic-models)  
+- [Metrics](/docs/build/metrics-overview)
 
-### **Step 1: Migrate Metric configs to the new spec.**
+To migrate to the new specification, you'll need to:
+- Create net new semantic model configs using YAML files in your dbt project
+- Upgrade your existing metrics configs to the new spec
 
-The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. We’ve introduced two new objects that MetricFlow expects in order to build a semantic graph: [Semantic Models](/docs/build/semantic-models) and [Metrics](/docs/build/metrics-overview). You will need to migrate your Metrics configs to the new spec. There are two steps to this process:
+To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
+**Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
-- Creating net new Semantic Model configs. These will be YAML files in your dbt project.
-- Upgrading the Metrics configs in your project to the new spec.
+For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-semantic-layer). 
 
-We’ve built a migration tool that automates this process for you. Check out the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) for installation instructions. Derived metrics are not supported in the migration tool, and will have to be migrated manually.  
+THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
 
-To learn how to configure Semantic Models and Metrics, check out our [getting started guide]/docs/build/sl-getting-started). **Note: Developing Metrics and Semantic Models in the IDE is not supported in the Beta release.**
+#### Test your metrics
 
-Once you’ve created your Semantic Models and created or updated your Metrics, you can test your updated configs using the MetricFlow CLI tools:
+Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools:
 
-1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`
-2. Run `dbt parse`. This will parse your project and create a semantic_manifest.json file in your target directory.
+1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
+2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
 3. Run `mf list metrics` to view the metrics in your project.
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-When you have tested your metrics and are satisfied that they are correct, commit your changes and push them to your dbt repository. **When you merge the new configurations, the legacy semantic layer will no longer work.** It is recommended that you complete step 2 as soon as you merge the changes to minimize downtime.
+Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy semantic layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new semantic layer.
 
-### **Step 2: Switch to the new Semantic Layer in your existing Cloud environment**
+### Step 2: Switch to the new Semantic Layer
 
-The suggested workflow for upgrading your Cloud environment to use the new semantic layer is as follows:
+Now that you've migrated your metrics and created semantic models, it's time to set up your environment using the new semantic layer configuration. 
 
-- Upgrade the existing deployment environment for your project to v1.6 or higher. Ensure that all jobs are running correctly with the new dbt version. Make any necessary updates to jobs that are impacted by the version upgrade.
-- To set up the Semantic Layer, you need to run a dbt job in your deployment environment. You can use any dbt command, but it must be successful.
-- To activate the new Semantic Layer, go to the ***Account Settings*** page and then **Project Details.** Select the specific project you want to enable the Semantic Layer for.
-- In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
-- After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. You can use this to connect to the Semantic Layer JDBC interface, which you will use in the next step. This interface allows you to connect and query your metrics in downstream tools.
-- Next, head back to the **Project Details** page and select **Generate Service Token** to create a semantic layer service token. Save this token for later.
-- You’re done! The Semantic Layer should now be enabled for your project! Time to start querying metrics.
+To upgrade your dbt Cloud environment to use the new semantic layer:
+
+1. [Upgrade](/docs/dbt-versions/upgrade-core-in-cloud) the existing deployment environment for your project to v1.6 or higher.
+Ensure that all jobs run successfully with the new version.
+2. Run a dbt job in your deployment environment to set up the new semantic layer. You can use any dbt command, as long as it completes successfully. 
+3. To activate the new Semantic Layer, go to the **Account Settings** page and select the specific project you want to enable the Semantic Layer for.
+3. On the **Project Details** page, select **Configure Semantic Layer** 
+4. Enter the connection details for the data platform and select the environment where you want to enable the semantic layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
+5. After you’ve entered your credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. You can use this to connect to the Semantic Layer JDBC interface, which you will use in the next step. This interface allows you to connect and query your metrics in downstream tools.
+6. Go back to the **Project Details** page and select **Generate Service Token** to create a semantic layer service token. Save this token for later.
+7. You’re done 🎉! The new dbt Semantic Layer is now enabled for your project an you can start querying metrics.
 
 ## Step 3: Update connection in downstream integrations
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
-**Migration guide for Hex**
+**Link to migration guide for Hex**
 
-…
 
-**Migration guide for Mode**
 
-…
+**Link to migration guide for Mode**
+
 
-## **Creating a new environment running the new Semantic Layer**
+## Create environment for the new Semantic Layer
 
-This approach will let you run the legacy Semantic Layer in your existing environment while you stage the metrics spec changes to your dbt project. You’ll set up a brand new deployment environment on dbt v1.6 or higher, which is essentially a clone of your current Semantic Layer environment. When you’re ready to merge your changes, the environment is already set up, and you can safely deprecate your old environment. This approach still involves downtime.
+This approach allows you to run the legacy semantic layer in your existing environment, while you stage the metrics spec changes to your dbt project. In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current semantic layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
 
-### **Step 1: Setup a new environment running 1.6 and enable the SL**
+### Step 1: Setup a new environment
 
-You will need to create a new environment for the Semantic Layer and migrate your existing jobs to this environment. The workflow is as follows:
+You'll need to create a new environment for the new semantic layer and migrate your existing jobs to this environment. The workflow is as follows:
 
 - In dbt Cloud, select **Deploy** and then **Environments** to create a new environment.
 - Select **dbt Version 1.6** and enter your deployment credentials.
@@ -105,7 +107,7 @@ You will need to create a new environment for the Semantic Layer and migrate you
 - Next, head back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
 - You’re done! The semantic layer should now be enabled for your project! The legacy Semantic Layer will still be running in your old environment.
 
-### **Step 2: Migrate metric configs to the new spec.**
+### Step 2: Migrate metric configs to the new spec
 
 The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. We’ve introduced two new objects that MetricFlow expects in order to build a semantic graph: [Semantic Models](/docs/build/semantic-models) and [Metrics](/docs/build/metrics-overview). You will need to migrate your Metrics configs to the new spec. The process broadly looks:
 
@@ -130,10 +132,20 @@ When you have tested your metrics and are satisfied that they are correct, commi
 
 Now that your semantic layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
-**Migration guide for Hex**
+**Link to migration guide for Hex**
 
 …
 
-**Migration guide for Mode**
+**Link to migration guide for Mode**
 
 …
+
+
+## Resources
+
+- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
+- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
+- [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)
+- [Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
+- Developer workflow demo
diff --git a/website/static/_redirects b/website/static/_redirects
index 8f5d274d3f3..a9add52bac4 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,5 +1,7 @@
 ## semantic layer
 
+
+/docs/build/sl-getting-started  /docs/use-dbt-semantic-layer/quickstart-semantic-layer 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
 /docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
 /docs/use-dbt-semantic-layer/quickstart-semantic-layer /docs/use-dbt-semantic-layer/quickstart-sl 301

From 4969fe4d1d98ab53657cfe9ed915464bfdfef520 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 15:34:22 +0100
Subject: [PATCH 081/703] migration guide fromatting

---
 website/docs/docs/build/metrics.md            |  2 +-
 .../docs/use-dbt-semantic-layer/setup-sl.md   |  2 +-
 website/docs/guides/migration/sl-migration.md | 61 +++++++++++--------
 3 files changed, 38 insertions(+), 27 deletions(-)

diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index 428844e0b96..d605ef590b3 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -707,6 +707,6 @@ The above example will return a dataset that contains the metric provided in the
 </VersionBlock>
 
 <Snippet path="discourse-help-feed-header" />
+
 <DiscourseHelpFeed tags="metrics"/>
 
-</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 402e6a0691b..32d94fc8561 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -52,7 +52,7 @@ With the dbt Semantic Layer, you'll be able to centrally define business metrics
 
 Before you set up the dbt Semantic Layer, make sure you meet the following:
 
-<Snippet src="sl-prerequisites" />
+<Snippet path="sl-prerequisites" />
 
 ## Set up dbt Semantic Layer
 
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 0c833006d13..06e8371d0b4 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -78,7 +78,7 @@ Ensure that all jobs run successfully with the new version.
 6. Go back to the **Project Details** page and select **Generate Service Token** to create a semantic layer service token. Save this token for later.
 7. You’re done 🎉! The new dbt Semantic Layer is now enabled for your project an you can start querying metrics.
 
-## Step 3: Update connection in downstream integrations
+### Step 3: Update connection in downstream integrations
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
@@ -91,55 +91,66 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 ## Create environment for the new Semantic Layer
 
-This approach allows you to run the legacy semantic layer in your existing environment, while you stage the metrics spec changes to your dbt project. In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current semantic layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
+This approach allows you to run the legacy semantic layer in your existing environment, while you stage the metrics spec changes to your dbt project. 
+
+In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current semantic layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
 
 ### Step 1: Setup a new environment
 
-You'll need to create a new environment for the new semantic layer and migrate your existing jobs to this environment. The workflow is as follows:
+You'll need to create a new environment for the new semantic layer and migrate your existing jobs to this environment:
+
+1. In dbt Cloud, select **Deploy** and then **Environments** to create a new [deployment environment](/docs/deploy/deploy-environments).
+2. Select **dbt Version 1.6** (or the latest) and enter your deployment credentials.
+    * There needs to be a successful run in your new environment to configure the  semantic layer. We recommend running `dbt ls`, since `dbt build` won’t succeed until you’ve updated your metrics configs.
+3. You'll need to migrate your jobs from your old deployment environment. You can do this by manually creating and configuring the new jobs in the new environment.
+4. To activate the new Semantic Layer, go to the Account Settings page and select the specific project you want to enable the Semantic Layer for.
+5. On the **Project Details** page, select **Configure Semantic Layer**.
+6. Enter the connection details for the data platform and select the environment where you want to enable the semantic layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
+7. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+7. Go back to **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
+8. You’re done ✨ ! The semantic layer should now be enabled for your project! 
 
-- In dbt Cloud, select **Deploy** and then **Environments** to create a new environment.
-- Select **dbt Version 1.6** and enter your deployment credentials.
-- There needs to be a successful run in your new environment to configure the Semantic Layer. We recommend running `dbt ls` as a `dbt build` won’t succeed until you’ve updated your metrics configs.
-- Migrate your jobs from your old deployment environment.
-- To activate the new Semantic Layer, go to the **Account Settings** page and then **Project Details.** Select the specific project you want to enable the Semantic Layer for.
-- In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
-- After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
-- Next, head back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
-- You’re done! The semantic layer should now be enabled for your project! The legacy Semantic Layer will still be running in your old environment.
+The legacy semantic layer will still be running in your old environment so make sure you complete step two to migrate your metrics configs to the new spec. Once you’ve migrated your metrics configs, you can safely deprecate the old environment.
 
 ### Step 2: Migrate metric configs to the new spec
 
-The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. We’ve introduced two new objects that MetricFlow expects in order to build a semantic graph: [Semantic Models](/docs/build/semantic-models) and [Metrics](/docs/build/metrics-overview). You will need to migrate your Metrics configs to the new spec. The process broadly looks:
+Starting from dbt v1.6, the metrics specification dbt Core has been updated to support the integration of MetricFlow. This introduces two new components that are essential for building a semantic graph:
+
+- [Semantic Models](/docs/build/semantic-models)  
+- [Metrics](/docs/build/metrics-overview)
+
+To migrate to the new specification, you'll need to:
+- Create net new semantic model configs using YAML files in your dbt project
+- Upgrade your existing metrics configs to the new spec
+
+To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
+**Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
-- Creating net new Semantic Model configs. These will be YAML files in your dbt project.
-- Upgrading the Metrics configs in your project to the new spec.
+For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-semantic-layer). 
 
-We’ve built a migration tool that automates this process for you. Check out the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) for installation instructions. Derived metrics are not supported in the migration tool, and will have to be migrated manually.  
+THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
 
-To learn how to configure Semantic Models and Metrics, check out our [getting started guide](/docs/build/sl-getting-started). **Note: Developing Metrics and Semantic Models in the IDE is not supported in the Beta release.**
+#### Test your metrics
 
-Once you’ve created your Semantic Models and created or updated your Metrics, you can test your updated configs using the MetricFlow CLI tools:
+Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools:
 
-1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`
-2. Run `dbt parse` . This will parse your project, and create a semantic_manifest.json file in your target directory.
+1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
+2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
 3. Run `mf list metrics` to view the metrics in your project.
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-When you have tested your metrics and are satisfied that they are correct, commit your changes and push them to your dbt repository. **When you merge the new configurations, the legacy Semantic Layer will no longer work**
+Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy semantic layer will no longer work**. 
 
-## Step 3: Update connection in downstream integrations
+### Step 3: Update connection in downstream integrations
 
 Now that your semantic layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
 **Link to migration guide for Hex**
 
-…
 
 **Link to migration guide for Mode**
 
-…
-
 
 ## Resources
 

From a43cd4c667203657cc4b2939ea9ed4d0650a99be Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 14 Jul 2023 15:39:43 +0100
Subject: [PATCH 082/703] fix link

---
 website/docs/guides/migration/sl-migration.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 06e8371d0b4..371f94bea22 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -46,7 +46,7 @@ To migrate to the new specification, you'll need to:
 To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
 **Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
-For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-semantic-layer). 
+For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
 THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
 
@@ -126,7 +126,7 @@ To migrate to the new specification, you'll need to:
 To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
 **Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
-For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-semantic-layer). 
+For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
 THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
 

From 98ff0b09ea909f5dc80718131ca2aae3e3265953 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 08:44:46 -0600
Subject: [PATCH 083/703] Fix link

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/reference/node-selection/syntax.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/node-selection/syntax.md b/website/docs/reference/node-selection/syntax.md
index 7c55ee6e6b6..6789067aa02 100644
--- a/website/docs/reference/node-selection/syntax.md
+++ b/website/docs/reference/node-selection/syntax.md
@@ -91,7 +91,7 @@ dbt can leverage artifacts from a prior invocation as long as their file path is
 - [The `state:` selector](/reference/node-selection/methods#the-state-method), whereby dbt can identify resources that are new or modified
 by comparing code in the current project against the state manifest.
 - [Deferring](/reference/node-selection/defer) to another environment, whereby dbt can identify upstream, unselected resources that don't exist in your current environment and instead "defer" their references to the environment provided by the state manifest.
-- The [`dbt clone` command](website/docs/reference/commands/clone.md), whereby dbt can clone nodes based on their location in the manifest provided to the `--state` flag.
+- The [`dbt clone` command](/reference/commands/clone), whereby dbt can clone nodes based on their location in the manifest provided to the `--state` flag.
 
 Together, the `state:` selector and deferral enable ["slim CI"](/guides/legacy/best-practices#run-only-modified-models-to-test-changes-slim-ci). We expect to add more features in future releases that can leverage artifacts passed to the `--state` flag.
 

From 184a0858316be121166629984ccb9ab06dbe1b7c Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 08:46:25 -0600
Subject: [PATCH 084/703] Update link

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/reference/commands/clone.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index e0e92d52bc7..942932f63f9 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -11,7 +11,7 @@ The `dbt clone` command clones selected nodes from the specified state to the ta
 Note: 
 - The state to clone from is based on the location of nodes in the manifest provided to `--state`.
 - By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 
-- You may want to specify a higher number of [threads](website/docs/docs/running-a-dbt-project/using-threads.md) to decrease execution time since individual clone statements are independent of one another.
+- You may want to specify a higher number of [threads](/docs/running-a-dbt-project/using-threads) to decrease execution time since individual clone statements are independent of one another.
 
 The `clone` command is useful for:
 - blue/green deployment

From 14d9c4c1f36d0087bdaa1d543b51b72efb90b628 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 08:48:38 -0600
Subject: [PATCH 085/703] Update explanations for zero-copy cloning (or not)

Co-authored-by: Jeremy Cohen <jeremy@dbtlabs.com>
---
 website/docs/reference/commands/clone.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 942932f63f9..196748deef1 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -5,8 +5,8 @@ id: "clone"
 ---
 
 The `dbt clone` command clones selected nodes from the specified state to the target schema(s). This command makes use of the `clone` materialization:
-- If using a data warehouses that support zero-copy cloning of tables, selected models that are tables will be created as clones
-- For views and warehouses that do not support zero-copy cloning, selected models will be created as simple `select * from ...` pointer views
+- If your data platform supports zero-copy cloning of tables, and this model exists as a table in the other environment, dbt will create it in your environment as a clone
+- Otherwise, dbt will create a simple pointer view (`select * from` the other object)
 
 Note: 
 - The state to clone from is based on the location of nodes in the manifest provided to `--state`.

From a8a849d114d5fe82811fbfc11300819ab329deaf Mon Sep 17 00:00:00 2001
From: anais vaillant <availlant46@gmail.com>
Date: Fri, 14 Jul 2023 08:47:21 -0700
Subject: [PATCH 086/703] Update sources.md

hi team, link is currently broken for source configurations.  when you click on the current link it brings you to (https://docs.getdbt.com/docs/build/reference/source-configs) instead of (https://docs.getdbt.com/reference/source-configs)

I think all that's missing is a `/`?
---
 website/docs/docs/build/sources.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sources.md b/website/docs/docs/build/sources.md
index d1b269cdd13..a657b6257c9 100644
--- a/website/docs/docs/build/sources.md
+++ b/website/docs/docs/build/sources.md
@@ -8,7 +8,7 @@ search_weight: "heavy"
 
 ## Related reference docs
 * [Source properties](/reference/source-properties)
-* [Source configurations](reference/source-configs)
+* [Source configurations](/reference/source-configs)
 * [`{{ source() }}` jinja function](/reference/dbt-jinja-functions/source)
 * [`source freshness` command](/reference/commands/source)
 

From b1f734f3c57d88726b50ac5dd43adf3b302dbbf1 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 10:17:04 -0600
Subject: [PATCH 087/703] Link to docs for specifying / establishing state

---
 website/docs/reference/commands/clone.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 196748deef1..30a26f8d846 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -4,7 +4,7 @@ sidebar_label: "clone"
 id: "clone"
 ---
 
-The `dbt clone` command clones selected nodes from the specified state to the target schema(s). This command makes use of the `clone` materialization:
+The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). This command makes use of the `clone` materialization:
 - If your data platform supports zero-copy cloning of tables, and this model exists as a table in the other environment, dbt will create it in your environment as a clone
 - Otherwise, dbt will create a simple pointer view (`select * from` the other object)
 

From 3f46631d4a2d74b3e35f227b42954065fac1c74c Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 10:17:24 -0600
Subject: [PATCH 088/703] Remove note in favor of link to docs for
 "establishing state"

---
 website/docs/reference/commands/clone.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 30a26f8d846..6a304d9050a 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -7,9 +7,6 @@ id: "clone"
 The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). This command makes use of the `clone` materialization:
 - If your data platform supports zero-copy cloning of tables, and this model exists as a table in the other environment, dbt will create it in your environment as a clone
 - Otherwise, dbt will create a simple pointer view (`select * from` the other object)
-
-Note: 
-- The state to clone from is based on the location of nodes in the manifest provided to `--state`.
 - By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 
 - You may want to specify a higher number of [threads](/docs/running-a-dbt-project/using-threads) to decrease execution time since individual clone statements are independent of one another.
 

From 0087b7a30a6a303eec38d5799d0f1f92ff588e6c Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 13:48:17 -0400
Subject: [PATCH 089/703] add images and date var

---
 .../components/communitySpotlightList/index.js |   5 +++++
 .../community/spotlight/alan-cruickshank.jpg   | Bin 0 -> 284343 bytes
 .../img/community/spotlight/fabiyi-opeyemi.jpg | Bin 0 -> 235503 bytes
 .../community/spotlight/faith-lierheimer.jpg   | Bin 0 -> 169906 bytes
 .../img/community/spotlight/jing-lim.jpg       | Bin 0 -> 302008 bytes
 .../img/community/spotlight/josh-devlin.jpg    | Bin 0 -> 151431 bytes
 .../img/community/spotlight/owen-prough.jpg    | Bin 0 -> 130821 bytes
 7 files changed, 5 insertions(+)
 create mode 100644 website/static/img/community/spotlight/alan-cruickshank.jpg
 create mode 100644 website/static/img/community/spotlight/fabiyi-opeyemi.jpg
 create mode 100644 website/static/img/community/spotlight/faith-lierheimer.jpg
 create mode 100644 website/static/img/community/spotlight/jing-lim.jpg
 create mode 100644 website/static/img/community/spotlight/josh-devlin.jpg
 create mode 100644 website/static/img/community/spotlight/owen-prough.jpg

diff --git a/website/src/components/communitySpotlightList/index.js b/website/src/components/communitySpotlightList/index.js
index d91c257122d..46ea43c84e7 100644
--- a/website/src/components/communitySpotlightList/index.js
+++ b/website/src/components/communitySpotlightList/index.js
@@ -8,6 +8,11 @@ import CommunitySpotlightCard from '../communitySpotlightCard'
 const communityTitle = 'Community spotlight'
 const communityDescription = "The dbt Community is where analytics engineering lives and grows, and you're a part of it! Every quarter we'll be highlighting community members in the dbt Community Spotlight. These are individuals who have gone above and beyond to contribute to the community in a variety of ways. We all see you. We appreciate you. You are awesome."
 
+// This date determines where the 'Previously on the Spotlight" text will show.
+// Any spotlight members with a 'dateCreated' field before this date
+// will be under the 'Previously..' header.
+const currentSpotlightDate = new Date('2023-06-01') 
+
 function CommunitySpotlightList({ spotlightData }) {
   const { siteConfig } = useDocusaurusContext()
 
diff --git a/website/static/img/community/spotlight/alan-cruickshank.jpg b/website/static/img/community/spotlight/alan-cruickshank.jpg
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literal 0
HcmV?d00001


From 4e8b7613a4a9bb493fb3f530c9b0cd030f3fbc8a Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 12:50:33 -0600
Subject: [PATCH 090/703] Versioning components

---
 website/docs/reference/artifacts/other-artifacts.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index 1767b11f7f1..fc5b0faaaf1 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -25,6 +25,14 @@ Stores the networkx representation of the dbt resource DAG.
 
 ### graph_summary.json
 
+<VersionBlock lastVersion="1.5">
+
+:::info New functionality
+This functionality is new in v1.6. You should upgrade to 1.6 or later if you want to use this artifact.
+:::
+
+</VersionBlock>
+
 **Produced by:** TODO
 
 TODO

From 8da5a89f21f78c0131df00ab96988afc6329ccb5 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 15:16:22 -0400
Subject: [PATCH 091/703] add first new spotlight member and adjust truncate
 util

---
 .../docs/community/spotlight/josh-devlin.md   | 39 +++++++++++++++++++
 .../communitySpotlightCard/index.js           | 16 +++++++-
 2 files changed, 53 insertions(+), 2 deletions(-)
 create mode 100644 website/docs/community/spotlight/josh-devlin.md

diff --git a/website/docs/community/spotlight/josh-devlin.md b/website/docs/community/spotlight/josh-devlin.md
new file mode 100644
index 00000000000..1a1db622209
--- /dev/null
+++ b/website/docs/community/spotlight/josh-devlin.md
@@ -0,0 +1,39 @@
+---
+id: josh-devlin
+title: Josh Devlin
+description: |
+  After "discovering" dbt in early 2020, I <a href="https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___" title="joined the community" rel="noopener noreferrer" target="_blank">joined the community</a> and used it as a learning tool while I tried to get dbt introduced at my company. By helping others, I learned about common pitfalls, best practices, and the breadth of the tool. When it came time to implement it months later, I already felt like an expert!
+
+  In December 2020 I attended the first virtual Coalesce conference, attending all 4 days across 3 time zones! I found my quirky-nerdy-purple-people, and felt at home.
+
+  3 years later I had the pleasure of presenting at my first <a href="https://www.meetup.com/sydney-dbt-meetup/" title="dbt Meetup in Sydney" rel="noopener noreferrer" target="_blank">dbt Meetup in Sydney</a>, and then <a href="https://coalesce.getdbt.com/blog/babies-and-bathwater-is-kimball-still-relevant" title="at the first in-person Coalesce" rel="noopener noreferrer" target="_blank">at the first in-person Coalesce</a> in New Orleans. My passion is helping people, and I'm glad that the dbt community gives me a place to do that!
+image: /img/community/spotlight/josh-devlin.jpg
+pronouns: he/him
+location: Melbourne, Australia (but spent most of the last decade in Houston, USA)
+jobTitle: Senior Analytics Engineer
+companyName: Canva
+organization: ""
+socialLinks:
+  - name: Twitter
+    link: https://twitter.com/JayPeeDevlin
+  - name: LinkedIn
+    link: https://www.linkedin.com/in/josh-devlin/
+dateCreated: 2023-06-27
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I have been a subscriber to 'The Data Science Roundup' (now ['The Analytics Engineering Roundup'](https://roundup.getdbt.com/)) since its inception, so I knew that dbt existed from the very beginning, since the time that dbt Labs was still called Fishtown Analytics.  Despite that, I never really understood what the tool was or how it fit in until early 2020 when I first started experimenting with the tool.  I immediately joined the community and found it warm and welcoming, so I started to help people where I could and never stopped!
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+I like to think I represent the warm, helpful vibes of the early days of the Community, where folks like Claire Carroll warmly welcomed myself and others!
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+I've learned that the more you give, the more you get. I've put hundreds of hours into helping other people in the community, but I've gotten all that back and much more. I hope I can encourage others to give of themselves and reap the rewards later!
+
+## Anything else interesting you want to tell us?
+
+In a previous life I was an orchestral musician!
diff --git a/website/src/components/communitySpotlightCard/index.js b/website/src/components/communitySpotlightCard/index.js
index 5be1179d620..804dcf62bc4 100644
--- a/website/src/components/communitySpotlightCard/index.js
+++ b/website/src/components/communitySpotlightCard/index.js
@@ -100,7 +100,8 @@ function CommunitySpotlightCard({ frontMatter, isSpotlightMember = false }) {
           </div>
         )}
         {description && !isSpotlightMember && (
-          <p className={styles.spotlightMemberDescription}>{truncateText(description)}</p>
+          // <p className={styles.spotlightMemberDescription}>{truncateText(description)}</p>
+          <p className={styles.spotlightMemberDescription} dangerouslySetInnerHTML={{__html: truncateText(description)}} />
         )}
         {socialLinks && isSpotlightMember && socialLinks?.length > 0 && (
           <div className={styles.spotlightMemberSocial}>
@@ -137,7 +138,18 @@ function CommunitySpotlightCard({ frontMatter, isSpotlightMember = false }) {
 // Truncate text
 function truncateText(str) {
   // Max length of string
-  const maxLength = 300
+  let maxLength = 300
+
+  // Get initial substring to check for link html
+  let substring = str?.substring(0, maxLength - 3)
+
+  // Exclude link html from content length count
+  // Otherwise href, title, rel values all counted as text.
+  const linkText = substring.match(/(?<=<a ).*(?=<\/a>)/g)
+  linkText?.length && linkText.map(l => {
+    if(l?.length) maxLength += l.length
+  })
+
   return str.length > maxLength
     ? `${str.substring(0, maxLength - 3)}...`
     : str

From 8db9a1ca79f00ee7c08e1d524fd5775b30634c78 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 15:20:02 -0400
Subject: [PATCH 092/703] update util

---
 .../src/components/communitySpotlightCard/index.js   | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/website/src/components/communitySpotlightCard/index.js b/website/src/components/communitySpotlightCard/index.js
index 804dcf62bc4..a6e66ccb4c7 100644
--- a/website/src/components/communitySpotlightCard/index.js
+++ b/website/src/components/communitySpotlightCard/index.js
@@ -100,7 +100,6 @@ function CommunitySpotlightCard({ frontMatter, isSpotlightMember = false }) {
           </div>
         )}
         {description && !isSpotlightMember && (
-          // <p className={styles.spotlightMemberDescription}>{truncateText(description)}</p>
           <p className={styles.spotlightMemberDescription} dangerouslySetInnerHTML={{__html: truncateText(description)}} />
         )}
         {socialLinks && isSpotlightMember && socialLinks?.length > 0 && (
@@ -140,15 +139,12 @@ function truncateText(str) {
   // Max length of string
   let maxLength = 300
 
-  // Get initial substring to check for link html
-  let substring = str?.substring(0, maxLength - 3)
-
   // Exclude link html from content length count
   // Otherwise href, title, rel values all counted as text.
-  const linkText = substring.match(/(?<=<a ).*(?=<\/a>)/g)
-  linkText?.length && linkText.map(l => {
-    if(l?.length) maxLength += l.length
-  })
+  const linkText = str.match(/(?<=<a ).*(?=<\/a>)/g)
+  if(linkText?.length && linkText[0]?.length) {
+    maxLength += linkText[0]?.length
+  }
 
   return str.length > maxLength
     ? `${str.substring(0, maxLength - 3)}...`

From 4ad42668d0febef27dc747714268dd11934479d2 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 15:29:06 -0400
Subject: [PATCH 093/703] update util v2

---
 .../components/communitySpotlightCard/index.js | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/website/src/components/communitySpotlightCard/index.js b/website/src/components/communitySpotlightCard/index.js
index a6e66ccb4c7..06b77aa2be2 100644
--- a/website/src/components/communitySpotlightCard/index.js
+++ b/website/src/components/communitySpotlightCard/index.js
@@ -139,15 +139,25 @@ function truncateText(str) {
   // Max length of string
   let maxLength = 300
 
+  // Check if anchor link starts within first 300 characters
+  let hasLinks = false
+  if(str.substring(0, maxLength - 3).match(/(?:<a)/g)) {
+    hasLinks = true
+  }
+
   // Exclude link html from content length count
   // Otherwise href, title, rel values all counted as text.
-  const linkText = str.match(/(?<=<a ).*(?=<\/a>)/g)
-  if(linkText?.length && linkText[0]?.length) {
-    maxLength += linkText[0]?.length
+  if(hasLinks) {
+    const linkText = str.match(/(?<=<a ).*(?=<\/a>)/g)
+    if(linkText?.length && linkText[0]?.length) {
+      maxLength += linkText[0]?.length
+    }
   }
 
+  const substring = str.substring(0, maxLength - 3)
+
   return str.length > maxLength
-    ? `${str.substring(0, maxLength - 3)}...`
+    ? `${substring}...`
     : str
 }
 

From 223f8fbe4b7175a9f4054995e12c56f50580c774 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:36:31 -0600
Subject: [PATCH 094/703] Update earliest version for materialized views in
 dbt-bigquery

---
 website/docs/reference/resource-configs/bigquery-configs.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/website/docs/reference/resource-configs/bigquery-configs.md b/website/docs/reference/resource-configs/bigquery-configs.md
index b3c05c1d827..c447859ba61 100644
--- a/website/docs/reference/resource-configs/bigquery-configs.md
+++ b/website/docs/reference/resource-configs/bigquery-configs.md
@@ -764,6 +764,8 @@ Views with this configuration will be able to select from objects in `project_1.
 
 The `grant_access_to` config is not thread-safe when multiple views need to be authorized for the same dataset. The initial `dbt run` operation after a new `grant_access_to` config is added should therefore be executed in a single thread. Subsequent runs using the same configuration will not attempt to re-apply existing access grants, and can make use of multiple threads.
 
+<VersionBlock firstVersion="1.6">
+
 ## Materialized view
 
 The BigQuery adapter supports [materialized views](https://cloud.google.com/bigquery/docs/materialized-views-intro) and refreshes them for every subsequent `dbt run` you execute. For more information, see [Refresh Materialized Views](https://cloud.google.com/bigquery/docs/materialized-views-manage#refresh) in the Google docs.
@@ -804,3 +806,5 @@ models:
     materialized: materialized_view
 ```
 </File>
+
+</VersionBlock>

From b02f029436cffbd0f844b1f4d04d6d965c9c4aac Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:46:49 -0600
Subject: [PATCH 095/703] Update earliest version for materialized views in
 dbt-postgres

---
 website/docs/reference/resource-configs/postgres-configs.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index cee1a9861d4..eb9108ad431 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -105,6 +105,8 @@ models:
 
 </File>
 
+<VersionBlock firstVersion="1.6">
+
 ## Materialized view
 
 The Postgres adapter supports [materialized views](https://www.postgresql.org/docs/current/rules-materializedviews.html) and refreshes them for every subsequent `dbt run` you execute. For more information, see [Refresh Materialized Views](https://www.postgresql.org/docs/15/sql-refreshmaterializedview.html) in the Postgres docs.
@@ -144,4 +146,6 @@ models:
   path:
     materialized: materialized_view
 ```
-</File>
\ No newline at end of file
+</File>
+
+</VersionBlock>

From 2ab877922acca24131750d2a48f80f8674369547 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:47:04 -0600
Subject: [PATCH 096/703] Update earliest version for materialized views in
 dbt-redshift

---
 website/docs/reference/resource-configs/redshift-configs.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 00da3130f75..a0ebf7e88df 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -96,6 +96,8 @@ models:
 
 </File>
 
+<VersionBlock firstVersion="1.6">
+
 ## Materialized view
 
 The Redshift adapter supports [materialized views](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-overview.html) and refreshes them for every subsequent `dbt run` that you execute. For more information, see [Refresh Materialized Views](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html) in the Redshift docs.
@@ -132,4 +134,6 @@ models:
   path:
     materialized: materialized_view
 ```
-</File>
\ No newline at end of file
+</File>
+
+</VersionBlock>

From 953608e5fb1c34bf64648bdda0547d857f118f31 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:52:32 -0600
Subject: [PATCH 097/703] Explain purpose and structure of `graph_summary.json`

---
 website/docs/reference/artifacts/other-artifacts.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index fc5b0faaaf1..c0e72109f78 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -35,4 +35,8 @@ This functionality is new in v1.6. You should upgrade to 1.6 or later if you wan
 
 **Produced by:** TODO
 
-TODO
+This file is useful for investigating performance issues in Core's graph algorithms.
+
+It is more anonymized and compact than [`manifest.json`](/reference/artifacts/manifest-json) and [`graph.gpickle`](#graph.gpickle).
+
+It contains only the `name` and `type` of each node along with ids of its child nodes (`succ`). It includes that information at two separate points in time: Immediately after the graph is linked together (`linked`), and then after test edges have been added (`with_test_edges`).

From d970930d67560a167d7dc58b963b292fb302a45c Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 15:55:44 -0400
Subject: [PATCH 098/703] add content for new members

---
 .../community/spotlight/alan-cruickshank.md   | 41 ++++++++++++++++
 .../community/spotlight/fabiyi-opeyemi.md     | 41 ++++++++++++++++
 .../community/spotlight/faith-lierheimer.md   | 47 +++++++++++++++++++
 .../docs/community/spotlight/jing-yu-lim.md   | 41 ++++++++++++++++
 .../docs/community/spotlight/owen-prough.md   | 41 ++++++++++++++++
 5 files changed, 211 insertions(+)
 create mode 100644 website/docs/community/spotlight/alan-cruickshank.md
 create mode 100644 website/docs/community/spotlight/fabiyi-opeyemi.md
 create mode 100644 website/docs/community/spotlight/faith-lierheimer.md
 create mode 100644 website/docs/community/spotlight/jing-yu-lim.md
 create mode 100644 website/docs/community/spotlight/owen-prough.md

diff --git a/website/docs/community/spotlight/alan-cruickshank.md b/website/docs/community/spotlight/alan-cruickshank.md
new file mode 100644
index 00000000000..763b0f917d0
--- /dev/null
+++ b/website/docs/community/spotlight/alan-cruickshank.md
@@ -0,0 +1,41 @@
+---
+id: alan-cruickshank
+title: Alan Cruickshank
+description: |
+  I've been around in the dbt community, especially the <a href="https://www.meetup.com/london-dbt-meetup/" title="London dbt Meetup" rel="noopener noreferrer" target="_blank">London dbt Meetup</a>, since early 2019—around the time that we started using dbt at tails.com. My background is the startup/scaleup space and building data teams in a context where there is a lot of growth going on but there isn't a lot of money around to support that. That's a topic that I've written and spoken about on several occasions on podcasts, blogposts and even at Coalesce <a href="https://www.getdbt.com/coalesce-2020/presenting-sqlfluff/" title="2020" rel="noopener noreferrer" target="_blank">2020</a> and <a href="https://www.getdbt.com/coalesce-2021/this-is-just-the-beginning/" title="2021" rel="noopener noreferrer" target="_blank">2021</a>!
+
+  Aside from my work at tails.com, my other main focus at the moment is <a href="https://sqlfluff.com/" title="SQLFluff" rel="noopener noreferrer" target="_blank">SQLFluff</a>, the open source SQL linter which I started developing as part of a hackday at tails.com in late 2019 and now is the most starred SQL linter on Github with almost 1M downloads a month.
+image: /img/community/spotlight/alan-cruickshank.jpg
+pronouns: he/him
+location: London, UK
+jobTitle: Insights Director
+companyName: tails.com
+organization: Author & Maintainer of SQLFluff
+socialLinks:
+  - name: LinkedIn
+    link: https://www.linkedin.com/in/amcruickshank/
+  - name: SQLFluff
+    link: sqlfluff.com
+dateCreated: 2023-06-30
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I [joined the community](https://www.getdbt.com/community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___) in 2019 and it's been an invaluable source of advice and wisdom, especially operating on the bleeding edge of open source data tooling. It's been a place to meet like-minded people, even find new colleagues and certainly one of the places I look to when thinking about how to approach hairy data problems.
+
+In London it's also been one of the most vibrant meetup groups in person, compared to many others which are either very, very specialized or more focussed on larger organisations.
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+I just want to be useful 😁. I've learned a lot from the community over the years, and now I want to be able to give back to it. My primary vehicle for that is SQLFluff - both as a tool for the community to use, but also as a way of encouraging a wider group of people to feel welcome and able to contribute to open source software and build the tools of the future.
+
+I also see SQLFluff as a vehicle to drive more consistency in the way we write SQL, and through that drive better communication and lower the barrier for new people to enter this field and find their own success.
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+For better or worse, I spend most of my day job on people and organisational things, less on how to solve individual problems, and more on how to enable and support groups of people in being able to make great decisions themselves. In some ways, if I have to touch the keyboard too much, it's a sign that I've failed in that calling. dbt itself is a tool which enables better collaboration—and the community is full of people with great ideas on how to better enable other people around us. I hope that I'm able to pass some of that knowledge and the experience of applying it in a scaleup environment back to others also treading this path.
+
+## Anything else interesting you want to tell us?
+
+If you're not using SQLFluff on your dbt project, you probably should be: https://github.com/sqlfluff/sqlfluff
diff --git a/website/docs/community/spotlight/fabiyi-opeyemi.md b/website/docs/community/spotlight/fabiyi-opeyemi.md
new file mode 100644
index 00000000000..dee785cda11
--- /dev/null
+++ b/website/docs/community/spotlight/fabiyi-opeyemi.md
@@ -0,0 +1,41 @@
+---
+id: fabiyi-opeyemi
+title: Opeyemi Fabiyi
+description: |
+  I'm an Analytics Engineer with Data Culture, a Data Consulting firm where I use dbt regularly to help clients build quality-tested data assets. I've also got a background in financial services and supply chain. I'm passionate about helping organizations to become data-driven and I majorly use dbt for data modeling, while the other aspect of the stack is largely dependent on the client infrastructure I'm working for, so I often say I'm tool-agnostic. 😀
+
+  I'm the founder of Nigeria's Young Data Professional Community. I'm also the organizer of the <a href="https://www.meetup.com/lagos-dbt-meetup/" title="Lagos dbt Meetup" rel="noopener noreferrer" target="_blank">Lagos dbt Meetup</a> which I started, and one of the organizers of the DataFest Africa Conference. I became an active member of the dbt Community in 2021 & <a href="https://coalesce.getdbt.com/blog/how-to-leverage-dbt-community-as-the-first-and-only-data-hire-to-survive" title="spoke at Coalesce 2022" rel="noopener noreferrer" target="_blank">spoke at Coalesce 2022</a>.
+image: /img/community/spotlight/fabiyi-opeyemi.jpg
+pronouns: he/him
+location: Lagos, Nigeria
+jobTitle: Senior Analytics Engineer
+companyName: Data Culture
+organization: Young Data Professionals (YDP)
+socialLinks:
+  - name: Twitter
+    link: https://twitter.com/Opiano_1
+  - name: LinkedIn
+    link: https://www.linkedin.com/in/opeyemifabiyi/
+dateCreated: 2023-07-02
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I joined the [dbt Slack community](https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___) in 2021, and it has been an experience getting to learn from thought leaders in the space and stay in touch with cutting-edge innovation in the data space. The community has helped me become a better engineer by reading different responses to questions on Slack, and seeing genuine support from community members help other members tackle and solve their difficult problems is inspiring and has allowed me to model my community (YDP & the Lagos dbt Meetup) through that lens. I randomly enter the dbt Slack daily to read and learn from different channels. I love the sense of community that resonates in the dbt Slack channel, and the good news is that I got my current role from the #jobs channel from a post from Data Culture Co-Founder. So you can stay glued to that page if you are looking for a job role.  
+
+The dbt community greatly impacted my previous role as a one-person data team. The community became the team I didn't have, providing all the necessary support and guidance I needed to deliver great value for the company excellently, and my experience with the community was the inspiration for my Coalesce talk in 2022 on how to leverage the dbt community as a data team of one.
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+Many great leaders inspire me in the dbt community; Joel Labes for constantly interacting with new folks and providing that safe space for everyone to ask any question, no matter how dumb you may think your question may be. He will give a response that will solve your problem; Benn Stancil for his vast experience and how he communicates it well with humour in his Friday night Substack, a newsletter I look forward to, which helps me stay current with recent trends in the global data space. 
+
+Both of them resonate with the kind of leader I want to grow in the dbt Community; to be vast, experienced and readily available to provide support and guidance and help people solve problems and grow their careers.
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+I've learned how to show empathy as a data professional and be a great engineer from various best practices around working with data. I also want others to know that irrespective of their current level of expertise or maturity in their data career, they can make an impact by getting involved in the community and helping others grow.
+
+## Anything else interesting you want to tell us?
+
+Maybe, I will consider DevRel as a career sometime 😀 because of my innate passion and love for community and people. Several folks tell me I'm a strong DevRel talent any product-led company would be lucky to have, so you can reach out if you need someone to drive your community engagement, haha 😂.
diff --git a/website/docs/community/spotlight/faith-lierheimer.md b/website/docs/community/spotlight/faith-lierheimer.md
new file mode 100644
index 00000000000..3edb839bb1d
--- /dev/null
+++ b/website/docs/community/spotlight/faith-lierheimer.md
@@ -0,0 +1,47 @@
+---
+id: faith-lierheimer
+title: Faith Lierheimer
+description: |
+  I've been a dbt Community member for around a year and a half. I come to the data world from teaching and academic research. Working in data fuses the aspects of those careers that I like the most, which are technical problem solving, and helping non-technical audiences understand data and what they can do with it. I have a dream stack with Databricks, dbt, and Looker. 
+
+  Professionally, I help shippers of perishable goods (everything from blueberries to childhood vaccinations) understand the risks their goods face in transit and how to mitigate them.This reduces food and medical waste worldwide.
+
+  You can read more about these interests at <a href="https://faithfacts.substack.com/" title="faithfacts.substack.com" rel="noopener noreferrer" target="_blank">faithfacts.substack.com</a>.
+image: /img/community/spotlight/faith-lierheimer.jpg
+pronouns: she/her
+location: Denver, CO, USA
+jobTitle: Data Analyst II
+companyName: Parsyl
+organization: Data Angels
+socialLinks:
+  - name: Twitter
+    link: https://twitter.com/FaithLierheimer
+  - name: LinkedIn
+    link: https://www.linkedin.com/in/faithlierheimer/
+  - name: Substack
+    link: https://faithfacts.substack.com/
+  - name: Data Folks
+    link: https://data-folks.masto.host/@faithlierheimer
+dateCreated: 2023-06-28
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I joined the dbt community early in 2022 hoping to find technical help with dbt, and instead found a wide support network of career-minded data professionals. Being in the dbt community has helped me find my niche in the data world, and has helped me discover ways I can grow my career and technical acumen. Being in this community has been huge in easing my career transition from teaching into data.
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+I primarily conceptualize of leadership as raising the floor beneath everyone, rather than enabling a few to touch its vaulted ceiling. As I gain more experience, I'd be delighted to be a resource for fellow career changers and teachers in transition. 
+
+And, I love to goof in #roast-my-graph in the dbt Slack. [Come join](https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___) that channel, it's a hoot and a holler.
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+I've learned a lot from community members, but most notably and concretely, I've actually gotten excellent visualization advice in #roast-my-graph. I've taken graphs there several times where I felt stuck on the presentation and have learned a lot about effective vizzes from my peers there. 
+
+As I continue to gain experience, I hope others can learn from me what a successful career change looks like. And, ultimately, to take the work seriously but to not take ourselves that seriously.
+
+## Anything else interesting you want to tell us?
+
+I have a black cat with one eye named Gus and my purpose is now to give him the best existence possible.
diff --git a/website/docs/community/spotlight/jing-yu-lim.md b/website/docs/community/spotlight/jing-yu-lim.md
new file mode 100644
index 00000000000..f492cd351c0
--- /dev/null
+++ b/website/docs/community/spotlight/jing-yu-lim.md
@@ -0,0 +1,41 @@
+---
+id: jing-yu-lim
+title: Jing Yu Lim
+description: |
+  For ~3 years, I was a Product Analyst at Grab, a ride-hailing and food delivery app in Southeast Asia, before taking on an Analytics Engineering role in Spenmo, a B2B Fintech startup. I recently made the switch back to an Analyst role when I joined ExpressVPN, a VPN service for both consumers and businesses. I'm also one of the co-organisers of the <a href="https://www.meetup.com/singapore-dbt-meetup/" title="Singapore dbt Meetup" rel="noopener noreferrer" target="_blank">Singapore dbt Meetup</a>! 
+
+  My story with dbt started in Jan 2022, when I joined Spenmo where I taught myself dbt, mainly via <a href="https://docs.getdbt.com/docs/introduction" title="dbt's documentation">dbt's documentation</a> and <a href="https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___" title="Slack community" rel="noopener noreferrer" target="_blank">Slack community</a>. We used Snowflake as our data warehouse, and Holistics for BI. I spoke about data self-serve and Spenmo's journey with dbt at multiple meetups.
+image: /img/community/spotlight/jing-lim.jpg
+pronouns: she/her
+location: Singapore, Singapore
+jobTitle: Senior Data Analyst
+companyName: ExpressVPN
+organization: ""
+socialLinks:
+  - name: LinkedIn
+    link: https://www.linkedin.com/in/limjingyu/
+dateCreated: 2023-07-01
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I joined the dbt community late January 2022, while setting up Spenmo's first dbt project. I was completely new to dbt, and relied heavily on the #advice-dbt-help channel in dbt Slack whenever I got stuck. I have learnt so much from reading discussions in other channels as well (e.g. #leading-data-teams, #advice-mock-interviews, #db-snowflake, #tools-holistics).
+
+The dbt community also helped me expand my professional network, where I met so many amazing individuals! It all started with #local-singapore which was created by community member Jolanda Zwagemaker sometime in April 2022. We organised dinners to connect with one another, which eventually led to an opportunity to run Singapore dbt Meetup (HUGE thank you to dbt) - it is heartwarming to see connections forged between many attendees of the meetup, where we also learn from one another. It really does feel like a community!
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+Claire Carroll and Mila Page! My very first touchpoint with dbt was their articles in [The Analytics Engineering Guide](https://www.getdbt.com/analytics-engineering/). I remember relating to it so much that I was saying ""YES"" to every other line I read, and sending text snippets to my friends. 
+
+To me, Analytics Engineering could help overcome certain challenges I face as an analyst, and make the job feels less like a ""hamster wheel."" As the concept of analytics engineering is fairly new in Singapore, I feel the need to spread the word and bring about a mindset shift among not just data teams, but anyone who needs to work with a data team.
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+One of my favourite presentations from the Singapore dbt Meetup was [""How would the ideal Semantic Layer look like?""](https://docs.google.com/presentation/d/1t1ts04b7qA-BVlV3qbNZ4fI-MSZn0iL6_FhsaWhJk_0/edit?usp=sharing ) by fellow community member Thanh Dinh from Holistics. It taught me a new perspective on metrics: they could be like dbt models, where dependencies can be set up between metric models.
+
+I definitely have so much more to learn as an individual, but I hope to share some of my tips and lessons in terms of data modelling with others.
+
+## Anything else interesting you want to tell us?
+
+Thank you dbt for enabling us to run meetups! It has been critical for ensuring a great experience for the Singapore community. Also a huge shoutout to Amada, the Global Community Development Lead, for always being super helpful and supportive despite the 12-hour time difference!
diff --git a/website/docs/community/spotlight/owen-prough.md b/website/docs/community/spotlight/owen-prough.md
new file mode 100644
index 00000000000..cc8ce37221e
--- /dev/null
+++ b/website/docs/community/spotlight/owen-prough.md
@@ -0,0 +1,41 @@
+---
+id: owen-prough
+title: Owen Prough
+description: |
+  Well met, data adventurer! My professional data history is mostly USA healthcare-related (shout out to ANSI X12 claim files) while working with large (10k+ employee) software companies and small (but growing!) startups. My constant companion for the last decade has been SQL of various flavors <a href="https://xkcd.com/927/" rel="noopener noreferrer" target="_blank">https://xkcd.com/927/</a>, and these days I mostly work with PostgreSQL, AWS Athena, and Snowflake. I think SQL is a great tool to solve interesting problems.
+
+  Oh and also dbt. I haven't done anything too fancy with dbt, but I have contributed to the <a href="https://docs.getdbt.com/docs/core/connect-data-platform/athena-setup" title="dbt-athena adapter">dbt-athena adapter</a> and a few different packages. Mostly I lurk on Slack, cleverly disguised as a duck. It's a professional goal of mine to someday attend <a href="https://coalesce.getdbt.com/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_coalesce-2023_aw&utm_content=coalesce____&utm_term=all_all__" title="Coalesce" rel="noopener noreferrer" target="_blank">Coalesce</a>.
+image: /img/community/spotlight/owen-prough.jpg
+pronouns: he/him
+location: Milwaukee, USA
+jobTitle: Data Engineer
+companyName: Sift Healthcare
+organization: ""
+socialLinks:
+  - name: LinkedIn
+    link: https://linkedin.com/in/owen-prough
+dateCreated: 2023-06-28
+hide_table_of_contents: true
+---
+
+## When did you join the dbt community and in what way has it impacted your career?
+
+I committed dbt_project.yml to the company git repo in July 2021 so I've been hanging out with all of you for about 2 years. What I love the most about dbt is how easy it is to write data tests. Writing data tests without dbt was painful, but now with all the tests we have in dbt I have a dramatically improved confidence in our data quality.
+
+The wider dbt community is also a reliable and constant source of education. I only interact in a few Slack channels, but I read *many* Slack channels to see what others are doing in the Analytics Engineering space and to get ideas about how to improve the processes/pipelines at my company. Y'all are great.
+
+## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
+
+This is an interesting question. I think I most identify with or am inspired by [Josh Devlin](./josh-devlin), who seems to be everywhere on Slack and very knowledgeable/helpful. I also want to know things and pay it forward.
+
+Also shout out to [Faith Lierheimer](./faith-lierheimer), whose contributions to [#roast-my-graph](https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___) always make me laugh and/or weep.
+
+## What have you learned from community members? What do you hope others can learn from you?
+
+The [public documentation for dbt](https://docs.getdbt.com/docs/introduction) is quite good. You should bookmark it and make it a personal goal to read through it all. There are a lot of cool things that dbt can do.
+
+Also I think it's really cool to see newcomers asking questions on Slack/[Discourse](https://discourse.getdbt.com/) and then see those same people answering others' questions. It speaks to the value we all get from dbt that folks want to give back to the community.
+
+## Anything else interesting you want to tell us?
+
+Did you notice how I avoided starting a sentence with "dbt"? That's because I know the standard is lowercase, but starting a sentence with a lowercase word looks weird to my eyes.

From 8bb088e2b77a059121e1a317ae32845eae886bfe Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 13:55:50 -0600
Subject: [PATCH 099/703] Commands that produce `graph_summary.json`

---
 website/docs/reference/artifacts/other-artifacts.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index c0e72109f78..c9f7699d747 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -33,7 +33,7 @@ This functionality is new in v1.6. You should upgrade to 1.6 or later if you wan
 
 </VersionBlock>
 
-**Produced by:** TODO
+**Produced by:** [manifest commands](/reference/artifacts/manifest-json)
 
 This file is useful for investigating performance issues in Core's graph algorithms.
 

From 694cd697843c3a567612f9cea122655b6ce8f88c Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 14 Jul 2023 13:16:58 -0700
Subject: [PATCH 100/703] Update sl-architecture.md

I updated the arcitechture here. hope its ok that I just commited it :)
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index eee196ef535..62a326a1405 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -7,15 +7,13 @@ sidebar_label: "Product architecture"
 
 <VersionBlock firstVersion="1.6">
 
-The dbt Semantic Layer product architecture includes four primary components:
+At a high level, the dbt Semantic Layer has two primary components -- defining your metrics in MetricFlow and querying them with Semantic Layer APIs. These are made possible by the following architechtural pieces:
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
 | **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
 | **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
-| **Semantic Layer Gateway**| Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform.  | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview)**  | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br> ❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-
+| **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 
 
 

From e2aa81e4af1a4e666d6aa04e7760623634817a3f Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Fri, 14 Jul 2023 16:17:37 -0400
Subject: [PATCH 101/703] show previous spotlight members header

---
 .../communitySpotlightList/index.js           | 24 +++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/website/src/components/communitySpotlightList/index.js b/website/src/components/communitySpotlightList/index.js
index 46ea43c84e7..b72d640b74d 100644
--- a/website/src/components/communitySpotlightList/index.js
+++ b/website/src/components/communitySpotlightList/index.js
@@ -19,6 +19,18 @@ function CommunitySpotlightList({ spotlightData }) {
   // Build meta title from communityTitle and docusaurus config site title
   const metaTitle = `${communityTitle}${siteConfig?.title ? ` | ${siteConfig.title}` : ''}`
 
+  // Split spotlight members into current and previous
+  let currentSpotlightMembers = []
+  let previousSpotlightMembers = []
+
+  spotlightData?.map(member => {
+    if(currentSpotlightDate > new Date(member?.data?.dateCreated)) {
+      previousSpotlightMembers.push(member)
+    } else {
+      currentSpotlightMembers.push(member)
+    }
+  })
+  
   return (
     <Layout>
       <Head>
@@ -36,11 +48,19 @@ function CommunitySpotlightList({ spotlightData }) {
       />
       <section id='spotlight-members-section'>
         <div className='container'>   
-          {spotlightData && spotlightData.length > 0 ? (
+          {currentSpotlightMembers?.length || previousSpotlightMembers?.length ? (
             <>
-              {spotlightData.map((member, i) => (
+              {currentSpotlightMembers?.map((member, i) => (
                 <CommunitySpotlightCard frontMatter={member.data} key={i} />
               ))}
+              {previousSpotlightMembers?.length ? (
+                <>
+                  <h2>Previously on the Spotlight</h2>
+                  {previousSpotlightMembers.map((member, i) => (
+                    <CommunitySpotlightCard frontMatter={member.data} key={i} />
+                  ))}
+                </>
+              ) : ''}
             </>
           ) : 
             <p>No community spotlight members are available at this time. 😕</p>

From 05185ed520190e4cc877e2e5b3a3c1d83929623a Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 14 Jul 2023 13:22:43 -0700
Subject: [PATCH 102/703] Update jdbc.md

Made a few terminology updates here
---
 .../docs/docs/use-dbt-semantic-layer/jdbc.md    | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
index 3b9f28b58a5..7fd85836d9b 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/use-dbt-semantic-layer/jdbc.md
@@ -17,18 +17,15 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 </VersionBlock>
 
-The dbt Semantic Layer Java Database Connectivity (JDBC) driver enables users to query metrics and dimensions using the JDBC protocol, while also providing standard metadata functionality. 
+The dbt Semantic Layer Java Database Connectivity (JDBC) API enables users to query metrics and dimensions using the JDBC protocol, while also providing standard metadata functionality. 
 
-A JDBC driver is a software component enabling a Java application to interact with a data platform. Here's some more information about the JDBC:
+A JDBC driver is a software component enabling a Java application to interact with a data platform. Here's some more information about our JDBC API:
 
 - The Semantic Layer JDBC API utilizes the open-source JDBC driver with ArrowFlight SQL protocol.
 - You can download the JDBC driver from [Maven](https://search.maven.org/remotecontent?filepath=org/apache/arrow/flight-sql-jdbc-driver/12.0.0/flight-sql-jdbc-driver-12.0.0.jar). 
 - The dbt Semantic Layer supports ArrowFlight SQL driver version 12.0.0 and higher. 
 - You can embed the driver into your application stack as needed, and you can use dbt Labs' [example project](https://github.com/dbt-labs/example-semantic-layer-clients) for reference.
-
-## Installation
-
-- Install an AWS root CA to the Java Trust Store [documentation](https://www.amazontrust.com/repository/).
+- To call the JDBC API in an application, you should install an AWS root CA to the Java Trust Store [documentation](https://www.amazontrust.com/repository/).
 
 ## Connection parameters
 
@@ -49,7 +46,7 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 
 *Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable into the JDBC URL `&disableCertificateVerification=true`
 
-## Querying the API
+## Querying the API for Metric Metadata
 
 The Semantic Layer JDBC API has built-in metadata calls which can provide a user with information about their metrics and dimensions. Here are some metadata commands and examples:
 
@@ -95,9 +92,9 @@ semantic_layer.dimension_values(metrics=["food_order_amount"], group_by="custome
 
 </Tabs>
 
-## Querying Parameters
+## Querying the API for Metric Values
 
-Use the following query parameters to filter and sort data, along with examples:
+To query metric values, here are the following parameters that are available:
 
 | Parameter | Description  | Example    | Type |
 | --------- | -----------| ------------ | -------------------- |
@@ -109,7 +106,7 @@ Use the following query parameters to filter and sort data, along with examples:
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
 | `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |
 
-## Additional examples
+## Examples
 
 Use the following examples to help you get started with the JDBC API
 

From 423a3edce856c2adf62e79333e7e07f71eea21ae Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 14:28:37 -0600
Subject: [PATCH 103/703] Update bigquery-configs.md

---
 website/docs/reference/resource-configs/bigquery-configs.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/resource-configs/bigquery-configs.md b/website/docs/reference/resource-configs/bigquery-configs.md
index c447859ba61..c425fd5b94b 100644
--- a/website/docs/reference/resource-configs/bigquery-configs.md
+++ b/website/docs/reference/resource-configs/bigquery-configs.md
@@ -766,6 +766,7 @@ The `grant_access_to` config is not thread-safe when multiple views need to be a
 
 <VersionBlock firstVersion="1.6">
 
+
 ## Materialized view
 
 The BigQuery adapter supports [materialized views](https://cloud.google.com/bigquery/docs/materialized-views-intro) and refreshes them for every subsequent `dbt run` you execute. For more information, see [Refresh Materialized Views](https://cloud.google.com/bigquery/docs/materialized-views-manage#refresh) in the Google docs.

From 653f4043a01f2c332b5a7b1613031239b801b632 Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Sat, 15 Jul 2023 08:38:36 +1200
Subject: [PATCH 104/703] Switch from dots to underscores for versioning
 (#3316)

## What are you changing in this pull request and why?
The docs for model versioning use `dim_customers.v1` in a lot of places,
instead of `dim_customers_v1`. This was an original proposal for model
version naming that got abandoned due to database peculiarities.

This PR swaps out the `.` for `_` everywhere except for when it's being
used in a model's fully qualified name (where the dots are actually how
it's represented.)

I'm assuming this was an oversight as the product evolved; maybe it's on
purpose across the board? If so then this is more up for debate, but IMO
the more time we spend talking about versioned models along our
preferred naming scheme (single digit with an underscore) the better.
The fact that we use dots behind the scenes isn't relevant to most
folks.

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/docs/docs/collaborate/govern/model-versions.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index 64559aa70af..b38ed13289d 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -269,7 +269,7 @@ models:
 
 </Tabs>
 
-The configuration above says: Instead of two unrelated models, I have two versioned definitions of the same model: `dim_customers.v1` and `dim_customers.v2`.
+The configuration above says: Instead of two unrelated models, I have two versioned definitions of the same model: `dim_customers_v1` and `dim_customers_v2`.
 
 **Where are they defined?** dbt expects each model version to be defined in a file named `<model_name>_v<v>`. In this case: `dim_customers_v1.sql` and `dim_customers_v2.sql`. It's also possible to define the "latest" version in `dim_customers.sql` (no suffix), without additional configuration. Finally, you can override this convention by setting [`defined_in: any_file_name_you_want`](/reference/resource-properties/versions#defined_in)—but we strongly encourage you to follow the convention, unless you have a very good reason.
 
@@ -299,7 +299,7 @@ Like with all config inheritance, any configs set _within_ the versioned model's
 
 ### Configuring database location with `alias`
 
-Following the example, let's say you wanted `dim_customers.v1` to continue populating the database table named `dim_customers`. That's what the table was named previously, and you may have several other dashboards or tools expecting to read its data from `<dbname>.<schemaname>.dim_customers`.
+Following the example, let's say you wanted `dim_customers_v1` to continue populating the database table named `dim_customers`. That's what the table was named previously, and you may have several other dashboards or tools expecting to read its data from `<dbname>.<schemaname>.dim_customers`.
 
 You could use the `alias` configuration:
 
@@ -412,4 +412,4 @@ We expect to develop more opinionated recommendations as teams start adopting mo
 - Where possible, define other versions as `select` transformations, which take the latest version as their starting point
 - When bumping the `latest_version`, migrate the SQL and YAML accordingly.
 
-In the example above, the third point might be tricky. It's easier to _exclude_ `country_name`, than it is to add it back in. Instead, we might need to keep around the full original logic for `dim_customers.v1`—but materialize it as a `view`, to minimize the data warehouse cost of building it. If downstream queriers see slightly degraded performance, it's still significantly better than broken queries, and all the more reason to migrate to the new "latest" version.
+In the example above, the third point might be tricky. It's easier to _exclude_ `country_name`, than it is to add it back in. Instead, we might need to keep around the full original logic for `dim_customers_v1`—but materialize it as a `view`, to minimize the data warehouse cost of building it. If downstream queriers see slightly degraded performance, it's still significantly better than broken queries, and all the more reason to migrate to the new "latest" version.

From b9fe2b84913f06cf571c9d286de4d4d36ea095dc Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 14 Jul 2023 14:46:46 -0700
Subject: [PATCH 105/703] Update graphql.md

Adding initial graphql content

I think it's totally fine if we don't do any automation of graphql inputs to start. We can add that later
---
 .../docs/use-dbt-semantic-layer/graphql.md    | 78 ++++++++++++++++++-
 1 file changed, 75 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/graphql.md b/website/docs/docs/use-dbt-semantic-layer/graphql.md
index fd2f60347b0..783c056bcf4 100644
--- a/website/docs/docs/use-dbt-semantic-layer/graphql.md
+++ b/website/docs/docs/use-dbt-semantic-layer/graphql.md
@@ -11,12 +11,84 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
 <UpgradeSL />
 
-ADD GRAPHQL CONTENT HERE
-
 </VersionBlock>
 
 <VersionBlock firstVersion="1.6">
 
-ADD GRAPHQL CONTENT HERE
+## What is GraphQL?
+
+GraphQL is an open-source query language for APIs (Application Programming Interfaces). It provides a more efficient and flexible alternative to traditional RESTful APIs. 
+
+With GraphQL, clients can request specific data requirements using a single query. GraphQL allows clients to retrieve all the needed data in a single request. This reduces the number of round trips to the server, minimizing network overhead and improving performance.
+
+Overall, GraphQL offers benefits such as being self-documenting, a strong typing system, versioning and evolution support, enabling rapid development, and a robust ecosystem. These advantages make GraphQL a powerful choice for building APIs that prioritize flexibility, performance, and developer productivity.
+
+
+### dbt Semantic Layer GraphQL API
+
+The dbt Semantic Layer GraphQL API has the following capabilities, and has similarities to the calls available in our JDBC API.
+
+One benefit of GraphQL is its self documenting nature, so you can explore using our <schema explorer>. To give you an idea of the types of commands you can execute, we provide some available calls below.
+
+
+**Metric Metadata Calls**
+
+Fetch available metrics
+```graphql
+metrics(environmentId: Int!): [Metric!]!
+```
+
+Fetch available dimensions for metrics
+
+```graphql
+dimensions(
+environmentId: Int!
+metrics: [String!]!
+): [Dimension!]!
+```
+
+Fetch available time granularities given metrics
+
+```graphql
+queryableGranularities(
+environmentId: Int!
+metrics: [String!]!
+): [TimeGranularity!]!
+```
+
+Fetch available metrics given a set of a dimensions
+
+```graphql
+metricsForDimensions(
+environmentId: Int!
+dimensions: [String!]!
+): [Metric!]!
+```
+
+Fetch dimension values for metrics and a given dimension
+
+```graphql
+dimensionValues(
+environmentId: Int!
+metrics: [String!]!
+dimension: String!
+```
+
+**Metric Value Query Parameters**
+
+The mutation is `createQuery`. The parameters are as follows:
+
+```graphql
+createQuery(
+environmentId: Int!
+metrics: [String!]!
+dimensions: [String!] = null
+limit: Int = null
+startTime: String = null
+endTime: String = null
+where: String = null
+order: [String!] = null
+): String
+```
 
 </VersionBlock>

From d46f50ec28878f34a273472152e0d0d1c526899e Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 14 Jul 2023 14:56:11 -0700
Subject: [PATCH 106/703] Update graphql.md

Fixing bug with <schema>
---
 website/docs/docs/use-dbt-semantic-layer/graphql.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/graphql.md b/website/docs/docs/use-dbt-semantic-layer/graphql.md
index 783c056bcf4..d43069f6952 100644
--- a/website/docs/docs/use-dbt-semantic-layer/graphql.md
+++ b/website/docs/docs/use-dbt-semantic-layer/graphql.md
@@ -28,7 +28,7 @@ Overall, GraphQL offers benefits such as being self-documenting, a strong typing
 
 The dbt Semantic Layer GraphQL API has the following capabilities, and has similarities to the calls available in our JDBC API.
 
-One benefit of GraphQL is its self documenting nature, so you can explore using our <schema explorer>. To give you an idea of the types of commands you can execute, we provide some available calls below.
+One benefit of GraphQL is its self documenting nature, so you can explore using our schema explorer(todolink). To give you an idea of the types of commands you can execute, we provide some available calls below.
 
 
 **Metric Metadata Calls**

From 45ccbe17f6d6b2bd6cba61b20c9663fb474b8249 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 16:12:28 -0600
Subject: [PATCH 107/703] Revert "`merge` is not (yet) supported in
 dbt-redshift for v1.6"

---
 website/docs/docs/build/incremental-models.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index e6c04ab7ded..28345ba1873 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -253,7 +253,7 @@ to build incremental models.
 
 Click the name of the adapter in the below table for more information about supported incremental strategies.
 
-The `merge` strategy is available in dbt-postgres beginning in dbt v1.6.
+The `merge` strategy is available in dbt-postgres and dbt-redshift beginning in dbt v1.6.
 
 <VersionBlock lastVersion="1.5">
 
@@ -276,7 +276,7 @@ The `merge` strategy is available in dbt-postgres beginning in dbt v1.6.
 | data platform adapter  | default strategy | additional supported strategies  |
 | :----------------- | :----------------| : ---------------------------------- |
 | [dbt-postgres](/reference/resource-configs/postgres-configs#incremental-materialization-strategies) | `append`         | `merge` , `delete+insert`                  |
-| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | `append`         | `delete+insert`                  |
+| [dbt-redshift](/reference/resource-configs/redshift-configs#incremental-materialization-strategies) | `append`         | `merge`, `delete+insert`                  |
 | [dbt-bigquery](/reference/resource-configs/bigquery-configs#merge-behavior-incremental-models)      | `merge`          | `insert_overwrite`                       |
 | [dbt-spark](/reference/resource-configs/spark-configs#incremental-models)                           | `append`         | `merge` (Delta only)  `insert_overwrite` |
 | [dbt-databricks](/reference/resource-configs/databricks-configs#incremental-models)                 | `append`         | `merge` (Delta only) `insert_overwrite`  |

From 961cae634db5bf208fec7673b71b5830123ac720 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 14 Jul 2023 15:24:51 -0700
Subject: [PATCH 108/703] Update dbt-sl.md

Took a stab at summarizing the differences betwen what team/enterprise vs developer and dbt core get.
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 3f4df7dfc8e..242a839412c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -23,7 +23,7 @@ To read more about why you need a universal Semantic Layer, read this [blog post
 
 ## Explore the dbt Semantic Layer
 
-The dbt Semantic Layer is available to all dbt Cloud plans (Developer, Team, and Enterprise) during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Start exploring the dbt Semantic Layer by visiting the following pages:
+The dbt Semantic Layer is available to all Team and Enterprise Plans during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Those on dbt Cloud Developer plans or dbt core users can use MetricFlow for defining and testing metrics locally. Start exploring the dbt Semantic Layer by visiting the following pages:
 
 <div className="grid--3-col">
 

From 32628b031ca45d31a4e1746fc29bf621fea3fc99 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 16:46:49 -0600
Subject: [PATCH 109/703] Update required version message for consistency

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/reference/artifacts/other-artifacts.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index c9f7699d747..612f96791f6 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -28,7 +28,7 @@ Stores the networkx representation of the dbt resource DAG.
 <VersionBlock lastVersion="1.5">
 
 :::info New functionality
-This functionality is new in v1.6. You should upgrade to 1.6 or later if you want to use this artifact.
+This functionality is new in v1.6. 
 :::
 
 </VersionBlock>

From a494cd15cc94a462cf7dc9c28bedbdc80689feb5 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 16:47:23 -0600
Subject: [PATCH 110/703] Use full name for "dbt Core"

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/reference/artifacts/other-artifacts.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index 612f96791f6..eaa26848c72 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -35,7 +35,7 @@ This functionality is new in v1.6.
 
 **Produced by:** [manifest commands](/reference/artifacts/manifest-json)
 
-This file is useful for investigating performance issues in Core's graph algorithms.
+This file is useful for investigating performance issues in dbt Core's graph algorithms.
 
 It is more anonymized and compact than [`manifest.json`](/reference/artifacts/manifest-json) and [`graph.gpickle`](#graph.gpickle).
 

From 3fe5dfdfcb5f7998469c59946d6f3e8cd58cf677 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Fri, 14 Jul 2023 16:47:58 -0600
Subject: [PATCH 111/703] Update
 website/docs/reference/artifacts/other-artifacts.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/reference/artifacts/other-artifacts.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/artifacts/other-artifacts.md b/website/docs/reference/artifacts/other-artifacts.md
index eaa26848c72..d776bc8a099 100644
--- a/website/docs/reference/artifacts/other-artifacts.md
+++ b/website/docs/reference/artifacts/other-artifacts.md
@@ -39,4 +39,4 @@ This file is useful for investigating performance issues in dbt Core's graph alg
 
 It is more anonymized and compact than [`manifest.json`](/reference/artifacts/manifest-json) and [`graph.gpickle`](#graph.gpickle).
 
-It contains only the `name` and `type` of each node along with ids of its child nodes (`succ`). It includes that information at two separate points in time: Immediately after the graph is linked together (`linked`), and then after test edges have been added (`with_test_edges`).
+It contains only the `name` and `type` of each node along with IDs of its child nodes (`succ`). It includes that information at two separate points in time: immediately after the graph is linked together (`linked`), and after test edges have been added (`with_test_edges`).

From e3d6c47056af33da148dd2311c8e06d0772b5171 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Sat, 15 Jul 2023 01:10:58 +0100
Subject: [PATCH 112/703] dbt debug --connection flag (#3731)

Adds `dbt debug --connection` flag to test connection only  in 1.6.

Resolves  #3725

Not sure if i did this correctly @jtcohen6

---------

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/reference/commands/debug.md | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/debug.md b/website/docs/reference/commands/debug.md
index 8295df8c77f..4ae5a1d2dd9 100644
--- a/website/docs/reference/commands/debug.md
+++ b/website/docs/reference/commands/debug.md
@@ -4,9 +4,24 @@ sidebar_label: "debug"
 id: "debug"
 ---
 
-`dbt debug` is a utility function to test the database connection and show information for debugging purposes. Not to be confused with [debug-level logging](/reference/global-configs/about-global-configs#debug-level-logging) via the `--debug` option which increases verbosity.
 
-The `--config-dir` option to `dbt debug` will show the configured location for the `profiles.yml` file and exit:
+`dbt debug` is a utility function to test the database connection and display information for debugging purposes, such as the validity of your project file and your installation of any requisite dependencies (like `git` when you run `dbt deps`).
+
+*Note: Not to be confused with [debug-level logging](/reference/global-configs/about-global-configs#debug-level-logging) via the `--debug` option which increases verbosity.
+
+### Example usage
+
+<VersionBlock firstVersion="1.6">
+
+Only test the connection to the data platform and skip the other checks `dbt debug` looks for:
+
+```shell
+$ dbt debug --connection
+```
+
+</VersionBlock>
+
+Show the configured location for the `profiles.yml` file and exit:
 
 ```text
 $ dbt debug --config-dir

From 2737ec1407c1b75269bbc0620283617f8567fdda Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Sat, 15 Jul 2023 01:22:56 +0100
Subject: [PATCH 113/703] metric nodes `depends_on` (#3733)

adding language that metric nodes include dependencies on semantic
models based on measures but not filters in dag and dbt selection.

Resolves #3682

---------

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 website/docs/docs/build/measures.md        |  4 ++++
 website/docs/docs/build/semantic-models.md |  6 ++++++
 website/snippets/_metrics-dependencies.md  | 15 +++++++++++++++
 3 files changed, 25 insertions(+)
 create mode 100644 website/snippets/_metrics-dependencies.md

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index febdb991b03..f85d21e3dcb 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -219,3 +219,7 @@ We can query the semi-additive metrics using the following syntax:
 mf query --metrics mrr_by_end_of_month --dimensions metric_time__month --order metric_time__month 
 mf query --metrics mrr_by_end_of_month --dimensions metric_time__week --order metric_time__week 
 ```
+
+import SetUpPages from '/snippets/_metrics-dependencies.md';
+
+<SetUpPages /> 
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index c4b14d703fb..28fccaddb72 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -161,6 +161,12 @@ For semantic models with a measure, you must have a primary time group.
 | `non_additive_dimension` | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
 | `create_metric`* | You can create a metric directly from a measure with create_metric: True and specify its display name with create_metric_display_name. | Optional |
 _*Coming soon_
+
+
+import SetUpPages from '/snippets/_metrics-dependencies.md';
+
+<SetUpPages />
+
 ## Related docs
 
 - [About MetricFlow](/docs/build/about-metricflow)
diff --git a/website/snippets/_metrics-dependencies.md b/website/snippets/_metrics-dependencies.md
new file mode 100644
index 00000000000..1be88a54496
--- /dev/null
+++ b/website/snippets/_metrics-dependencies.md
@@ -0,0 +1,15 @@
+## Dependencies
+
+Metric nodes will reflect dependencies on semantic models based on their _measures_. However, dependencies based on filters should not be reflected in:
+
+- [dbt selection syntax](/reference/node-selection/syntax)
+- Visualization of the <Term id="dag">DAG</Term> in dbt-docs and the [integrated development environment](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) (IDE).
+
+This is because metrics need to source nodes for their `depends_on` attribute from a few different places:
+
+- `RATIO` and `DERIVED` type metrics should reference `Metric.type_params.input_metrics`.
+- `SIMPLE` <!--and `CUMULATIVE`--> type metrics should reference `Metric.type_params.measure`.
+
+For example, when you run the command `dbt list --select my_semantic_model+`, it will show you the metrics that belong to the specified semantic model.
+
+But there's a condition: Only the metrics that actually use measures or derived metrics from that semantic model will be included in the list. In other words, if a metric only uses a dimension from the semantic model in its filters, it won't be considered as part of that semantic model.

From 49ea692c30d542f1e3c0d41b907a83c89afc492d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 17 Jul 2023 11:55:32 +0100
Subject: [PATCH 114/703] Delete
 2022-10-12-how-to-design-and-structure-metrics.md

removing blogs related to legacy semantic layer per convo w Roxi and Jordan
---
 ...-12-how-to-design-and-structure-metrics.md | 394 ------------------
 1 file changed, 394 deletions(-)
 delete mode 100644 website/blog/2022-10-12-how-to-design-and-structure-metrics.md

diff --git a/website/blog/2022-10-12-how-to-design-and-structure-metrics.md b/website/blog/2022-10-12-how-to-design-and-structure-metrics.md
deleted file mode 100644
index 4f738543dff..00000000000
--- a/website/blog/2022-10-12-how-to-design-and-structure-metrics.md
+++ /dev/null
@@ -1,394 +0,0 @@
----
-title: "How to design and structure dbt metrics: Recommendations for getting started"
-description: "The introduction of the dbt Semantic Layer expands what users can do with dbt but introduces a familiar questions around where logic should live. Read along as the dbt Labs team talks about best practices through the lens of two different examples!"
-slug: how-to-design-and-structure-metrics
-
-authors: [callum_mccann]
-
-tags: [dbt product updates]
-hide_table_of_contents: false
-
-date: 2022-10-12
-is_featured: true
----
-
----
-
-**IMPORTANT:** This document serves as the temporary location for information on how to design and structure your metrics. It is our intention to take this content and turn it into a Guide, like [How we structure our dbt projects](https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview), but we feel that codifying information in a Guide first requires that metrics be rigorously tested by the community so that best practices can arise. This document contains our early attempts to create best practices. In other words, read these as suggestions for a new paradigm and share in the community where they do (or don’t) match your experiences! You can find more information on where to do this at the end.
-
----
-
-## The power of a semantic layer on top of a mature data modeling framework
-
-As a longtime [dbt Community](https://www.getdbt.com/community/join-the-community/) member, I knew I had to get involved when I first saw the dbt Semantic Layer in the now infamous [`dbt should know about metrics` Github Issue](https://github.com/dbt-labs/dbt-core/issues/4071). It gave me a vision of a world where metrics and business logic were unified across an entire organization; a world where the data team was no longer bound to a single consuming experience and could enable their stakeholders in dozens of different ways. To me, it felt like the opportunity to contribute to the next step of what dbt could become.
-
-In past roles, I’ve been referred to as the `dbt zealot` and I’ll gladly own that title! It’s not a surprise - dbt was built to serve data practitioners expand the power of our work with software engineering principles. It gave us flexibility and power to serve our organizations. But I always wondered if there were more folks who could directly benefit from interacting with dbt.
-
-The Semantic Layer expands the reach of dbt **by coupling dbt’s mature data modeling framework with semantic definitions.** The result is a first of its kind data experience that serves both the data practitioners writing your analytics code and stakeholders who depend on it. Metrics are the first step towards this vision, allowing users to version control and centrally define their key business metrics in a single repo while also serving them to the entire business.
-
-However, this is still a relatively new part of the dbt toolbox and you probably have a lot of questions on how exactly you can do that. This blog contains our early best practice recommendations for metrics in two key areas:
-- **Design**: What logic goes into metrics and how to use calculations, filters, dimensions, etc.
-- **Structure**: Where these metrics will live in your dbt project and how to compose the files that contain your metrics
-
-We developed these recommendations by combining the overall philosophy of dbt, with our hands-on learning gathered during the beta period and internal testing.
-
-<!--truncate-->
-
-**Pre-reading:** We recommend reading through the [metrics documentation](/docs/build/metrics), which contains a table of all the required/optional properties.
-
-### When to put business logic in the semantic layer vs the modeling layer
-
-Our instinct when designing metrics might be to encode as much information as possible into the semantic layer. An example of this is case statements - the analytics engineer’s gut instinct might be to mimic tools of the past and provide complicated case statements for the metric `expression` property to try and capture the nuance of how it should be calculated.
-
-But remember - you always have the option of performing this logic _in the modeling layer_. This is the key difference between dbt and other semantic layer offerings - by sitting the semantic layer atop a mature transformation layer, you always have the option to configure and optimize your logic within your models and then _define semantic components with intentionality_.
-
-Getting the balance  just right is a learning experience and developing community best practices and standards will take time, which is why it’s important for us to think from first principles. What should really be our goal when determining whether logic lives in a model or a metrics?
-
-To explore this question and begin to develop an intuition, we’ll walk through two examples of handling this divide.
-
-## Basic example: Revenue
-
-### Designing your metrics
-
-In this example, we’ll cover the basics of defining a metric and a fairly straightforward example of where users can draw the line between the semantic layer and the modeling layer. You should finish this section with a better understanding of dbt metrics and its relationship to the modeling layer.
-
-In the past, the `marts` tables were often your end stage layer before data was consumed in another tool or system. Now, the mart is the springboard for the creation of our metric. So we'll begin by looking our end-state `marts` model called `order_events` that looks something like the below table, but on the order of millions of rows instead of five. Our finance team uses the below model to better understand revenue but inconsistencies in how it's reported have led to requests that the data team centralize the definition in the dbt repo.
-
-| event_date | order_id | order_country | order_status | customer_id | customer_status | amount |
-| --- | --- | --- | --- | --- | --- | --- |
-| 2022-10-01 | 1 | United States | completed | 19 | Healthy | 10 |
-| 2022-10-01 | 2 | France | completed | 36 | Churn Risk | 15 |
-| 2022-10-02 | 2 | France | returned | 36 | Churned | 15 |
-| 2022-10-02 | 3 | Turkey | completed | 20 | Healthy | 80 |
-| 2022-10-03 | 4 | Korea | completed | 14 | Churn Risk | 24 |
-
-### Logic in the modeling layer vs the semantic layer
-
-We know from our upstream dbt models that the `amount` field represents the revenue from from each order. The inconsistent reporting, however, has arisen because the correct definition of revenue only refers to orders that are completed, not returned. Some teams aren’t familiar with this additional filter and it has led to company wide misreporting.
-
-The solution is to use the flexibility of the dbt modeling layer to add a boolean field called `is_active_row` that shows whether or not the row in question is the most recent version. With this, we can understand and filter out duplicate rows that may be connected to the same order.
-
-Once we have this field, we reach a diverging path:
-
-- If we are not interested in seeing the history of `order_events` , we can add a `where` clause **to the model itself**. This would ensure there is only one row per order.
-- If we **are** interested in seeing the history of `order_events` , we can add a `filter` to the metric definition to ensure that these duplicate order rows don’t cause us to misreport revenue
-
-Both of these paths ensure that only the correct orders are included in the metric calculation but one does it at the modeling layer and the other the semantic layer. There is no **best** path here - it depends on your organization's reporting and analytics needs. For this example, we’ll say that our business isn’t interested in understanding orders that have gone from completed to returned and so we’ll use option one moving forward. Now lets define the metric:
-
-```yaml
-version: 2
-metrics:
-  - name: total_revenue
-    label: The revenue of our business
-    model: ref('order_events')
-    description: "The revenue for our business, as defined by Jerry in Finance"
-
-    calculation_method: sum
-    expression: amount
-
-    timestamp: event_date
-    time_grains: [day, week, month, all_time]
-
-    dimensions:
-      - customer_status
-      - order_country
-
-    ## We don't need this section because we chose option 1
-    ## filters:
-    ##   - field: order_status
-    ##     operator: '='
-    ##     value: 'completed
-```
-
-Each of the properties of the above definition are defined [in the metrics documentation](/docs/build/metrics), but let’s dig into the two that might require some additional explanation. The two in question are `expression` and `dimensions`.
-
-In plain english, the `expression` property is the sql column (or expression) that we are applying the calculation method on. In our example above, this simplifies to `sum(amount)`. However, this doesn’t **need** to be a field in the model. It could also be a sql expression like `case when condition = true then field else 0 end` .
-
-And then there’s `dimensions`.
-
-### Choosing which dimensions to use with your metric
-
-The `dimensions` attribute is a bit more nuanced than the others because it involves curating the ways through which a user can interact with the metric. To that end …
-
-❗ **We recommend curating dimensions, not including all columns within the model. Most models contain dimensions that aren’t relevant for end-user analysis.**
-
-What do we mean? Well, there is a lot of nuance in what constitutes a useful or less useful dimension that is dependent on the shape of the underlying data and the ways with which the metric will be consumed. Continuing with our revenue use case, here are some examples:
-
-- **Useful Dimensions:**
-    - `customer_status`: This field is helpful to end users because it allows them to break down the revenue generated by each customer status grouping. Members of the retention team might be interested in understanding the long-term trends of revenue from the Churn Risk group so that they can better understand the impact that their retention initiatives campaigns have had.
-    - `order_country`: This field is useful because it allows members of the finance team to break down the accepted revenue from each country of origin so that they can better understand which countries are experiencing the highest growth.
-- **Less Useful Dimensions:**
-    - `order_status` : Given that order_status is a part of the metric definition, it doesn’t make sense to include in the acceptable dimensions list because the value returned would always be `completed`.
-    - `order_id`: Each order id corresponds to a single order and a single point in time. Grouping the metric of revenue by order_id would just return the base grain of the table and the same value as the amount field - not useful from a metric perspective!
-- **Nuanced Dimensions:**
-    - `customer_id`: This is an interesting field because it can be both good and bad depending on the context in which it is used and the underlying data. In our example use case, this dimension wouldn’t be that useful - it would contain too many unique values and tracking the individual revenue impact by a single customer doesn’t make sense on a retail scale.
-        - In a SaaS business though, it might make more sense - especially with usage based pricing. The Customer Success team might be interested in tracking the revenue of certain customers and ensuring that they remain consistent.
-
-To quote Cameron Afzal, Product Manager of the dbt Semantic Layer:
-
-> Thoughtful curation of dimensions is essential for three main reasons:
-- **Relevance:** Analysts must include the dimensions most relevant to answering the question.
-- **Trust**: Curating high-quality dimensions with little to no known errors helps ensure trust in analysis results and the decisions that follow.
-- **Efficiency**: Curation provides a faster path to high-quality analysis results.
->
-
-To put it another way, **metrics are most useful when every dimension provided can help provide answers to the business.**
-
-## Advanced example: NPS
-
-### Designing a complex metric
-
-Now let’s look at a more complex example of a metric  - one that is built from components that could theoretically themselves be metrics. The metric in question is Net Promoter score, which is used by the dbt Labs internal analytics team to understand the experience that users are having on dbt Cloud.
-
-For those of you who are unfamiliar with the industry metric of Net Promoter Score, here is a [great article from the folks over at Delighted on how it is calculated.](https://delighted.com/net-promoter-score) The short version of it is `the percentage of promoters - the percentage of detractors`.
-
----
-
-Here at dbt Labs we provide users with short surveys where they can provide feedback (as well as in a few other locations). The data is collected from those surveys is used to calculate our NPS Score, which helps us understand user sentiment over time.
-
-Given that these surveys come from a few different sources, there is a large amount of upstream modeling performed in order to unify them in a single model, but the end result is something that looks like the table below:
-
-| feedback_date | unique_id | feedback_source | user_type | account_plan | score | nps_category |
-| --- | --- | --- | --- | --- | --- | --- |
-| 2022-10-01 | 1 | nps_tool_1 | developer | team | 5 | detractor |
-| 2022-10-01 | 2 | nps_tool_2 | read_only | developer | 8 | promoter |
-| 2022-10-02 | 3 | nps_tool_1 | developer | enterprise | 10 | promoter |
-| 2022-10-02 | 4 | nps_tool_1 | developer | developer | 7 | passive |
-| 2022-10-02 | 5 | nps_tool_2 | developer | team | 9 | promoter |
-| 2022-10-03 | 6 | nps_tool_1 | developer | enterprise | 7 | passive |
-
-The dbt Internal Analytics team ([long may they reign](https://www.linkedin.com/feed/update/urn:li:activity:6962884130569080833/)) took this data and decided to build the NPS Score metric into our repo so that it could be surfaced to stakeholders in multiple tools. This process is where we began to form our opinions on what should live in the modeling layer vs semantic layer - but these are sure to progress as we add in more and more real world use cases.
-
-### Option 1: Putting everything in the semantic layer
-
-If we wanted to store all the logic inside metric definitions, we could use the following code in the Semantic Layer section to create 6 different metrics that result in the NPS Score metric. This would allow end users to retrieve the NPS Score they are interested in a version-controlled, standard way across any of their BI tools of choice. Additionally, it allows users to individually slice/dice any of the component metrics by themselves.
-
-```yaml
-metrics:
-  - name: total_respondents
-    label: Total of NPS Respondents
-    model: ref('customer_nps')
-    description: 'The count of users responding to NPS surveys in dbt Cloud.'
-    calculation_method: count
-    expression: unique_id
-    timestamp: created_at
-    time_grains: [day, month, quarter, year]
-    dimensions:
-      - feedback_source
-      - account_plan
-      - user_type
-
-  - name: total_promoter_respondents
-    ......... ##same as total_respondents
-    filters:
-    - field: nps_category
-      operator: '='
-      value: "'promoter'"
-
-  - name: total_detractor_respondents
-    ......... ##same as total_respondents
-    filters:
-    - field: nps_category
-      operator: '='
-      value: "'detractor'"
-
-  - name: promoters_pct
-    label: Percent Promoters (Cloud)
-    description: 'The percent of dbt Cloud users in the promoters segment.'
-    calculation_method: expression
-    expression: "{{metric('total_promoter_respondents')}} / {{metric('total_respondents')}}"
-    timestamp: created_at
-    time_grains: [day, month, quarter, year]
-    dimensions:
-      - feedback_source
-      - account_plan
-      - user_type 
-
-  - name: detractor_pct
-    ... ##same as promoters_pct
-    expression: "{{metric('total_detractor_respondents')}} / {{metric('total_respondents')}}"
-
-  - name: nps_score
-    label: Net Promoter Score
-    description: 'The NPS (-1 to 1) of all dbt Cloud users.'
-    calculation_method: expression
-    expression: "{{metric('promoters_pct')}} - {{metric('detractors_pct')}}"
-    timestamp: created_at
-    time_grains: [day, month, quarter, year]
-    dimensions:
-      - feedback_source
-      - account_plan
-      - user_type
-
-```
-
-### Option 2: Keeping logic in the modeling layer
-
-But what if we didn’t want to encode all that information in the metric definitions? If we didn’t need the ability to dig into the component metrics and only wanted to look at the final score? In that case, we could encode most of the logic into the model itself and define the metric on top of that!
-
-Thinking through this, we know that our NPS Score is a series of ratios dependent on conditions of which category people fall into with the end result being a number between 100 to -100. That number is usually then *displayed* in a percentage format but it is *calculated* as a number.
-
-So in order to reduce the complexity of metric code, we can add a new field into the model that assigns an `nps_value` to each survey received. The logic for this field would assign a value of 100, 0, or -100 depending on the survey’s `nps_category`. Example code below:
-
-```sql
-case
-	when nps_category = 'detractor' then -100
-	when nps_category = 'promoter' then 100
-	else 0
-end as nps_value
-```
-
-The end result of adding this code to the model would look something like this:
-
-| feedback_date | unique_id | feedback_source | user_type | account_plan | score | nps_category | nps_value |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| 2022-10-01 | 1 | nps_tool_1 | developer | team | 5 | detractor | -100 |
-| 2022-10-01 | 2 | nps_tool_2 | read_only | developer | 9 | promoter | 100 |
-| 2022-10-02 | 3 | nps_tool_1 | developer | enterprise | 10 | promoter | 100 |
-| 2022-10-02 | 4 | nps_tool_1 | developer | developer | 7 | passive | 0 |
-| 2022-10-02 | 5 | nps_tool_2 | developer | team | 9 | promoter | 100 |
-| 2022-10-03 | 6 | nps_tool_1 | developer | enterprise | 7 | passive | 0 |
-
-Now that each survey has an associated `nps_value` we can forgo the ratio calculations used in the Metric Logic section and create our NPS Score metric as a single average metric.
-
-```yaml
-metrics:
-  - name: nps_score
-    label: NPS Score
-    model: ref('customer_nps')
-    calculation_method: average
-    expression: nps_value
-    timestamp: created_at
-    time_grains: [day, month, quarter, year]
-    dimensions:
-      - feedback_source
-      - account_plan
-      - user_type
-```
-
-<details>
-  <summary>Why does this work?</summary>
-
-This is a slightly different way of calculating NPS from the usually provided formula but it ends up with the same result. Here is why:
-
-- `promoter_pct` was defined as `total_promoter_respondents` / `total_respondents`
-    - In our example dataset, this nets out to 3 / 6 = 50%.
-    - If we instead assign a value of 100 and take the average, it becomes 300 / 6 = 50.
-- `detractor_pct` was defined as `total_detractor_respondents` / `total_respondents`
-    - In our example dataset, this nets out to 1 / 6 = 16.67%.
-    - If we instead assign a value of 100 and take the average, it becomes -100 / 6 = -16.67.
-- Therefore, our `nps_score` follows suit:
-    - In our example dataset, 50% - 16.67% = 33.33%
-    - If we instead assign a value of 100 and take the average, it becomes 200 / 6 = 33.33
-
-The underlying principle of why this works is based on the fact that averages divide the sum of the values in the set by their number. In more dbt friendly terms, what it really means is that average is creating the following equation: `sum(value)/count(*)`. In the first example implementation, we were doing roughly the same thing with multiple metric definitions - the only difference was our numerator was a count that assigned each row a value of 1. So if we duplicate that logic and give each row a value of 1 then we can create far fewer metrics.
-
-But that only gets us to the `promoter_pct` and `detractor_pct` metrics. In order to combine these both into a single metric definition, we needed to change the value that we assign. Given that the total range of values that the metric could output is -100 (all detractors) to 100 (all promoters) we can assign each of those categories that peak value, along with 0 for passives. This means that when the numerator is aggregated, it nets out promoters against detractors just like the documented equation does `promoter score - detractor score` .
-
-</details>
-
-**Is this what I should do?**
-
-[It depends!](https://twitter.com/SBinLondon/status/1413113782214266885) There will be times when it might be better to have logic stored in the modeling layer and there will be times when it might be better to have logic stored in the semantic layer. Our shorthand is to only include logic in the semantic layer if it is needed by our stakeholders - if they don't need to analyze the components, we keep them in the modeling layer. In the end, the needs of your business stakeholders should drive your decision on where to keep this logic.
-
-## How to structure your metrics
-
-Now that we’ve designed our metrics, let's move on to structuring them within our project. We'll examine the different ways to organize metrics  and take a look at the pros and cons of several strategies.
-
-### Folder structure
-
-If you follow [dbt’s best practices for structuring your project](https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview), you will have a folder structure that looks similar to this:
-
-```yaml
-models:
-  staging:
-  intermediate:
-  marts:
-```
-
-Your marts folder would most likely contain your end-state models ready for business consumption. Given that metrics are meant for business consumption, we are presented with two options - staying within the same framework or representing metrics as their own level.
-
-We recommend Option A (metrics within marts) but recognize that some people might prefer Option B (metrics within models).
-
-**A. Metrics within marts**
-
-Create a metrics folder within marts and use this to contain all of your metric definitions.
-
-```yaml
-models:
-  staging:
-  intermediate:
-  marts:
-    metrics:
-```
-
-**B. Metrics within models**
-
-Create a metrics folder within models and use this to contain all of your metric definitions.
-
-```yaml
-models:
-  staging:
-  intermediate:
-  marts:
-  metrics:
-```
-
-### File structure
-
-Once you’ve decided ***where*** to put your metrics folder, you can now decide ***how*** you want to structure your metrics within this folder. Choose one of two methods for structuring metrics:
-
-**Option A: The all-in-one YML method**
-This method follows a similar pattern to [dbt’s best practices around model structure](https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview). The introduction of the metrics folder is the only change from the standard best practice.
-
-In practice, the all-in-one YML method would look like the following:
-
-```yaml
-## Metrics within Marts
-models:
-  marts: 
-    metrics:
-      - metrics.yml
-------
-## Metrics within Models
-models:
-  metrics:
-    - metrics.yml
-```
-
-**Option B: The single-metric-per-file method**
-In this method, you create *one* yml file for *each* metric*.* Although this is an opinionated stance that differs from [dbt’s best practices](https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview), here are some reasons why this **could** be useful:
-
-- Individual files are more easily discovered by new analytics engineers as your organization expands
-- Individual files can more easily define specific code owners that may not be part of the data team.
-
-For example, Jerry from the Finance department is the code owner for the `revenue` metric definition and oversees it for the business. So, any change to this specific file would need Jerry’s sign-off.
-
-This can be tricky for code owners who aren’t familiar with your git flow, but it brings them into the chain of responsibility for the metric definition. It also helps them take ownership for reporting on this metric and creates a responsible party when definitions need to change.
-
-The single-file-code-owner method would look like this:
-
-```yaml
-models:
-  metrics:
-    marts:
-      - revenue.yml
-      - average_order_value.yml
-      - some_other_metric_name.yml
-```
-
-### Folder and file structure is a preference, not a hard rule
-
-In the end, all of the structuring information above is just a recommendation. Your project probably has a defined convention in how nodes are organized, whether or not it follows dbt best practices, and you should continue to follow your own organizational practices. That said, we do recommend that metrics should be separate from model yml files. The reason?
-
-**Metrics are important business objects unto themselves and should live separate from the model definitions.**
-
-## A call to action
-
-This is just the beginning of dbt metrics and the Semantic Layer. We have a number of exciting ideas for expanding capabilities that we plan to begin work on in the coming months. However, we can’t do that without you.
-
-This semantic layer is a fundamental change to what it means to interact with dbt and ultimately most of the best practices will come from the dbt Community - folks like you. It does not matter if you consider yourself an "expert" on this - we want to talk to you and hear how you are using or would like to use metrics and the semantic layer.  Y’all are going to be our guiding light to help us make sure that all the functionality we add helps **you** serve the needs of your business.
-
-If your experience with the Semantic Layer match what we’ve written in this post, and especially if they don’t, please share [comments and feedback in this Discourse Discussion](https://discourse.getdbt.com/t/how-to-design-and-structure-metrics/5040)!
-
-Additionally, I would invite you to join us over at #dbt-core-metrics on the dbt Slack where we’ll be posting updates, answering questions, discussing usage, and hopefully responding with the best emojis.

From f56c32183b0e89a967549f5cca6e50aae697bf6a Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 17 Jul 2023 12:00:14 +0100
Subject: [PATCH 115/703] Delete
 2022-07-27-understanding-the-components-of-the-dbt-semantic-layer.md

deleting blog related to legacy semantic layer per convo with roxi and jordan
---
 ...he-components-of-the-dbt-semantic-layer.md | 173 ------------------
 1 file changed, 173 deletions(-)
 delete mode 100644 website/blog/2022-07-27-understanding-the-components-of-the-dbt-semantic-layer.md

diff --git a/website/blog/2022-07-27-understanding-the-components-of-the-dbt-semantic-layer.md b/website/blog/2022-07-27-understanding-the-components-of-the-dbt-semantic-layer.md
deleted file mode 100644
index 3615a6204d6..00000000000
--- a/website/blog/2022-07-27-understanding-the-components-of-the-dbt-semantic-layer.md
+++ /dev/null
@@ -1,173 +0,0 @@
----
-title: "Understanding the components of the dbt Semantic Layer"
-description: "Heard about dbt Metrics or the dbt Semantic Layer and curious to give them a try? Callum McCann digs into what they are, walks through an example, and discusses how they all fit together!"
-slug: understanding-the-components-of-the-dbt-semantic-layer
-
-authors: [callum_mccann]
-
-tags: [dbt product updates]
-hide_table_of_contents: false
-
-date: 2022-07-27
-is_featured: true
----
-
-# Getting started with the dbt Semantic Layer
-
-> TLDR: The Semantic Layer is made up of a combination of open-source and SaaS offerings and is going to change how your team defines and consumes metrics.
-
-At last year's Coalesce, Drew showed us the future[^1] - a vision of what metrics in dbt could look like. Since then, we've been getting the infrastructure in place to make that vision a reality. We wanted to share with you where we are today and how it fits into the broader picture of [where we're going](https://www.getdbt.com/blog/dbt-semantic-layer).
-
-To those who haven't followed this saga with the intensity of [someone watching their investments on the crypto market](https://mobile.twitter.com/scannergr1/status/1536198701215109122/photo/1), we're rolling out this new resource to help you better understand the dbt Semantic Layer and provide clarification on the following things:
-
-1. What is the dbt Semantic Layer?
-2. How do I use it?
-3. What is publicly available now?
-4. What is still in development?
-
-With that, lets get into it!
-
-<!--truncate-->
-
-> Some of you might have been around when this was initially being referred to as the Metrics Layer. As we evaluated the long term plans for what this part of dbt was going to become, we realized that naming it the Semantic Layer better reflected its capabilities and where we plan on taking it. 
-
-## What is the dbt Semantic Layer?
-
-The dbt Semantic Layer is a new part of dbt to help improve precision and consistency while expanding flexibility and capability in the modern data stack. Our maestro of metrics, Drew Banin, [released a blog post detailing the vision of where we're going here](https://www.getdbt.com/blog/dbt-semantic-layer). The first use case that we are addressing is one that most practicioners **and** stakeholders are familiar with - metrics. We'll walk through what this looks like in practice later on in this post.
-
-Under the hood, the dbt Semantic layer is collection of several components - some of these are part of dbt Core, some part of dbt Cloud, and some are net new functionality. They all [combine together like Voltron](https://www.youtube.com/watch?v=5rPSLQxMT8w) to create a single experience through which business users can query data in the context of the metric that is most familiar to them. And the best part is that they can do it in systems they are already comfortable using.
-
-***What will this look like for my data consumers and business stakeholders?*** 
-
-Ultimately, this looks like people being able to interact with trusted datasets in the tools that they are comfortable with (and eventually new tools designed specifically around metrics). 
-
-An example that we’ve found helpful is [ARR](https://www.zuora.com/billing-topics/annual-recurring-revenue/#:~:text=Annual%20Recurring%20Revenue%2C%20or%20ARR,for%20a%20single%20calendar%20year). A business-critical metric to SaaS companies, ARR can be a tricky calculation to keep consistent across all of the tools used in the business. With the dbt Semantic Layer, this definition would live in dbt and the logic to create the dataset for that metric would be consistent across all different consuming experiences. Best of all, definition changes would get reflected in downstream tools, so you no longer need to manually search and update every downstream dependency. Callum of 3 years ago is jumping with joy.
-
-***That’s good and all, but what does this look like for practitioners to use?*** 
-
-The dbt Semantic layer is comprised of the following components[^2]:
-
-**Available Today**
-
-- **[`metric` node in dbt Core :](/docs/build/metrics)** Similar to `models` or `sources` , this is a specific node type in dbt Core. It is the definition of a time-series aggregation over a table that supports zero or more dimensions. The resulting node is stored in the `manifest.json` just like `models` and referenced in the DAG.
-- **[`dbt_metrics` package:](https://github.com/dbt-labs/dbt_metrics)** this package provides macros that combine the version-controlled metric definition and query-time parameters (like dimensions, a time grain, and secondary calculations) to generate a SQL query which calculates the metric value.
-- **[dbt Cloud Metadata API](https://docs.getdbt.com/docs/dbt-cloud-apis/metadata-api):** a GraphQL API which supports arbitrary queries over the metadata produced by dbt Cloud jobs. Contains metadata related to the accuracy, recency, configuration, and structure of the views and tables in the warehouse, as well as much more.
-
-**New**
-
-- **dbt Server:** this component wraps dbt Core in a persistent server that is responsible for handling RESTful API requests for dbt operations. It’s a thin interface that is primarily responsible for performance and reliability in production environments.
-- **dbt Cloud proxy server:** this component enables dbt Cloud to dynamically rewrite requests to a data warehouse and compile dbt-SQL into raw SQL that the database understands. It then returns the dataset produced by the raw SQL to the platform that sent it. 
-
-![Untitled](/img/blog/2022-07-27-getting-started-with-the-dbt-semantic-layer/semantic-layer-description.png)
-
-### Understanding how and when to use metrics?
-
-> Use of metrics and the metrics package is recommended for experienced dbt users and early adopters who want to explore this functionality.
-
-Let's walk through an example of how you can use the components above to get started today using our old friend - [the Jaffle Shop](https://github.com/dbt-labs/jaffle_shop_metrics). We'll take a look at how you can start defining and testing metrics today as well as how you'll interact with them once the new components are released.
-
-**When to use Metrics**
-
-The first question you need to ask is, *Should we be using metrics?* 
-
-It is our belief that metrics are not a one-size fits all solution. They are designed for core business metrics where consistency and precision are of key importance, not for exploratory use cases or ad hoc analysis. Our shorthand way of determining whether the metric should be defined in dbt has been - *is this something our teams need to report on?*
-
-So, let’s say the CFO of our Jaffle comes to us on a Monday morning and commands the data team to overhaul how we're reporting on Revenue. Our Regional Manager Jim and Sales Director Pam[^3] have been giving him different reports! Right now its a mess of tools and inconsistencies - Jim’s numbers are defined in Tableau and say one thing, Pam’s within Hex and say another! The CFO is frustrated with it and wants a cohesive experience across the company where everyone has the same numbers for revenue. It passes the report test, it’s an important business metric; away we go!
-
-**Defining the Metric with Metric Node**
-
-In this example, we’ll say that both Jim and Pam are pulling from a table created by dbt called `orders`. It currently contains fields for `amount` and all different methods of payment_amounts, such as credit cards or gift cards.  Jim has been calculating revenue by summing up the `credit_card_amount` and `gift_card_amount` fields, as he forgot to update his definition when the business added coupons and bank transfers payments. Meanwhile, Pam is correctly summing the `amount` field but hasn’t accounted for return orders that shouldn’t be counted!
-
-The first step is creating a unified definition for what revenue is. In order to do this, we will create the following yml definition within our dbt repo: 
-
-```yaml
-version: 2
-
-metrics:
-  - name: revenue
-    label: Revenue
-    model: ref('orders')
-    description: "The total revenue of our jaffle business"
-
-    type: sum
-    sql: amount
-
-    timestamp: order_date
-    time_grains: [day, week, month, year]
-
-    dimensions:
-      - customer_status
-      - has_coupon_payment
-      - has_bank_transfer_payment
-      - has_credit_card_payment
-      - has_gift_card_payment
-
-    filters:
-      - field: status
-        operator: '='
-        value: "'completed'"
-```
-
-This metric has now been defined in the dbt metadata and can be seen in the DAG! 
-
-![Untitled](/img/blog/2022-07-27-getting-started-with-the-dbt-semantic-layer/metrics-dag.png)
-
-**Running The Metric Package To calculate the metric**
-
-In order to ensure that both Jim and Pam are retrieving the same numbers for their metric, we’ll need them to both run a metrics `calculate` query. In this example, we’re not interested in the specific payment types and only want to see revenue broken up by `week` and `customer_status`.
-
-```sql
-select * 
-from {{ metrics.calculate(
-    metric('revenue'),
-    grain='week',
-    dimensions=['customer_status']
-) }}
-```
-This would return a dataset that looks like this:
-
-| date_week | customer_status | revenue |
-| --- | --- | --- |
-| 2018-01-01 | Churn Risk | 43 |
-| 2018-01-01 | Churned | 0 |
-| 2018-01-01 | Healthy | 26 |
-| 2018-01-08 | Churn Risk | 27 |
-
-Jim and Pam would then be able to reference the `revenue` column within the newly created dataset and never have to worry about the calculation of revenue ever again[^4]! The world is perfect and [balance has been restored.](https://www.youtube.com/watch?v=d1EnW4kn1kg) 
-
-**In the near future with dbt Server**
-
-When dbt Server releases later this year, the flow of how metrics are consumed will change significantly. Your organization will no longer need to materialize each metric within a model in order to take advantage of the metric definition. Instead, you’ll be able to directly query dbt Server with the metric code provided and have the correct dataset returned to your BI tool of choice. 
-
-Additionally, integration partners will have built out experiences around Metrics using the Metadata API to create unique and creative ways for consumers to obtain metric data while abstracting away complexity. For example, a box that allows the user to select from a list of metrics, time grains, dimensions, and secondary calculation and then have the correct information returned to them regardless of the selection!
-
-### So what is publicly available now?
-
-Right now, the two main open-source components that are publicly available are the [`metric` node](/docs/build/metrics) within dbt Core and the `dbt_metrics` package. Combined, these two can operate an introductory semantic layer experience by allowing analytics engineers to define metrics and then query that metric via the metrics package.
-
-These two components are a static experience that have to be defined in the dbt project (as the selected dimensions are defined at model creation) but are useful for those who want to ensure that metrics remain consistent across every BI tool. If you identify with any of the following conditions, you could be a good fit for implementing this as it exists today:
-
-- You want to prepare your organization for the full Semantic Layer launch.
-- Your organization has at least a few key metrics
-- Your organization uses 1 or more BI tools
-- Your organization occasionally has issues around different metric calculations
-- Your organization wants a centralized location for all metrics so everyone in the business knows where to look
-
-All of these are great reasons to begin exploring implementing metrics in your dbt project! If you’re curious about what an implementation of this might look like, we recommend referencing the [jaffle_shop_metrics](https://github.com/dbt-labs/jaffle_shop_metrics) repo!
-
-### What is still in development?
-
-Both the dbt Cloud proxy server and dbt Server are currently in development, with a scheduled release of later this year. If you’re curious about testing them once they are released, we recommend keeping an eye on our product announcements and then reaching out once they become publicly available! 
-
-### What if I have questions?
-
-If you have any questions about those components, or metrics in general, please feel free to post in the #dbt-metrics-and-server channel on dbt Slack! I hang around there and am always willing to chat metrics!
-
-### Footnotes
-[^1]: That future may not have mentioned robots but I'm holding out for [Jetson's style morning machine](https://www.youtube.com/watch?v=-0S3Jf-NxdI) to help me get ready in the morning.
-
-[^2]: We’re specifically calling out the licensing because there is a lot of confusion in the community around what is open-source and what isn’t. This is only becoming trickier with the introduction of the BSL licensing, which ensures users can run their own server but it cannot be sold as a cloud service. For more information on why these licensing types were picked, we recommend [Tristan’s blog around licensing dbt.](https://www.getdbt.com/blog/licensing-dbt/). The big takeaway around licensing is that you can still run components of the dbt Semantic Layer even if you aren’t a dbt Cloud customer! 
-
-[^3]: Full transparency, I've never seen the Office. The awkward humor makes me so uncomfortable that I have to turn off the TV. Apologies if the titles of the characters are incorrect.
-
-[^4]: Psych! They’re definitely interested in the calculation of ARR. In fact, they don’t really trust the numbers **unless** they understand how it’s calculated. This is where they could use the Metadata API in order to query all the information about the metric, such as definition, run-time, acceptable dimensions, etc. Right now Jim and Pam would need to query the API directly but in the future we expect there to be a number of different ways to obtain this information, ranging from [direct integration with the BI tool](https://learn.hex.tech/docs/connect-to-data/data-connections/dbt-integration) all the way to having that information materialized in a dbt information schema! *For current tabular alternatives, there are some interesting macros in the newly released [dbt-project-evaluator package](https://github.com/dbt-labs/dbt-project-evaluator). Take a look there if you’re curious about materializing your metric information!*
\ No newline at end of file

From 7e3575471308edbe31d83f637dfa1106056b7407 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 12:37:36 +0100
Subject: [PATCH 116/703] tweaks from meeting

---
 website/dbt-versions.js                       |   4 ++
 .../docs/docs/build/build-metrics-intro.md    |  14 +++---
 .../sl-api-overview.md                        |  16 +++----
 .../sl-graphql.md}                            |   8 ++--
 .../jdbc.md => dbt-cloud-apis/sl-jdbc.md}     |  14 +++---
 .../sl-manifest.md                            |   0
 .../avail-sl-integrations.md                  |  10 ++---
 .../docs/use-dbt-semantic-layer/dbt-sl.md     |  29 +++++-------
 .../use-dbt-semantic-layer/quickstart-sl.md   |  32 +++----------
 website/docs/guides/migration/sl-migration.md |  42 +++++++++---------
 website/sidebars.js                           |  20 ++++-----
 website/snippets/_legacy-sl-callout.md        |   9 ++--
 website/snippets/_new-sl-setup.md             |   1 +
 website/snippets/_v2-sl-prerequisites.md      |   4 +-
 .../semantic-layer}/sl-architecture.jpg       | Bin
 15 files changed, 90 insertions(+), 113 deletions(-)
 rename website/docs/docs/{use-dbt-semantic-layer => dbt-cloud-apis}/sl-api-overview.md (57%)
 rename website/docs/docs/{use-dbt-semantic-layer/graphql.md => dbt-cloud-apis/sl-graphql.md} (94%)
 rename website/docs/docs/{use-dbt-semantic-layer/jdbc.md => dbt-cloud-apis/sl-jdbc.md} (91%)
 rename website/docs/docs/{use-dbt-semantic-layer => dbt-cloud-apis}/sl-manifest.md (100%)
 rename website/static/img/{ => docs/dbt-cloud/semantic-layer}/sl-architecture.jpg (100%)

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 63fa6034abe..af75087d7c8 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -209,5 +209,9 @@ exports.versionedCategories = [
   {
     "category": "Build your metrics",
     "firstVersion": "1.6",
+  },
+  {
+    "category": "Semantic Layer APIs",
+    "firstVersion": "1.6",
   }
 ]
diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 10febfa5dfc..0cc62b48b55 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -45,24 +45,24 @@ Before you start, keep the following considerations in mind:
     link="/docs/build/metrics-overview"
     icon="dbt-bit"/>
 
- <Card
-    title="Get started with the dbt Semantic Layer"
-    body="Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs"
-    link="/docs/use-dbt-semantic-layer/quickstart-sl"
-    icon="dbt-bit"/>
-
   <Card
     title="About the dbt Semantic Layer"
     body="Introducing the dbt Semantic Layer, the universal process that allows data teams to centrally define and query metrics"
     link="/docs/use-dbt-semantic-layer/dbt-sl"
     icon="dbt-bit"/>
 
+ <Card
+    title="Get started with the dbt Semantic Layer"
+    body="Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs"
+    link="/docs/use-dbt-semantic-layer/quickstart-sl"
+    icon="dbt-bit"/>
+
   <Card
     title="Available integrations"
     body="Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem."
     link="/docs/use-dbt-semantic-layer/avail-sl-integrations"
     icon="dbt-bit"/>
-  
+
 
 </div> <br />
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
similarity index 57%
rename from website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
rename to website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index afe872ec087..4f46f37173f 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -1,5 +1,5 @@
 ---
-title: "Semantic Layer API"
+title: "Semantic Layer APIs"
 id: sl-api-overview
 description: "Integrate and query using the Semantic Layer API."
 tags: ["semantic-layer, apis"]
@@ -16,7 +16,7 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
 The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads.
 
-The dbt Semantic Layer allows users to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that uses your product make more efficient and trustworthy decisions with their data. It also helps avoid duplicative coding, optimizes development workflow, ensures data governance, and guarantee consistency for data consumers.  
+The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) allows users to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that uses your product make more efficient and trustworthy decisions with their data. It also helps avoid duplicative coding, optimizes development workflow, ensures data governance, and guarantee consistency for data consumers.  
 
 You can use the dbt Semantic Layer for a variety of tools and applications of data. Here are some common use cases:
 
@@ -26,26 +26,26 @@ You can use the dbt Semantic Layer for a variety of tools and applications of da
 * Data discovery and cataloging
 * Machine learning and data science
 
-The dbt Semantic Layer APIs are available for accounts on [Team or Enterprise plans](https://www.getdbt.com/pricing/), allowing them to query metrics and build integrations.
+The dbt Semantic Layer APIs are available for accounts on [Team or Enterprise plans](https://www.getdbt.com/pricing/), allowing them to query metrics and build integrations. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
 
 <div className="grid--3-col">
 
 <Card
     title="JDBC API"
     body="Use a JDBC driver to query metrics in downstream tools."
-    link="/docs/use-dbt-semantic-layer/jdbc"
+    link="/docs/dbt-cloud-apis/sl-jdbc"
     icon="dbt-bit"/>
 
 <Card
     title="GraphQL API"
     body="Use GraphQL to query metrics in downstream tools."
-    link="/docs/use-dbt-semantic-layer/graphql"
+    link="/docs/dbt-cloud-apis/sl-graphql"
     icon="dbt-bit"/>
 
 <Card
-    title="Discovery API"
-    body="Uses the Discovery API to query metrics in downstream tools using metadata details about your project’s models, sources, and other nodes along with their execution results."
-    link="/docs/dbt-cloud-apis/discovery-api"
+    title="Semantic manifest"
+    body="Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."
+    link="/docs/dbt-cloud-apis/sl-manifest"
     icon="dbt-bit"/>
 
 </div>
diff --git a/website/docs/docs/use-dbt-semantic-layer/graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
similarity index 94%
rename from website/docs/docs/use-dbt-semantic-layer/graphql.md
rename to website/docs/docs/dbt-cloud-apis/sl-graphql.md
index d43069f6952..9d528f1b3aa 100644
--- a/website/docs/docs/use-dbt-semantic-layer/graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -1,6 +1,6 @@
 ---
 title: "GraphQL"
-id: graphql
+id: sl-graphql
 description: "Integrate and use the GraphQL API to query your metrics."
 tags: ["semantic-layer, apis"]
 ---
@@ -13,9 +13,8 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.6">
 
-## What is GraphQL?
+
 
 GraphQL is an open-source query language for APIs (Application Programming Interfaces). It provides a more efficient and flexible alternative to traditional RESTful APIs. 
 
@@ -26,7 +25,7 @@ Overall, GraphQL offers benefits such as being self-documenting, a strong typing
 
 ### dbt Semantic Layer GraphQL API
 
-The dbt Semantic Layer GraphQL API has the following capabilities, and has similarities to the calls available in our JDBC API.
+The dbt Semantic Layer GraphQL API has the following capabilities and has similarities to the calls available in our JDBC API.
 
 One benefit of GraphQL is its self documenting nature, so you can explore using our schema explorer(todolink). To give you an idea of the types of commands you can execute, we provide some available calls below.
 
@@ -91,4 +90,3 @@ order: [String!] = null
 ): String
 ```
 
-</VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
similarity index 91%
rename from website/docs/docs/use-dbt-semantic-layer/jdbc.md
rename to website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 7fd85836d9b..43f567874bb 100644
--- a/website/docs/docs/use-dbt-semantic-layer/jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -1,6 +1,6 @@
 ---
 title: "JDBC"
-id: jdbc
+id: sl-jdbc
 description: "Integrate and use the JDBC API to query your metrics."
 tags: ["semantic-layer, apis"]
 ---
@@ -25,7 +25,7 @@ A JDBC driver is a software component enabling a Java application to interact wi
 - You can download the JDBC driver from [Maven](https://search.maven.org/remotecontent?filepath=org/apache/arrow/flight-sql-jdbc-driver/12.0.0/flight-sql-jdbc-driver-12.0.0.jar). 
 - The dbt Semantic Layer supports ArrowFlight SQL driver version 12.0.0 and higher. 
 - You can embed the driver into your application stack as needed, and you can use dbt Labs' [example project](https://github.com/dbt-labs/example-semantic-layer-clients) for reference.
-- To call the JDBC API in an application, you should install an AWS root CA to the Java Trust Store [documentation](https://www.amazontrust.com/repository/).
+- If you’re a partner or user building a homegrown application, you’ll need to install an AWS root CA to the Java Trust [documentation](https://www.amazontrust.com/repository/) (specific to Java and JDBC call).
 
 ## Connection parameters
 
@@ -44,9 +44,9 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 | `environmentId` | The unique identifier for the dbt production environment, you can retrieve this from the dbt Cloud URL <br /> when you navigate to **Environments** under **Deploy**. | If your URL ends with `.../environments/222222`, your `environmentId` is `222222`<br /><br />   |
 | `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” permission. Create a new service token in your **Account Settings** page. Encode the value before inserting it into the string.  | `token=SERVICE_TOKEN` |
 
-*Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable into the JDBC URL `&disableCertificateVerification=true`
+*Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable at the end or beginning of the JDBC URL `&disableCertificateVerification=true`.
 
-## Querying the API for Metric Metadata
+## Querying the API for metric metadata
 
 The Semantic Layer JDBC API has built-in metadata calls which can provide a user with information about their metrics and dimensions. Here are some metadata commands and examples:
 
@@ -92,7 +92,7 @@ semantic_layer.dimension_values(metrics=["food_order_amount"], group_by="custome
 
 </Tabs>
 
-## Querying the API for Metric Values
+## Querying the API for metric values
 
 To query metric values, here are the following parameters that are available:
 
@@ -133,7 +133,7 @@ select * from {{
 
 ### Query grouped by time
 
-Use the following example query to fetch revenue and new customers grouped by time: 
+The following example query uses the [shorthand method](#faqs) to fetch revenue and new customers grouped by time:
 
 ```bash
 select * from {{
@@ -204,7 +204,7 @@ semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 ## FAQs
 
 - **Why do some dimensions use different syntax, like `metric_time` versus `[Dimension('metric_time')`?**<br />
-	When you select a dimension on its own, such as `metric_time` you don’t need the “Dimension” syntax. However, when you perform operations on the dimension, such as adding granularity, the object syntax (`[Dimension('metric_time')`) is required. 
+	When you select a dimension on its own, such as `metric_time` you can use the shorthand method which doesn't need the “Dimension” syntax. However, when you perform operations on the dimension, such as adding granularity, the object syntax `[Dimension('metric_time')` is required. 
 
 - **What does the double underscore `"__"` syntax in dimensions mean?**<br />
 	The double underscore `"__"` syntax indicates a mapping from an entity to a dimension, as well as where the dimension is located. For example, `user__country` means someone is looking at the `country` dimension from the `user` table.
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
similarity index 100%
rename from website/docs/docs/use-dbt-semantic-layer/sl-manifest.md
rename to website/docs/docs/dbt-cloud-apis/sl-manifest.md
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 4519b217dc0..86bf89c9cbe 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -13,23 +13,23 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 Discover the diverse range of data applications that seamlessly integrate with the powerful dbt Semantic Layer, powered by MetricFlow &mdash; from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
 
-- Tableau &mdash; 
-- Google Sheets &mdash;
+- Google Sheets  &mdash; coming soon
 - Hex &mdash;
 - Mode &mdash; 
+- Any SQL developer tool that supports a generic driver, such as DataGrip
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 
 ## Related docs
 
-- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview) to learn how to integrate with the JDBC, GraphQL, and Discovery APIs to query your metrics in downstream tools.
+- [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to learn how to integrate with the JDBC, GraphQL, and Discovery APIs to query your metrics in downstream tools.
 
 </VersionBlock>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 242a839412c..ddb36083cec 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -13,17 +13,19 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 <NewSLChanges />
 
 
-The dbt Semantic Layer, powered by MetricFlow, simplifies the process of defining and using critical business metrics, like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
+The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies the process of defining and using critical business metrics, like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
 
 Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 
 To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
-<Lightbox src="/img/sl-architecture.jpg" width="70%" title="Define and query metrics with the dbt Semantic Layer and seamlessly connect with integration tools for reliable organization-wide metrics." />
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="65%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Explore the dbt Semantic Layer
 
-The dbt Semantic Layer is available to all Team and Enterprise Plans during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Those on dbt Cloud Developer plans or dbt core users can use MetricFlow for defining and testing metrics locally. Start exploring the dbt Semantic Layer by visiting the following pages:
+The universal dbt Semantic Layer is available to all Team and Enterprise Plans during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
+
+Start exploring the dbt Semantic Layer by visiting the following pages:
 
 <div className="grid--3-col">
 
@@ -44,12 +46,6 @@ The dbt Semantic Layer is available to all Team and Enterprise Plans during [pub
     body="Review a wide range of partners you can integrate and query with the dbt Semantic Layer."
     link="/docs/use-dbt-semantic-layer/avail-sl-integrations"
     icon="dbt-bit"/>
-    
-<Card
-    title="Semantic Layer APIs"
-    body="Use the Semantic Layer APIs to query metrics in downstream tools for consistent, reliable data metrics."
-    link="/docs/use-dbt-semantic-layer/sl-api-overview"
-    icon="dbt-bit"/>
 
 <Card
     title="Product architecture"
@@ -57,6 +53,12 @@ The dbt Semantic Layer is available to all Team and Enterprise Plans during [pub
     link="/docs/use-dbt-semantic-layer/sl-architecture"
     icon="dbt-bit"/>
 
+<Card
+    title="Semantic Layer API"
+    body="Use the Semantic Layer APIs to query metrics in downstream tools for consistent, reliable data metrics."
+    link="/docs/dbt-cloud-apis/sl-api-overview"
+    icon="dbt-bit"/>
+
 </div>
 
 </VersionBlock>
@@ -73,15 +75,12 @@ The result? You have less duplicate coding for data teams and more consistency f
 
 The dbt Semantic Layer has four main parts:
 
-- Define your metrics in version-controlled dbt project code using MetricFlow 
+- Define your metrics in version-controlled dbt project code using [MetricFlow](/docs/build/about-metricflow) 
 	* dbt_metrics is now deprecated
 - Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
 - Query your metric data via the dbt Proxy Server
 - Explore and analyze dbt metrics in downstream tools
 
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
-
 ### What makes the dbt Semantic Layer different?
 
 The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
@@ -126,14 +125,10 @@ To read about best practices on structuring and organizing your metrics, review
 **Define metrics**
 You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
 
-- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project
 - [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics, and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
 - [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
 - [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
     
-Review our helpful metrics video below, which explains what metrics are, why they're important, and how you can get started:
-    
-<LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
 
 ## Related questions
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index b5cec90c58a..da62001c3d9 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -21,10 +21,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - [Create your metrics](#create-your-metrics) with MetricFlow
 - [Run your production job](#run-your-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#setup) in dbt Cloud
-- [Connect to the Semantic Layer APIs](#connect-to-apis)
-- [Query your metrics using partner integrations](#query-metrics)
-
-
+- [Connect and Query APIs](#connect-and-query-apis)
 
 ## Prerequisites
 
@@ -142,38 +139,21 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 <SlSetUp/>
 
 
-## Connect to the APIs
+## Connect and query APIs
 
 add content here
 
-## Query your metrics 
-
-add content
-
 ## FAQs
 
 If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
     
 <details>
-  <summary>How are you storing my data?</summary>
-  <div>
-    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
-  </div>
+<summary>How are you storing my data?</summary>
+The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.
 </details>
 <details>
-    <summary>Is the dbt Semantic Layer open source?</summary>
-  <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
-
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
-<ul>    
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, you will need to have a dbt Cloud account.</li><br></br><br></br>
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definitions.</li>
-    </ul>
-    </div>
-    </div>
-</details>
+<summary>Is the dbt Semantic Layer open source?</summary>
+The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 
    
 ## Next steps
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 371f94bea22..0fcfa557a5f 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -10,11 +10,11 @@ The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.c
 
 This guide is for users who have the legacy dbt Semantic Layer set up and would like to migrate to the new and revamped  [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). The legacy dbt Semantic Layer is supported on dbt v1.5 or lower, uses the dbt_metrics package, and will be deprecated. 
 
-**Note**: Migrating to the new semantic layer will require downtime and this guide will explain how to minimize it.
+**Note**: Migrating to the new Semantic Layer will require downtime and this guide will explain how to minimize it.
 
 ## Migration components
 
-In order to migrate from the legacy semantic layer to the new one, there are three migration components you'll need to complete: 
+In order to migrate from the legacy Semantic Layer to the new one, there are three migration components you'll need to complete: 
 
 - Create semantic models and migrate metric definitions
 - Setup the new dbt Semantic Layer in dbt Cloud
@@ -28,9 +28,9 @@ This guide assumes you have an environment with the legacy Semantic Layer enable
 
 ## Migrate an existing environment
 
-Switching to th new semantic layer requires a complete transition. This is because the legacy semantic layer will no longer work once you update your metrics to the new specifications in dbt v1.6 or higher. 
+Switching to th new Semantic Layer requires a complete transition. This is because the legacy Semantic Layer will no longer work once you update your metrics to the new specifications in dbt v1.6 or higher. 
 
-To avoid any disruptions or downtime, you _must_ set up the new semantic layer in your deployment environment after upgrading to the new metrics spec.
+To avoid any disruptions or downtime, you _must_ set up the new Semantic Layer in your deployment environment after upgrading to the new metrics spec.
 
 ### Step 1: Migrate metrics
 
@@ -60,22 +60,22 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy semantic layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new semantic layer.
+Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new Semantic Layer.
 
 ### Step 2: Switch to the new Semantic Layer
 
-Now that you've migrated your metrics and created semantic models, it's time to set up your environment using the new semantic layer configuration. 
+Now that you've migrated your metrics and created semantic models, it's time to set up your environment using the new Semantic Layer configuration. 
 
-To upgrade your dbt Cloud environment to use the new semantic layer:
+To upgrade your dbt Cloud environment to use the new Semantic Layer:
 
 1. [Upgrade](/docs/dbt-versions/upgrade-core-in-cloud) the existing deployment environment for your project to v1.6 or higher.
 Ensure that all jobs run successfully with the new version.
-2. Run a dbt job in your deployment environment to set up the new semantic layer. You can use any dbt command, as long as it completes successfully. 
+2. Run a dbt job in your deployment environment to set up the new Semantic Layer. You can use any dbt command, as long as it completes successfully. 
 3. To activate the new Semantic Layer, go to the **Account Settings** page and select the specific project you want to enable the Semantic Layer for.
 3. On the **Project Details** page, select **Configure Semantic Layer** 
-4. Enter the connection details for the data platform and select the environment where you want to enable the semantic layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
+4. Enter the connection details for the data platform and select the environment where you want to enable the Semantic Layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
 5. After you’ve entered your credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. You can use this to connect to the Semantic Layer JDBC interface, which you will use in the next step. This interface allows you to connect and query your metrics in downstream tools.
-6. Go back to the **Project Details** page and select **Generate Service Token** to create a semantic layer service token. Save this token for later.
+6. Go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
 7. You’re done 🎉! The new dbt Semantic Layer is now enabled for your project an you can start querying metrics.
 
 ### Step 3: Update connection in downstream integrations
@@ -91,26 +91,26 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 ## Create environment for the new Semantic Layer
 
-This approach allows you to run the legacy semantic layer in your existing environment, while you stage the metrics spec changes to your dbt project. 
+This approach allows you to run the legacy Semantic Layer in your existing environment, while you stage the metrics spec changes to your dbt project. 
 
-In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current semantic layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
+In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current Semantic Layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
 
 ### Step 1: Setup a new environment
 
-You'll need to create a new environment for the new semantic layer and migrate your existing jobs to this environment:
+You'll need to create a new environment for the new Semantic Layer and migrate your existing jobs to this environment:
 
 1. In dbt Cloud, select **Deploy** and then **Environments** to create a new [deployment environment](/docs/deploy/deploy-environments).
 2. Select **dbt Version 1.6** (or the latest) and enter your deployment credentials.
-    * There needs to be a successful run in your new environment to configure the  semantic layer. We recommend running `dbt ls`, since `dbt build` won’t succeed until you’ve updated your metrics configs.
+    * There needs to be a successful run in your new environment to configure the  Semantic Layer. We recommend running `dbt ls`, since `dbt build` won’t succeed until you’ve updated your metrics configs.
 3. You'll need to migrate your jobs from your old deployment environment. You can do this by manually creating and configuring the new jobs in the new environment.
 4. To activate the new Semantic Layer, go to the Account Settings page and select the specific project you want to enable the Semantic Layer for.
 5. On the **Project Details** page, select **Configure Semantic Layer**.
-6. Enter the connection details for the data platform and select the environment where you want to enable the semantic layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
-7. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+6. Enter the connection details for the data platform and select the environment where you want to enable the Semantic Layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
+7. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the Semantic Layer JDBC.
 7. Go back to **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
-8. You’re done ✨ ! The semantic layer should now be enabled for your project! 
+8. You’re done ✨ ! The Semantic Layer should now be enabled for your project! 
 
-The legacy semantic layer will still be running in your old environment so make sure you complete step two to migrate your metrics configs to the new spec. Once you’ve migrated your metrics configs, you can safely deprecate the old environment.
+The legacy Semantic Layer will still be running in your old environment so make sure you complete step two to migrate your metrics configs to the new spec. Once you’ve migrated your metrics configs, you can safely deprecate the old environment.
 
 ### Step 2: Migrate metric configs to the new spec
 
@@ -140,11 +140,11 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy semantic layer will no longer work**. 
+Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. 
 
 ### Step 3: Update connection in downstream integrations
 
-Now that your semantic layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
+Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
 **Link to migration guide for Hex**
 
@@ -157,6 +157,6 @@ Now that your semantic layer is set up, you will need to update any downstream i
 - [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
 - [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)
-- [Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
 - Developer workflow demo
diff --git a/website/sidebars.js b/website/sidebars.js
index 028c0543f7d..a2659fdfff8 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -411,16 +411,6 @@ const sidebarSettings = {
         "docs/use-dbt-semantic-layer/setup-sl",
         "docs/use-dbt-semantic-layer/avail-sl-integrations",
         "docs/use-dbt-semantic-layer/sl-architecture",
-        {
-          type: "category",
-          label: "Semantic Layer APIs",
-          link: { type: "doc", id: "docs/use-dbt-semantic-layer/sl-api-overview" },
-          items: [
-            "docs/use-dbt-semantic-layer/jdbc",
-            "docs/use-dbt-semantic-layer/graphql",
-            "docs/use-dbt-semantic-layer/sl-manifest",
-          ],
-        },
       ],
     },
     {
@@ -490,6 +480,16 @@ const sidebarSettings = {
             },
           ],
         },
+        {
+          type: "category",
+          label: "Semantic Layer APIs",
+          link: { type: "doc", id: "docs/dbt-cloud-apis/sl-api-overview" },
+          items: [
+            "docs/dbt-cloud-apis/sl-jdbc",
+            "docs/dbt-cloud-apis/sl-graphql",
+            "docs/dbt-cloud-apis/sl-manifest",
+          ],
+        },
       ],
     },
     {
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index bd0c374dff5..a2d2204babb 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,14 +1,11 @@
 :::info Upgrade your dbt version to access the new dbt Semantic Layer
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. This means the legacy Semantic Layer (dbt v1.5 or lower) is no longer supported and won't receive any code fixes.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. We've introduced improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
 
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt. We also introduce improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
-
-**Who does this affect?** The new and revamped dbt Semantic Layer is available for users on [Team or Enterprise plans](https://www.getdbt.com/pricing/) using dbt v1.6 or higher. 
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt. The dbt_metrics package is no longer supported and won't receive any code fixes.
 
 **What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
--is this correct? no right?-->
-Although you can still use the legacy dbt Semantic Layer using the deprecated dbt_metrics package on v1.5 or older, these versions are no longer supported and won't receive any code fixes. 
+**Who does this affect?** Users on[Team or Enterprise plans](https://www.getdbt.com/pricing/) hosted on a multi-tenant North American [deployment region](/docs/cloud/about-cloud/regions-ip-addresses) and using dbt v1.6 or later can access the newly enhanced dbt Semantic Layer. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 
 :::
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 804b0d4b529..1e8631db14e 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -5,6 +5,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 :::
 
 1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
+    * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
 3. In the **Project Details** page, navigate to **Configure Semantic Layer**.
 4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index e8027f19c1b..7080ef950b9 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -5,10 +5,12 @@
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
+  * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - Install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow) package
   * Note &mdash; After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query manually via the CLI using SQL, but will not be able to dynamically query or browse metrics in external tools, which requires access to the Semantic Layer APIs.<br />
+  * Developer accounts will be able to query manually via the CLI using SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
+
 
 </VersionBlock>
 
diff --git a/website/static/img/sl-architecture.jpg b/website/static/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg
similarity index 100%
rename from website/static/img/sl-architecture.jpg
rename to website/static/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg

From 57d19f3127e7a68dfdf140c063a7f151f339cb86 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 12:40:11 +0100
Subject: [PATCH 117/703] update faq

---
 .../use-dbt-semantic-layer/quickstart-sl.md   |  6 ++++++
 .../use-dbt-semantic-layer/sl-architecture.md | 21 ++++---------------
 2 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index da62001c3d9..5b4251fa28b 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -147,6 +147,12 @@ add content here
 
 If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
     
+<details>
+  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
+  <div>
+    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
+  </div>
+</details>
 <details>
 <summary>How are you storing my data?</summary>
 The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 62a326a1405..1ebb406bc69 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -27,24 +27,12 @@ At a high level, the dbt Semantic Layer has two primary components -- defining y
 </details>
     
 <details>
-  <summary>How are you storing my data?</summary>
-  <div>
-    <div>The dbt Semantic Layer does not store, cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
-  </div>
+<summary>How are you storing my data?</summary>
+The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.
 </details>
 <details>
-  <summary>Is the dbt Semantic Layer open source?</summary>
-  <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
-      
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
-
-<ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
-    </ul></div> </div>
-</details>
+<summary>Is the dbt Semantic Layer open source?</summary>
+The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 <details>
     <summary>Is there a dbt Semantic Layer discussion hub?</summary>
   <div>
@@ -52,7 +40,6 @@ During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (De
     </div>
   </div>
 </details>
-      <br></br>
 
 </VersionBlock>
 

From 990090c70a228c7a0f8c7717256d83e332f76143 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 13:02:53 +0100
Subject: [PATCH 118/703] update build metrics

---
 website/docs/docs/build/build-metrics-intro.md | 11 ++++-------
 .../docs/docs/use-dbt-semantic-layer/dbt-sl.md |  2 --
 .../use-dbt-semantic-layer/quickstart-sl.md    | 18 +++++++++++++-----
 website/snippets/_legacy-sl-callout.md         |  2 +-
 website/snippets/_v2-sl-prerequisites.md       |  2 +-
 5 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 0cc62b48b55..3ecf62c278e 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -12,18 +12,15 @@ Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key comp
 Use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 :::info
-MetricFlow is currently available on dbt Core v1.6 beta for [command line (CLI)](/docs/core/about-the-cli) users, with support for dbt Cloud and integrations coming soon. MetricFlow, a BSL package (code is source available), is a new way to define metrics in dbt and will replace the dbt_metrics package.
+MetricFlow is currently available on dbt v1.6 beta for dbt Cloud users. MetricFlow, is a new way to define metrics in dbt and  replaces the dbt_metrics package.
 
 To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 :::
 
 Before you start, keep the following considerations in mind:
-- Use the CLI to define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
-- You must be on dbt Core v1.6 beta or higher to use MetricFlow. [Upgrade your dbt version](/docs/core/pip-install#change-dbt-core-versions) to get started.
-  * Note: Support for dbt Cloud and querying via external integrations coming soon.
-- MetricFlow currently only supports Snowflake and Postgres.
-  * Note: Support for BigQuery, Databricks, and Redshift coming soon.
-- dbt Labs is working with [integration partners](https://www.getdbt.com/product/semantic-layer-integrations) to develop updated integrations for the new Semantic Layer, powered by MetricFlow, in addition to introducing other consumption methods like Python and JDBC. <br /><br />
+- Define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
+- You must be on dbt v1.6 beta or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
+- MetricFlow currently only supports Snowflake, Postgres, BigQuery, Databricks, and Redshift
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index ddb36083cec..f294b93acc9 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -19,8 +19,6 @@ Moving metric definitions out of the BI layer and into the modeling layer allows
 
 To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="65%" title="The universal dbt Semantic Layer connecting to integration tools."/>
-
 ## Explore the dbt Semantic Layer
 
 The universal dbt Semantic Layer is available to all Team and Enterprise Plans during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 5b4251fa28b..24922ce668d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -93,9 +93,11 @@ group by metric_time_day, country  -- dimensions
 
 ## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). You can define metrics in the same YAML files as your semantic models, or create a new file.
+Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). 
 
-The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
+1. You can define metrics in the same YAML files as your semantic models, or create a new file.
+
+2. The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
 
 ```yaml
 ---
@@ -106,10 +108,16 @@ metrics:
     measure: transaction_amount_usd
 ```
  
-1. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
-2. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
+3. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
+
+
+### Test metrics
+
+The following steps explain how to query and test your metrics, which currently is only available via the CLI. However, IDE support will be available soon:
+
+1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
 3. Run `mf validate-configs` to validate the changes before committing them.
-4. Commit and merge the code changes that contain the metric definitions.
+3. Commit and merge the code changes that contain the metric definitions.
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index a2d2204babb..cd861b7405d 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -6,6 +6,6 @@ The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.c
 
 **What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
-**Who does this affect?** Users on[Team or Enterprise plans](https://www.getdbt.com/pricing/) hosted on a multi-tenant North American [deployment region](/docs/cloud/about-cloud/regions-ip-addresses) and using dbt v1.6 or later can access the newly enhanced dbt Semantic Layer. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
+**Who does this affect?** The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 
 :::
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 7080ef950b9..1063e8e961d 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -6,7 +6,7 @@
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
-- Install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow) package
+- Install the [MetricFlow command line (CLI)](https://github.com/dbt-labs/metricflow) package
   * Note &mdash; After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
   * Developer accounts will be able to query manually via the CLI using SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />

From 289ae348baf02de720778be94dcbca2d788d3910 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 13:09:21 +0100
Subject: [PATCH 119/703] nits

---
 website/docs/docs/dbt-cloud-apis/sl-graphql.md  | 2 --
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md     | 9 +++------
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 8 ++++++++
 website/snippets/_upgrade-new-sl.md             | 3 ++-
 4 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 9d528f1b3aa..a2ef2e93ca3 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -14,8 +14,6 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 </VersionBlock>
 
 
-
-
 GraphQL is an open-source query language for APIs (Application Programming Interfaces). It provides a more efficient and flexible alternative to traditional RESTful APIs. 
 
 With GraphQL, clients can request specific data requirements using a single query. GraphQL allows clients to retrieve all the needed data in a single request. This reduces the number of round trips to the server, minimizing network overhead and improving performance.
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 43f567874bb..b70bfcee923 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -5,15 +5,12 @@ description: "Integrate and use the JDBC API to query your metrics."
 tags: ["semantic-layer, apis"]
 ---
 
-<VersionBlock lastVersion="1.5">
-
-:::info Upgrade to access the new dbt Semantic Layer
 
-The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
+<VersionBlock lastVersion="1.5">
 
-If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 
-:::
+<UpgradeSL />
 
 </VersionBlock>
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 241a137ab42..fd1fe614139 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -5,6 +5,14 @@ description: "Learn about the semantic manifest.json file and how you can use ar
 sidebar_label: "Semantic manifest"
 ---
 
+<VersionBlock lastVersion="1.5">
+
+import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+
+<UpgradeSL />
+
+</VersionBlock>
+
 The dbt Semantic Layer creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
 
 Similar to the `manifest.json` file, the Semantic Manifest also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
index b22b690d046..765b0db8dac 100644
--- a/website/snippets/_upgrade-new-sl.md
+++ b/website/snippets/_upgrade-new-sl.md
@@ -2,6 +2,7 @@
 
 The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
 
-If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+If you're using the legacy Semantic Layer, you **must** [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. The dbt_metrics package has been deprecated and replaced with MetricFlow.
+Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info. 
 
 :::

From 90e65382706c7b9b360eaabde5a47d189b544eb8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 13:24:26 +0100
Subject: [PATCH 120/703] graphql

---
 .../docs/docs/dbt-cloud-apis/sl-graphql.md    | 27 +++++++++----------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index a2ef2e93ca3..24783f89065 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -14,28 +14,27 @@ import UpgradeSL from '/snippets/_upgrade-new-sl.md';
 </VersionBlock>
 
 
-GraphQL is an open-source query language for APIs (Application Programming Interfaces). It provides a more efficient and flexible alternative to traditional RESTful APIs. 
+[GraphQL](https://graphql.org/) is an open-source query language for APIs. It offers a more efficient and flexible approach compared to traditional RESTful APIs. 
 
-With GraphQL, clients can request specific data requirements using a single query. GraphQL allows clients to retrieve all the needed data in a single request. This reduces the number of round trips to the server, minimizing network overhead and improving performance.
+With GraphQL, users can request specific data using a single query, reducing the need for many server round trips. This improves performance and minimizes network overhead.
 
-Overall, GraphQL offers benefits such as being self-documenting, a strong typing system, versioning and evolution support, enabling rapid development, and a robust ecosystem. These advantages make GraphQL a powerful choice for building APIs that prioritize flexibility, performance, and developer productivity.
+GraphQL has several advantages, such as self-documenting, having a strong typing system, supporting versioning and evolution, enabling rapid development, and having a robust ecosystem. These features make GraphQL a powerful choice for APIs that prioritize flexibility, performance, and developer productivity.
 
+## dbt Semantic Layer GraphQL API
 
-### dbt Semantic Layer GraphQL API
+The dbt Semantic Layer GraphQL API offers capabilities like the JDBC API. It allows you to explore and execute commands conveniently through its self-documenting nature using the schema explorer (need link from roxi). 
 
-The dbt Semantic Layer GraphQL API has the following capabilities and has similarities to the calls available in our JDBC API.
+### Metric metadata calls
 
-One benefit of GraphQL is its self documenting nature, so you can explore using our schema explorer(todolink). To give you an idea of the types of commands you can execute, we provide some available calls below.
+Use the following example calls to provide you with an idea of the types of commands you can use:
 
+**Fetch available metrics**
 
-**Metric Metadata Calls**
-
-Fetch available metrics
 ```graphql
 metrics(environmentId: Int!): [Metric!]!
 ```
 
-Fetch available dimensions for metrics
+**Fetch available dimensions for metrics**
 
 ```graphql
 dimensions(
@@ -44,7 +43,7 @@ metrics: [String!]!
 ): [Dimension!]!
 ```
 
-Fetch available time granularities given metrics
+**Fetch available time granularities given metrics**
 
 ```graphql
 queryableGranularities(
@@ -53,7 +52,7 @@ metrics: [String!]!
 ): [TimeGranularity!]!
 ```
 
-Fetch available metrics given a set of a dimensions
+**Fetch available metrics given a set of a dimensions**
 
 ```graphql
 metricsForDimensions(
@@ -62,7 +61,7 @@ dimensions: [String!]!
 ): [Metric!]!
 ```
 
-Fetch dimension values for metrics and a given dimension
+**Fetch dimension values for metrics and a given dimension**
 
 ```graphql
 dimensionValues(
@@ -71,7 +70,7 @@ metrics: [String!]!
 dimension: String!
 ```
 
-**Metric Value Query Parameters**
+### Metric value query parameters
 
 The mutation is `createQuery`. The parameters are as follows:
 

From 76bf6575962d9e3547cd45e309f95cf6bb1e23a6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 13:33:43 +0100
Subject: [PATCH 121/703] fix links

---
 website/docs/docs/dbt-cloud-apis/apis-overview.md | 2 +-
 website/docs/docs/dbt-cloud-apis/sl-manifest.md   | 2 +-
 website/docs/reference/artifacts/dbt-artifacts.md | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/apis-overview.md b/website/docs/docs/dbt-cloud-apis/apis-overview.md
index 8323e3423da..b7d722747d8 100644
--- a/website/docs/docs/dbt-cloud-apis/apis-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/apis-overview.md
@@ -12,7 +12,7 @@ dbt Cloud provides the following APIs:
 
 - The [dbt Cloud Administrative API](/docs/dbt-cloud-apis/admin-cloud-api) can be used to administrate a dbt Cloud account.
 - The [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api) can be used to fetch metadata related to the state and health of your dbt project.
-- The [dbt Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) provides multiple API options which allow you to query your metrics defined in the dbt Semantic Layer.
+- The [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) provides multiple API options which allow you to query your metrics defined in the dbt Semantic Layer.
 
 If you want to learn more about webhooks, refer to [Webhooks for your jobs](/docs/deploy/webhooks).
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index fd1fe614139..b7f42ba277e 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -881,6 +881,6 @@ QUESTIONS:
 
 ## Related docs
 
-- [dbt Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [dbt Semantic Layer APIs](/docs/docs/dbt-cloud-apis/sl-api-overview)
 - [About dbt artifacts](/reference/artifacts/dbt-artifacts)
 
diff --git a/website/docs/reference/artifacts/dbt-artifacts.md b/website/docs/reference/artifacts/dbt-artifacts.md
index 84a7a40c986..9efd50f4665 100644
--- a/website/docs/reference/artifacts/dbt-artifacts.md
+++ b/website/docs/reference/artifacts/dbt-artifacts.md
@@ -22,7 +22,7 @@ dbt has produced artifacts since the release of dbt-docs in v0.11.0. Starting in
 ## When are artifacts produced?
 
 Most dbt commands (and corresponding RPC methods) produce artifacts:
-- [semantic manifest](/docs/use-dbt-semantic-layer/sl-manifest): Lives in the `/target` directory of your dbt project and stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+- [semantic manifest](/docs/docs/dbt-cloud-apis/sl-manifest): Lives in the `/target` directory of your dbt project and stores various artifacts (such as compiled models and tests) generated during the execution of your project.
 - [manifest](/reference/artifacts/manifest-json): produced by commands that read and understand your project
 - [run results](/reference/artifacts/run-results-json): produced by commands that run, compile, or catalog nodes in your DAG
 - [catalog](catalog-json): produced by `docs generate`

From b86e0ca9fb2a48f5bc3b6103a30e1767acb0710c Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 13:42:07 +0100
Subject: [PATCH 122/703] fix links

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md      | 2 +-
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 website/docs/reference/artifacts/dbt-artifacts.md    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index b7f42ba277e..a36e17d48fe 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -881,6 +881,6 @@ QUESTIONS:
 
 ## Related docs
 
-- [dbt Semantic Layer APIs](/docs/docs/dbt-cloud-apis/sl-api-overview)
+- [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview)
 - [About dbt artifacts](/reference/artifacts/dbt-artifacts)
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 32d94fc8561..7775ff86d97 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -100,6 +100,6 @@ Note  - It is _not_ recommended that you use your dbt Cloud credentials due to e
 
 - [Build your metrics](/docs/build/build-metrics-intro)
 - [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
-- [Semantic Layer APIs](/docs/use-dbt-semantic-layer/sl-api-overview)
+- [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview)
 - [Migrate your legacy Semantic Layer](/guides/migration/sl-migration)
 - [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
diff --git a/website/docs/reference/artifacts/dbt-artifacts.md b/website/docs/reference/artifacts/dbt-artifacts.md
index 9efd50f4665..2a73f477747 100644
--- a/website/docs/reference/artifacts/dbt-artifacts.md
+++ b/website/docs/reference/artifacts/dbt-artifacts.md
@@ -22,7 +22,7 @@ dbt has produced artifacts since the release of dbt-docs in v0.11.0. Starting in
 ## When are artifacts produced?
 
 Most dbt commands (and corresponding RPC methods) produce artifacts:
-- [semantic manifest](/docs/docs/dbt-cloud-apis/sl-manifest): Lives in the `/target` directory of your dbt project and stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+- [semantic manifest](/docs/dbt-cloud-apis/sl-manifest): Lives in the `/target` directory of your dbt project and stores various artifacts (such as compiled models and tests) generated during the execution of your project.
 - [manifest](/reference/artifacts/manifest-json): produced by commands that read and understand your project
 - [run results](/reference/artifacts/run-results-json): produced by commands that run, compile, or catalog nodes in your DAG
 - [catalog](catalog-json): produced by `docs generate`

From 0e333190c2eea172baa6cf574549b7f5e5115f72 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:00:05 +0100
Subject: [PATCH 123/703] fix image

---
 .../dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 16330dfbe40..0b08fea0a96 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -9,7 +9,7 @@ sidebar_position: 9
 
 We are thrilled to announce the re-release of the dbt Semantic Layer, now available in [public beta](#public-beta). It revolutionizes data analysis by introducing new semantic components that lay the foundation for effective organization and interaction with data. With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
-<Lightbox src="/img/sl-architecture.jpg" width="95%" title="The dbt Semantic Layer diagram displays how data flows from sources to your platform. Define and query metrics using with dbt Cloud for consistent organization-wide metrics." />
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Why the dbt Semantic Layer?
 

From b714240aca09dff7affb3761d4da0c27d2e2407e Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:10:16 +0100
Subject: [PATCH 124/703] fix image

---
 .../dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 0b08fea0a96..f540436932c 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -9,7 +9,7 @@ sidebar_position: 9
 
 We are thrilled to announce the re-release of the dbt Semantic Layer, now available in [public beta](#public-beta). It revolutionizes data analysis by introducing new semantic components that lay the foundation for effective organization and interaction with data. With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Why the dbt Semantic Layer?
 

From 12db0981aebd2990c2edd83684574d55863eb4e7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:12:10 +0100
Subject: [PATCH 125/703] clarify

---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 3ecf62c278e..b5a1ae3265c 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -12,7 +12,7 @@ Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key comp
 Use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 :::info
-MetricFlow is currently available on dbt v1.6 beta for dbt Cloud users. MetricFlow, is a new way to define metrics in dbt and  replaces the dbt_metrics package.
+MetricFlow is currently available on dbt v1.6 or higher. MetricFlow, is a new way to define metrics in dbt and  replaces the dbt_metrics package.
 
 To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 :::

From 97f4f639fceba34d356ae500d929a66394530f2d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 17 Jul 2023 14:19:52 +0100
Subject: [PATCH 126/703] Update website/docs/docs/deploy/job-scheduler.md

---
 website/docs/docs/deploy/job-scheduler.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-scheduler.md b/website/docs/docs/deploy/job-scheduler.md
index eae556309ba..03eeb6fb377 100644
--- a/website/docs/docs/deploy/job-scheduler.md
+++ b/website/docs/docs/deploy/job-scheduler.md
@@ -31,7 +31,7 @@ Familiarize yourself with these useful terms to help you understand how the job
 | Over-scheduled job | A situation when a cron-scheduled job's run duration becomes longer than the frequency of the job’s schedule, resulting in a job queue that will grow faster than the scheduler can process the job’s runs. |
 | Prep time | The time dbt Cloud takes to create a short-lived environment to execute the job commands in the user's cloud data platform. Prep time varies most significantly at the top of the hour when the dbt Cloud Scheduler experiences a lot of run traffic. |
 | Run | A single, unique execution of a dbt job. |
-| Run slot | Run slots control the number of jobs that can run concurrently. Developer and Team plan accounts have a fixed number of run slots, and Enterprise users have [unlimited run slots](/docs/dbt-versions/release-notes/July-2023/faster-run#unlimited-job-concurrency-for-enterprise-accounts). Each running job occupies a run slot for the duration of the run. |
+| Run slot | Run slots control the number of jobs that can run concurrently. Developer and Team plan accounts have a fixed number of run slots, and Enterprise users have [unlimited run slots](/docs/dbt-versions/release-notes/July-2023/faster-run#unlimited-job-concurrency-for-enterprise-accounts). Each running job occupies a run slot for the duration of the run. If you need more jobs to execute in parallel, consider the [Enterprise plan](https://www.getdbt.com/pricing/) |
 | Threads | When dbt builds a project's DAG, it tries to parallelize the execution by using threads. The [thread](/docs/running-a-dbt-project/using-threads) count is the maximum number of paths through the DAG that dbt can work on simultaneously. The default thread count in a job is 4. |
 | Wait time | Amount of time that dbt Cloud waits before running a job, either because there are no available slots or because a previous run of the same job is still in progress. |
 

From 5a0a26438a9871b25d9ecbdd4724dac4102a3729 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:27:54 +0100
Subject: [PATCH 127/703] update rn

---
 .../release-notes/06-July-2023/sl-revamp-beta.md         | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index f540436932c..0aee834be0a 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -7,7 +7,14 @@ date: 2023-07-31
 sidebar_position: 9
 ---
 
-We are thrilled to announce the re-release of the dbt Semantic Layer, now available in [public beta](#public-beta). It revolutionizes data analysis by introducing new semantic components that lay the foundation for effective organization and interaction with data. With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
+We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It revolutionizes data analysis by introducing:
+
+- [MetricFlow](/docs/build/about-metricflow) &mdash;  a new way to define metrics in dbt and one of the key components of the dbt Semantic Layer
+- New semantic components like semantic models and metrics, which help lay the foundation for effective organization and interaction with data,
+- Improved governance, enhanced efficiency, and data accessibility
+- New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations
+
+With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 

From 04d40a2978d99ea971125e60ebe84772019556ca Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:28:13 +0100
Subject: [PATCH 128/703] nit

---
 .../dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 0aee834be0a..40bfebe9297 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -52,7 +52,7 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
 To experience the universal dbt Semantic Layer and its enhanced beta capabilities, check out:
 
-- [Revamped dbt Semantic Layer documents](/docs/use-dbt-semantic-layer/dbt-sl)
+- [New dbt Semantic Layer documents](/docs/use-dbt-semantic-layer/dbt-sl)
 - [dbt Semantic Layer get started guide](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [Build your metrics with MetricFlow](/docs/build/build-metrics-intro)
 - BLOG announcement link.

From 922d8a01762b1715fa79c0809f9aec9b16fef7fb Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:54:19 +0100
Subject: [PATCH 129/703] fix fn

---
 .../06-July-2023/sl-revamp-beta.md            | 37 ++++++++++---------
 1 file changed, 20 insertions(+), 17 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 40bfebe9297..b1c594db0a7 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -9,16 +9,31 @@ sidebar_position: 9
 
 We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It revolutionizes data analysis by introducing:
 
-- [MetricFlow](/docs/build/about-metricflow) &mdash;  a new way to define metrics in dbt and one of the key components of the dbt Semantic Layer
+- [MetricFlow](/docs/build/about-metricflow), a new way to define metrics in dbt and one of the key components of the dbt Semantic Layer,
 - New semantic components like semantic models and metrics, which help lay the foundation for effective organization and interaction with data,
-- Improved governance, enhanced efficiency, and data accessibility
-- New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations
+- Improved governance, enhanced efficiency, and data accessibility,
+- New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="75%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
-## Why the dbt Semantic Layer?
+
+## Public beta 
+
+The dbt Semantic Layer is currently available as a public beta, which means:
+
+- **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. 
+  * Developer plans or dbt Core users can use MetricFlow to define and test metrics using the CLI only. 
+
+- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced). DO WE MENTION THAT WE'RE NOT CHARGING?
+
+- **When** &mdash; Public beta will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will make pricing information available.  DO WE MENTION THAT WE'LL BEGIN CHARGING?
+
+- **Where** &mdash; Experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
+
+
+## Enhanced dbt Semantic Layer
 
 What sets the dbt Semantic Layer apart is its ability to centralize logic and data applications, streamlining processes and enabling efficient management and utilization of data models. It provides a consistent view of data, incorporating context and historical information, simplifying complex tasks, and reducing costs.
 
@@ -36,18 +51,6 @@ We are excited to present several important capabilities with the enhanced dbt S
 
 By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows. 
 
-## Public beta 
-
-THINKING OF TURNING INTO A TABLE ONCE I HAVE THE ACCURATE INFO AND MOVING ABOVE AFTER INTO (BEFORE 'WHY SL')
-
-The dbt Semantic Layer is currently available as a public beta, which means:
-
-- **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during public beta. 
-  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Semantic Layer and Discovery APIs. 
-- **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-- **When?**  Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
-- **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user
-
 ## Next steps
 
 To experience the universal dbt Semantic Layer and its enhanced beta capabilities, check out:

From f543a77d5422d9332df976921bb50bec075a578f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:55:07 +0100
Subject: [PATCH 130/703] fix positioning

---
 .../06-July-2023/sl-revamp-beta.md            | 28 +++++++++----------
 1 file changed, 13 insertions(+), 15 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index b1c594db0a7..c9b89b1ea4b 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -18,21 +18,6 @@ With semantics at its core, the dbt Semantic Layer marks a crucial milestone tow
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="75%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
-
-## Public beta 
-
-The dbt Semantic Layer is currently available as a public beta, which means:
-
-- **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. 
-  * Developer plans or dbt Core users can use MetricFlow to define and test metrics using the CLI only. 
-
-- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced). DO WE MENTION THAT WE'RE NOT CHARGING?
-
-- **When** &mdash; Public beta will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will make pricing information available.  DO WE MENTION THAT WE'LL BEGIN CHARGING?
-
-- **Where** &mdash; Experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
-
-
 ## Enhanced dbt Semantic Layer
 
 What sets the dbt Semantic Layer apart is its ability to centralize logic and data applications, streamlining processes and enabling efficient management and utilization of data models. It provides a consistent view of data, incorporating context and historical information, simplifying complex tasks, and reducing costs.
@@ -51,6 +36,19 @@ We are excited to present several important capabilities with the enhanced dbt S
 
 By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows. 
 
+## Public beta 
+
+The dbt Semantic Layer is currently available as a public beta, which means:
+
+- **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. 
+  * Developer plans or dbt Core users can use MetricFlow to define and test metrics using the CLI only. 
+
+- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced). DO WE MENTION THAT WE'RE NOT CHARGING?
+
+- **When** &mdash; Public beta will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will make pricing information available.  DO WE MENTION THAT WE'LL BEGIN CHARGING?
+
+- **Where** &mdash; Experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
+
 ## Next steps
 
 To experience the universal dbt Semantic Layer and its enhanced beta capabilities, check out:

From 82eed1bf3a64f2f9e38ed4ecc47b6b344bc9fd72 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 17 Jul 2023 14:59:30 +0100
Subject: [PATCH 131/703] nit

---
 .../dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index c9b89b1ea4b..78b24a89709 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -47,7 +47,7 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
 - **When** &mdash; Public beta will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will make pricing information available.  DO WE MENTION THAT WE'LL BEGIN CHARGING?
 
-- **Where** &mdash; Experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
+- **Where** &mdash; You can experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
 
 ## Next steps
 

From 03a34c172c0d08b74d7e7c0f1da2bc2743fca404 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Mon, 17 Jul 2023 09:24:22 -0600
Subject: [PATCH 132/703] adding subcommand to invocation_args_dict (#3756)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

## What are you changing in this pull request and why?

Resolves #3727

Adds versioning and subcommand to invocation_args_dict section

- uses the `<File>` tag for `models/my_model.sql` example
- demarcates `invocation_args_dict` vs. dbt_metadata_envs via SQL
comments in both pre-1.6 and post 1.6
- gives example command to run for pre 1.6

## 🎩

### Pre 1.6


https://deploy-preview-3756--docs-getdbt-com.netlify.app/reference/dbt-jinja-functions/flags?version=1.5

<img width="830" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/e14d2b33-e71d-454f-a9ab-61e79cd5597b">

### Post 1.6


https://deploy-preview-3756--docs-getdbt-com.netlify.app/reference/dbt-jinja-functions/flags?version=1.6

<img width="857" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/1549042f-5a72-4be6-af45-53fbad9369ab">


## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.

---------

Co-authored-by: mirnawong1 <mirna.wong@fishtownanalytics.com>
Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 .../reference/dbt-jinja-functions/flags.md    | 53 ++++++++++++++++++-
 1 file changed, 51 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/dbt-jinja-functions/flags.md b/website/docs/reference/dbt-jinja-functions/flags.md
index a6931628f3c..6fe985cc93e 100644
--- a/website/docs/reference/dbt-jinja-functions/flags.md
+++ b/website/docs/reference/dbt-jinja-functions/flags.md
@@ -29,24 +29,73 @@ Recommended use cases include:
 
 **Note:** It is _not_ recommended to use flags as an input to parse-time configurations, properties, or dependencies (`ref` + `source`). Flags are likely to change in every invocation of dbt, and their parsed values will become stale (and yield incorrect results) in subsequent invocations that have partial parsing enabled. For more details, see [the docs on parsing](/reference/parsing).
 
+
 <VersionBlock firstVersion="1.3">
 
 ### invocation_args_dict
 
 For the full set of information passed from the CLI—subcommand, flags, arguments—you can use `invocation_args_dict`. This is equivalent to the `args` dictionary in [`run_results.json`](/reference/artifacts/run-results-json).
 
+<File name='models/my_model.sql'>
+
 ```sql
--- models/my_model.sql
+-- invocation_args_dict:
 -- {{ invocation_args_dict }}
+
+-- dbt_metadata_envs:
 -- {{ dbt_metadata_envs }}
 
 select 1 as id
 ```
-Compiles to:
+
+</File>
+
+<VersionBlock firstVersion="1.3" lastVersion="1.5">
+
+```shell
+$ DBT_ENV_CUSTOM_ENV_MYVAR=myvalue dbt compile -s my_model
+```
+
+<File name='target/compiled/my_project/models/my_model.sql'>
+
 ```sql
+-- invocation_args_dict:
 -- {'write_json': True, 'use_colors': True, 'printer_width': 80, 'version_check': True, 'partial_parse': True, 'static_parser': True, 'profiles_dir': '/Users/.../.dbt', 'send_anonymous_usage_stats': False, 'event_buffer_size': 100000, 'quiet': False, 'no_print': False, 'parse_only': False, 'which': 'compile', 'rpc_method': 'compile', 'indirect_selection': 'eager'}
 
+-- dbt_metadata_envs:
+-- {'MYVAR': 'myvalue'}
+
+select 1 as id
+```
+
+</File>
+
+</VersionBlock>
+
+
+<VersionBlock firstVersion="1.6">
+
+The `invocation_command` key within `invocation_args_dict` includes the entire subcommand when it compiles:
+
+```shell
+$ DBT_ENV_CUSTOM_ENV_MYVAR=myvalue dbt compile -s my_model
+
+12:10:22  Running with dbt=1.6.0-b8
+12:10:22  Registered adapter: postgres=1.6.0-b8
+12:10:22  Found 1 seed, 1 model, 349 macros
+12:10:22
+12:10:22  Concurrency: 5 threads (target='dev')
+12:10:22
+12:10:22  Compiled node 'my_model' is:
+-- invocation_args_dict:
+-- {'log_format_file': 'debug', 'log_level': 'info', 'exclude': (), 'send_anonymous_usage_stats': True, 'which': 'compile', 'defer': False, 'output': 'text', 'log_format': 'default', 'macro_debugging': False, 'populate_cache': True, 'static_parser': True, 'vars': {}, 'warn_error_options': WarnErrorOptions(include=[], exclude=[]), 'quiet': False, 'select': ('my_model',), 'indirect_selection': 'eager', 'strict_mode': False, 'version_check': False, 'enable_legacy_logger': False, 'log_path': '/Users/jerco/dev/scratch/testy/logs', 'profiles_dir': '/Users/jerco/.dbt', 'invocation_command': 'dbt compile -s my_model', 'log_level_file': 'debug', 'project_dir': '/Users/jerco/dev/scratch/testy', 'favor_state': False, 'use_colors_file': True, 'write_json': True, 'partial_parse': True, 'printer_width': 80, 'print': True, 'cache_selected_only': False, 'use_colors': True, 'introspect': True}
+
+-- dbt_metadata_envs:
+-- {'MYVAR': 'myvalue'}
+
 select 1 as id
 ```
 
 </VersionBlock>
+
+</VersionBlock>

From ddbc2b75b4dfa06f00e5bd1ccc6c3b1f6e20650e Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Mon, 17 Jul 2023 11:43:45 -0400
Subject: [PATCH 133/703] adjust left padding

---
 website/src/components/communitySpotlightCard/styles.module.css | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/src/components/communitySpotlightCard/styles.module.css b/website/src/components/communitySpotlightCard/styles.module.css
index e28c3fe7b41..29de378ff94 100644
--- a/website/src/components/communitySpotlightCard/styles.module.css
+++ b/website/src/components/communitySpotlightCard/styles.module.css
@@ -79,6 +79,7 @@ div.spotlightMemberCard {
   }
   div.spotlightMemberCard {
     margin-bottom: 0;
+    padding-left: 0 !important;
   }
   .spotlightMemberCard .spotlightMemberImgContainer {
     flex: 0 0 346px;

From d9bcdb7fbf68f89447e7fa05b736aec186639d26 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Mon, 17 Jul 2023 09:48:29 -0600
Subject: [PATCH 134/703] Incorporate feedback on phrasing for blue/green
 continuous deployment with `dbt clone`

---
 website/docs/reference/commands/clone.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 6a304d9050a..6225a087252 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -11,7 +11,7 @@ The `dbt clone` command clones selected nodes from the [specified state](/refere
 - You may want to specify a higher number of [threads](/docs/running-a-dbt-project/using-threads) to decrease execution time since individual clone statements are independent of one another.
 
 The `clone` command is useful for:
-- blue/green deployment
+- blue/green continuous deployment
 - cloning current production state into development schema(s)
 - handling incremental models in Slim CI dbt Cloud jobs
 

From f2ab8c19ef3c8e1c19d4b4a5f591bf7ff770a09b Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Mon, 17 Jul 2023 11:52:20 -0400
Subject: [PATCH 135/703] small content edits

---
 website/docs/community/spotlight/alan-cruickshank.md | 2 +-
 website/docs/community/spotlight/jing-yu-lim.md      | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/community/spotlight/alan-cruickshank.md b/website/docs/community/spotlight/alan-cruickshank.md
index 763b0f917d0..52bfb821402 100644
--- a/website/docs/community/spotlight/alan-cruickshank.md
+++ b/website/docs/community/spotlight/alan-cruickshank.md
@@ -15,7 +15,7 @@ socialLinks:
   - name: LinkedIn
     link: https://www.linkedin.com/in/amcruickshank/
   - name: SQLFluff
-    link: sqlfluff.com
+    link: https://sqlfluff.com
 dateCreated: 2023-06-30
 hide_table_of_contents: true
 ---
diff --git a/website/docs/community/spotlight/jing-yu-lim.md b/website/docs/community/spotlight/jing-yu-lim.md
index f492cd351c0..862e2a80de9 100644
--- a/website/docs/community/spotlight/jing-yu-lim.md
+++ b/website/docs/community/spotlight/jing-yu-lim.md
@@ -26,13 +26,13 @@ The dbt community also helped me expand my professional network, where I met so
 
 ## What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
 
-Claire Carroll and Mila Page! My very first touchpoint with dbt was their articles in [The Analytics Engineering Guide](https://www.getdbt.com/analytics-engineering/). I remember relating to it so much that I was saying ""YES"" to every other line I read, and sending text snippets to my friends. 
+Claire Carroll and Mila Page! My very first touchpoint with dbt was their articles in [The Analytics Engineering Guide](https://www.getdbt.com/analytics-engineering/). I remember relating to it so much that I was saying "YES" to every other line I read, and sending text snippets to my friends. 
 
-To me, Analytics Engineering could help overcome certain challenges I face as an analyst, and make the job feels less like a ""hamster wheel."" As the concept of analytics engineering is fairly new in Singapore, I feel the need to spread the word and bring about a mindset shift among not just data teams, but anyone who needs to work with a data team.
+To me, Analytics Engineering could help overcome certain challenges I face as an analyst, and make the job feels less like a "hamster wheel." As the concept of analytics engineering is fairly new in Singapore, I feel the need to spread the word and bring about a mindset shift among not just data teams, but anyone who needs to work with a data team.
 
 ## What have you learned from community members? What do you hope others can learn from you?
 
-One of my favourite presentations from the Singapore dbt Meetup was [""How would the ideal Semantic Layer look like?""](https://docs.google.com/presentation/d/1t1ts04b7qA-BVlV3qbNZ4fI-MSZn0iL6_FhsaWhJk_0/edit?usp=sharing ) by fellow community member Thanh Dinh from Holistics. It taught me a new perspective on metrics: they could be like dbt models, where dependencies can be set up between metric models.
+One of my favourite presentations from the Singapore dbt Meetup was ["How would the ideal Semantic Layer look like?"](https://docs.google.com/presentation/d/1t1ts04b7qA-BVlV3qbNZ4fI-MSZn0iL6_FhsaWhJk_0/edit?usp=sharing ) by fellow community member Thanh Dinh from Holistics. It taught me a new perspective on metrics: they could be like dbt models, where dependencies can be set up between metric models.
 
 I definitely have so much more to learn as an individual, but I hope to share some of my tips and lessons in terms of data modelling with others.
 

From a96502e6fe5a058d99e936cd9413a46ad4353c7d Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Mon, 17 Jul 2023 09:59:15 -0600
Subject: [PATCH 136/703] Incorporate feedback for delineating source and
 target environments

---
 website/docs/reference/commands/clone.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 6225a087252..150d3b12e62 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -5,8 +5,8 @@ id: "clone"
 ---
 
 The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). This command makes use of the `clone` materialization:
-- If your data platform supports zero-copy cloning of tables, and this model exists as a table in the other environment, dbt will create it in your environment as a clone
-- Otherwise, dbt will create a simple pointer view (`select * from` the other object)
+- If your data platform supports zero-copy cloning of tables, and this model exists as a table in the source environment, dbt will create it in your target environment as a clone
+- Otherwise, dbt will create a simple pointer view (`select * from` the source object)
 - By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 
 - You may want to specify a higher number of [threads](/docs/running-a-dbt-project/using-threads) to decrease execution time since individual clone statements are independent of one another.
 

From a4fe0d6fef281fef5bbfc293fe5fe0fcb06ba57a Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Mon, 17 Jul 2023 16:31:31 -0400
Subject: [PATCH 137/703] remove important

---
 .../src/components/communitySpotlightCard/styles.module.css   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/src/components/communitySpotlightCard/styles.module.css b/website/src/components/communitySpotlightCard/styles.module.css
index 29de378ff94..253a561ebea 100644
--- a/website/src/components/communitySpotlightCard/styles.module.css
+++ b/website/src/components/communitySpotlightCard/styles.module.css
@@ -77,9 +77,9 @@ div.spotlightMemberCard {
   .spotlightMemberCard {
     padding: 3rem 0;
   }
-  div.spotlightMemberCard {
+  :global(#spotlight-members-section) div.spotlightMemberCard {
     margin-bottom: 0;
-    padding-left: 0 !important;
+    padding-left: 0;
   }
   .spotlightMemberCard .spotlightMemberImgContainer {
     flex: 0 0 346px;

From 89374796a9ab04ee8c2c7e5748857d81f863f1ae Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 17 Jul 2023 17:08:40 -0400
Subject: [PATCH 138/703] Changed tables based on feedback

---
 .../manage-access/enterprise-permissions.md   |  1 +
 .../snippets/enterprise-permissions-table.md  | 99 +++++++++++++++----
 2 files changed, 80 insertions(+), 20 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index 8f0c8c3b2aa..2bd9fe1ae1b 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -2,6 +2,7 @@
 title: "Enterprise permissions"
 id: "enterprise-permissions"
 description: "Permission sets for Enterprise plans."
+hide_table_of_contents: true #For the sake of the tables on this page
 ---
 
 :::info Enterprise Feature
diff --git a/website/snippets/enterprise-permissions-table.md b/website/snippets/enterprise-permissions-table.md
index 6c021707a89..eda75a6e396 100644
--- a/website/snippets/enterprise-permissions-table.md
+++ b/website/snippets/enterprise-permissions-table.md
@@ -1,26 +1,85 @@
 
 Key:
 
-* (C)reate &mdash; Create new feature
-* (M)odify &mdash; Change existing fields. 
-* (D)elete &mdash; Remove feature fields entirely 
-* (V)iew &mdash; Read-only access
+* (W)rite &mdash; Create new or modify existing. Includes `send`, `create`, `delete`, `allocate`, `modify`, and `read`.
+* (M)odify &mdash; Change existing fields but can not create new. Includes `delete` and `read`.
+* (R)ead &mdash; Can view but canm not create or change any fields.
 * ❌ = No access to the feature
 * ✅ = Can access feature that does not use C/M/D/V permissions
 
-_Scroll to view all fields_
-
-| Role            | Environments | Projects | Connections | Jobs  | Repositories | Groups | Group memberships | Account settings | Webhooks | Account-level artifacts | Notifications | Use IDE |
-|-----------------|:------------:|:--------:|:-----------:|:-----:|:------------:|:------:|:-----------------:|:----------------:|:--------:|:-----------------------:|:-------------:|:-------:|
-| Account admin   | C/M/D        | C/M/D    | C/M/D       | C/M/D | C/M/D        | C/M/D  | C/M/D             | C/M/D            | C/M/D    | C/M/D                   |   C/M/D       | ✅      |
-| Account viewer  |     V        |   V      |   V         |   V   |   V          |  V     |      V            |    V             |   ❌     |   V                     |      V        | ❌      |
-| Admin           |  C/M/D       |   V      | C/M/D       | C/M/D | C/M/D        |  V     | C/M/D             |     V            | C/M/D    | ❌                      |      V        | ✅      |
-| Analyst         | V            |    ❌    |  V          | ❌    | ❌           | ❌      | ❌                | ❌               | ❌       | ❌                      | ❌            | ✅      |
-| Database admin  | V            | V        | C/M/D       | V     | V            | ❌     | ❌                 | ❌              | ❌        | ❌                      | ❌            | ❌      |
-| Developer       | ❌           | ❌       | ❌          | C/M/D | ❌            | ❌     | ❌                | ❌               | C/M/D    | ❌                      | ❌            | ✅      |
-| Git admin       | V            | V        | V           | V     | C/M/D        | ❌     | ❌                | ❌               | ❌        | ❌                      | ❌            | ❌      | 
-| Job admin       | C/M/V        | ❌       | V           | C/M/D | ❌            | ❌     | ❌                | ❌              | ❌        | ❌                      | ❌            | ❌      | 
-| Job viewer      | V            | ❌       | ❌          | V     | ❌            | ❌     | ❌                | ❌               | ❌       | ❌                      | ❌            |  ❌     |
-| Project creator | C/M/D        | C/M/D    | C/M/D       | C/M/D | C/M/D        | V       | V                | V                | ❌       | ❌                      | V             | ✅      |
-| Stakeholder     | ❌           | ❌       | ❌          | ❌     | ❌           | ❌      | ❌               | ❌               | ❌        | ❌                      | ❌            | ❌      | 
-| Team admin      | V            | V        | ❌          | V     | V            | V       | ❌               | ❌               | ❌        | ❌                      | ❌            | ❌      |   
+## Account role permissions
+
+### Account permissions
+
+|                   | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
+|-------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
+| Account settings  |     W         |               |        R        |       R        |   R    |
+| Audit logs        |     R         |               |                 |       R        |        |
+| Auth provider     |     W         |               |                 |       W        |   R    |
+| Billing           |     W         |       W       |                 |                |   R    |
+| Invitations       |     W         |               |        W        |       W        |   R    |
+| IP restrictions   |     W         |               |                 |       W        |   R    |
+| Members           |     W         |               |        W        |       W        |   R    |
+| Project (create)  |     W         |               |        W        |                |        |
+| Public models     |     R         |       R       |        R        |       R        |   R    |
+| Service tokens    |     W         |               |                 |       R        |        |
+| Webhooks          |     W         |               |                 |                |        |
+
+### Project permissions
+
+|                   | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
+|-------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
+| Connections       |       W       |               |       W         |                |   R    |
+| Credentials       |       W       |               |       W         |                |   R    |
+| Custom environment variables|  W  |               |       W         |                |   R    |
+| dbt adapters      |       W       |               |       W         |                |   R    |
+| Develop (IDE)     |       W       |               |       W         |                |        |
+| Environments      |       W       |               |       W         |                |   R    |
+| Groups            |       W       |               |       R         |       W        |   R    |
+| Jobs              |       W       |               |       W         |                |   R    |
+| Licenses          |       W       |               |       W         |       W        |   R    |
+| Metadata          |       R       |               |       R         |                |   R    |
+| Permissions       |       W       |               |       W         |       W        |   R    |
+| Profile           |       W       |               |       W         |                |   R    |
+| Projects          |       W       |               |       W         |       R        |   R    |
+| Repositories      |       W       |               |       W         |                |   R    |
+| Runs              |       W       |               |       W         |                |   R    |
+| Semantic Layer Config|    W       |               |       W         |                |   R    |
+
+
+## Project role permissions
+
+### Account permissions
+
+|                          | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+|--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
+| Account settings         |   R   |         |      R         |           |     R     |           |             |          |                |             |     R      |        |
+| Auth provider            |       |         |                |           |           |           |             |          |                |             |            |        |
+| Billing                  |       |         |                |           |           |           |             |          |                |             |            |        |
+| Invitations              |   W   |    R    |      R         |    R      |     R     |     R     |      R      |          |                |      R      |     R      |        |
+| Members                  |   W   |         |      R         |    R      |     R     |           |             |          |                |      R      |     R      |        |
+| Project (create)         |       |         |                |           |           |           |             |          |                |             |            |        |
+| Public models            |   R   |    R    |      R         |    R      |     R     |     R     |      R      |     R    |        R       |      R      |     R      |    R   |
+| Service tokens           |       |         |                |           |           |           |             |          |                |             |            |        |
+| Webhooks                 |   W   |         |                |    W      |           |           |             |          |                |             |            |    W   |
+
+### Project permissions
+
+|                          | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+|--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
+| Connections              |   W   |    R    |       W        |     R     |     R     |     R     |             |          |                |     R       |     R      |        |
+| Credentials              |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
+| Custom environment variables|  W |    W    |       W        |     W     |     W     |     W     |      R      |          |                |     R       |     W      |        |
+| dbt adapters             |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
+| Develop (IDE)            |   W   |    W    |                |     W     |           |           |             |          |                |             |            |        |
+| Environments             |   W   |    R    |       R        |     R     |     R     |     W     |      R      |          |                |     R       |     R      |        |
+| Groups                   |   R   |         |       R        |     R     |     R     |           |             |          |                |     R       |     R      |        |
+| Jobs                     |   W   |    R    |       R        |     W     |     R     |     W     |      R      |          |                |     R       |     R      |        |
+| Licenses                 |   W   |    R    |       R        |     R     |     R     |     R     |      R      |          |                |             |     R      |        |
+| Metadata                 |   R   |    R    |       R        |     R     |     R     |     R     |      R      |     R    |                |     R       |     R      |        |
+| Permissions              |   W   |         |       R        |     R     |     R     |           |             |          |                |             |     W      |        |
+| Profile                  |   W   |    R    |       W        |     R     |     R     |     R     |             |          |                |     R       |     R      |        |
+| Projects                 |   W   |    W    |       W        |     W     |     W     |     R     |      R      |          |                |     R       |     W      |        |
+| Repositories             |   W   |         |       R        |     R     |     W     |           |             |          |                |     R       |     R      |        |
+| Runs                     |   W   |    R    |       R        |     W     |     R     |     W     |      R      |          |                |     R       |     R      |        |
+| Semantic Layer Config    |   W   |    R    |       W        |     R     |     R     |     R     |             |          |        W       |     R       |     R      |        |
\ No newline at end of file

From 074b39f9c25b2abe3460c90edc1f5516ca69514b Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 17 Jul 2023 17:17:03 -0400
Subject: [PATCH 139/703] Update enterprise-permissions.md

---
 .../docs/docs/cloud/manage-access/enterprise-permissions.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index de73ba02e9a..75759e0d25e 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -20,12 +20,6 @@ The following permission sets are available for assignment in dbt Cloud Enterpri
 can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
 can be associated with more than one permission sets.
 
-:::info Role permisisons
-
-The following table lists the standard roles and permissions associated with them in dbt Cloud. Some roles, such as _Stakeholder_ have more nuanced access to features. View the individual role descriptions for more information beyond the table definitions.
-
-::: 
-
 <Snippet src="enterprise-permissions-table" />
 
 ### Account Admin

From e401b3dd8311ce1a2cdbf184597325fba2d2450d Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Mon, 17 Jul 2023 14:41:41 -0700
Subject: [PATCH 140/703] Update
 website/docs/docs/cloud/manage-access/enterprise-permissions.md

---
 website/docs/docs/cloud/manage-access/enterprise-permissions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index 75759e0d25e..7f33be50728 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -20,7 +20,7 @@ The following permission sets are available for assignment in dbt Cloud Enterpri
 can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
 can be associated with more than one permission sets.
 
-<Snippet src="enterprise-permissions-table" />
+<Snippet path="enterprise-permissions-table" />
 
 ### Account Admin
 

From ba1cd1909a2dd2e4cb5d73e76930112657524d75 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 17 Jul 2023 16:12:47 -0700
Subject: [PATCH 141/703] Update sl-jdbc.md

We will actually no longer need the encoded service token, so I remove mention of encoded
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index b70bfcee923..2cbf7d957de 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -39,7 +39,7 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 | `jdbc:arrow-flight-sql://` | The protocol for the JDBC driver.  | `jdbc:arrow-flight-sql://` |
 | `semantic-layer.cloud.getdbt.com` | The [access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your account's dbt Cloud region. You must always add the `semantic-layer` prefix before the access URL.  | For dbt Cloud deployment hosted in North America, use `semantic-layer.cloud.getdbt.com`  |
 | `environmentId` | The unique identifier for the dbt production environment, you can retrieve this from the dbt Cloud URL <br /> when you navigate to **Environments** under **Deploy**. | If your URL ends with `.../environments/222222`, your `environmentId` is `222222`<br /><br />   |
-| `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” permission. Create a new service token in your **Account Settings** page. Encode the value before inserting it into the string.  | `token=SERVICE_TOKEN` |
+| `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” permission. Create a new service token in your **Account Settings** page. | `token=SERVICE_TOKEN` |
 
 *Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable at the end or beginning of the JDBC URL `&disableCertificateVerification=true`.
 

From 6857cbe90c1509b691ffd0dca59a10b78be76613 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 17 Jul 2023 16:15:48 -0700
Subject: [PATCH 142/703] Update sl-architecture.md

Adding another component here to make it extra clear
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 1ebb406bc69..9ba636e089d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -7,13 +7,14 @@ sidebar_label: "Product architecture"
 
 <VersionBlock firstVersion="1.6">
 
-At a high level, the dbt Semantic Layer has two primary components -- defining your metrics in MetricFlow and querying them with Semantic Layer APIs. These are made possible by the following architechtural pieces:
+At a high level, the dbt Semantic Layer enables you to define metrics and query them with various interfaces. This functionality is enabled with the following components:
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
 | **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
 | **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
+| **Semantic Layer APIs** |  The interfaces that allow users to submit metric queries -- for example: the MetricFlow CLI, JDBC API, and GraphQL. These APIs are also how first-class integrations to various tools are built.| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 
 
 

From 1fa9bc1eab264c022978aa34ebff5b554796c55e Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 17 Jul 2023 16:22:00 -0700
Subject: [PATCH 143/703] Update sl-architecture.md

A few more tweaks to this table. Making everything a little more crisp.
---
 .../docs/docs/use-dbt-semantic-layer/sl-architecture.md   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 9ba636e089d..4c6c4b4804f 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -7,14 +7,14 @@ sidebar_label: "Product architecture"
 
 <VersionBlock firstVersion="1.6">
 
-At a high level, the dbt Semantic Layer enables you to define metrics and query them with various interfaces. This functionality is enabled with the following components:
+At a high level, the dbt Semantic Layer enables you to define metrics and query them in various interfaces. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). This functionality is enabled with the following components:
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
-| **[MetricFlow](/docs/build/about-metricflow)** | Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the dbt Semantic Layer and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **MetricFlow Server**| A proprietary server that takes metric requests and generates and translates them to optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with the YAML specifications  | ✅ | ✅ |  ✅  | BSL package (code is source available) |
+| **MetricFlow Server**| A proprietary server that takes metric requests and generates optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **Semantic Layer APIs** |  The interfaces that allow users to submit metric queries -- for example: the MetricFlow CLI, JDBC API, and GraphQL. These APIs are also how first-class integrations to various tools are built.| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **Semantic Layer APIs** |  The interfaces that allow users to submit metric queries -- for example: the MetricFlow CLI, JDBC API, and GraphQL. These APIs are also the mechanisms in which tools can build first-class integrations to the dbt Semantic Layer| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 
 
 

From e48fc56359457dc203ac6613081dd2a7e6a65845 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 09:23:38 +0100
Subject: [PATCH 144/703] Update website/package.json

---
 website/package.json | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/package.json b/website/package.json
index 080ede9e3e6..afb7a9b1cd4 100644
--- a/website/package.json
+++ b/website/package.json
@@ -19,7 +19,6 @@
     "@stoplight/elements": "^7.7.17",
     "@svgr/webpack": "^6.0.0",
     "axios": "^0.27.2",
-    "build": "^0.1.4",
     "classnames": "^2.3.1",
     "clsx": "^1.1.1",
     "color": "^3.1.2",

From f0473fa8de5b8502c40fbd5334c8ba86a3f75810 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 09:54:25 +0100
Subject: [PATCH 145/703] Delete sl-partner-integration-guide.md

---
 .../sl-partner-integration-guide.md           | 660 ------------------
 1 file changed, 660 deletions(-)
 delete mode 100644 website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
deleted file mode 100644
index 6f7ae1df2e7..00000000000
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ /dev/null
@@ -1,660 +0,0 @@
----
-title: "dbt Semantic Layer integration" 
-id: "sl-partner-integration-guide"
-description: Learn about partner integration guidelines, roadmap, and connectivity. 
----
-
-# dbt Semantic Layer partner integration
-
-:::info Coming soon
-The dbt Semantic Layer is undergoing some sophisticated changes, enabling more complex metric definitions and efficient querying. As part of these changes, the dbt_metrics package will be deprecated and replaced with MetricFlow. For more info, check out the [The dbt Semantic Layer: what's next?](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) and [dbt_metrics deprecation](https://docs.getdbt.com/blog/deprecating-dbt-metrics) blog.
-:::
-
-This guide is for dbt Semantic Layer integration partners and explains integration guidelines, and connectivity.<br />
-
-To become a formal partner, integrate with the API, or have questions/feedback &mdash; **[contact us](mailto:semantic-layer@dbtlabs.com)** for more info.
-
-The dbt Semantic Layer allows users to dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics, models, and entities. It helps organizations manage complexities such as data, tools, and teams to make more efficient and trustworthy decisions.
-
-The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads. 
-
-To solve this, the dbt Semantic Layer provides a platform where users can confidently leverage their data from within their tools. dbt Cloud's change management capabilities ensure that any user modifications made to core business constructs, like metrics or entities, are distributed into all the tools connected to the data platform.
-
-The dbt Semantic Layer can be used for a variety of tools and applications of data. Here are some common use cases
-
-* Business intelligence (BI), reporting, and analytics,
-* Data quality and monitoring,
-* Governance and privacy,
-* Data discovery and cataloging,
-* Machine learning and data science.
-
-:::info Share your use case
-
-If you'd like to share other use cases for the dbt Semantic Layer, contact the [dbt Labs team](mailto:semantic-layer@dbtlabs.com).
-
-:::
-
-
-## Product overview
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[dbt Project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source in dbt Core |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt Core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja-like query models and metrics, use macros), compiles the query into pure SQL, executes the query in the data platform, and returns the data. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary in dbt Cloud |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, and metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary in dbt Cloud |
-    
-Review the following current architecture to understand how the components work together:
-  
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="Current product architecture" />
-
-
-## Integration guidelines
-
-In collaboration with dbt Labs, partners and users can build dbt Semantic Layer integrations that can import model metadata and metric definitions, query metrics, use macros, and more. 
-
-For more details, refer to the [Integration roadmap](#integration) and [Integration best practices](#best-practices) guidance.
-
-**Integration roadmap <a id="integration"></a>** 
-
-Integration partners generally build and approach their roadmap in the following stages:
-
-| Feature  |  Info | Availability |
-|----------|-------|:------------:|
-| **Model metadata**  |  Import/sync model metadata (descriptions, dimensions, test, freshness, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
-| **Metric definitions**  | Import/sync metric definitions (metric calculation, dimensions, description, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
-| **dbt Semantic Layer as a data source**  | Connect to the dbt Semantic Layer as a data source (for example, the Snowflake Proxy Server). Users can execute dbt-SQL to query metrics or models and use macros.* | ✅ |
-| **Query metrics**  | Query the imported metrics via a metric-centric UI (for example, a user can select a metric, time grain, and dimensions of interest). | ✅ |
-| **Entity definitions**   | Import/sync entity definitions (descriptions, dimensions, data types, relationships, metrics, and more) and query entities via the dbt Semantic Layer. | _*Coming soon |
-| **dbt Semantic Layer Connector**   | A dedicated connector with the ability to query any data platform supported in dbt Cloud. (Will replace (3).) | _*Coming soon |
-
-_*The coming soon features are expected to launch in 2023. 
-
-**Integration best practices <a id="best-practices"></a>**
-
-To build a successful and seamless dbt Semantic Layer integration, it should express the following:
-
-- **Consistent**: Have a consistent user experience (UX) incorporated into existing core user workflows.
-- **Trustworthy**: Treat dbt assets (metrics, models, and entities) as first-class objects and indicate that their definitions and resulting datasets come from dbt Cloud.
-- **Efficient**: Provide a clear advantage over the current approach to setting up metrics and analyses, and finding dimensions/datasets in the tool.
-- **Accessible**: Include a self-serve component so a data consumer can ask questions via the user interface (UI), if applicable.
-
-
-## Use the Discovery API
-
-This section will explain how to connect to and query the [Discovery API](/docs/dbt-cloud-apis/discovery-api) for model and metric definitions. 
-
-To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-semantic-layer/dbt-sl#prerequisites). 
-
-<details>
-  <summary><b>Discovery API authorization</b></summary>
-  <div>
-    <div>Refer to our <a href="/docs/dbt-cloud-apis/discovery-querying#authorization">Authorization documentation</a> to learn how to authorize requests to the Discovery API.<br></br><br></br>
-    
-    Metrics-specific queries work identical to existing Discovery API queries. This means existing integrations that query model metadata will work perfectly in the context of metrics.
-    </div>
-    </div>
-    </details>
-
-  <details>
-  <summary><b>Query the Discovery API</b></summary>
-  <div>
-    <div>Test out the Discovery API by using the <a href="https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql">GraphQL sandbox</a> and use this <a href="https://github.com/transform-data/dbt-metadata-client">Python client</a> as a starting point to develop.
-    </div>
-    </div>
-    </details>
-    <br></br>
-
-<!-- the following content was used for the <details> portion above. leaving this here for legacy purposes 
-**Metadata API authorization**
-
-To learn how to authorize requests to the Metadata API, review the [documentation](/docs/dbt-cloud-apis/discovery-querying#authorization) for more details. Metrics-specific queries work identically to existing Metadata API queries, so existing integrations used to query model metadata will work perfectly in the context of metrics. 
-
-**Query the Metadata API**
-
-Test out the Metadata API by using the [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) and use this [Python client](https://github.com/transform-data/dbt-metadata-client) as a starting point to develop. 
-
--->
-
-### Query models for a project
-
-You can query model definitions or details about a specific model for a project from a given job.
-
- 
-<!--- tabs for listing models and fetching details about specific model --->
-<Tabs>
-
-<TabItem value="list" label="Listing models for a job">
-
-This is an example of querying all models that utilize the schema`analytics` from a given job.
-
-```
-{  
- models(jobId: 181329, schema: "analytics") {
-   name
-   status
-   compileCompletedAt
-   database
-   dbtVersion
-   runGeneratedAt
-   }
-}
-```
-</TabItem>
-
-<TabItem value="exampleresponse" label="Example response with models">
-
-```
-{
-   "data": {
-       "models": [
-           {
-               "name": "customers",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:24.186Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           },
-           {
-               "name": "stg_customers",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           },
-           {
-               "name": "stg_orders",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           }
-       ]
-   }
-}
-```
-</TabItem>
-
-<TabItem value="details" label="Query details about a specific model">
-
-This is an example of querying details about a specific model, `model.jaffle_shop.customers`, from a given job.
-
-```
-{
- model(jobId: 181329, uniqueId: "model.jaffle_shop.customers") {
-   parentsModels {
-     runId
-     uniqueId
-     executionTime
-   }
- }
-}
-{
-   "data": {
-       "model": {
-           "parentsModels": [
-               {
-                   "runId": 105297555,
-                   "uniqueId": "model.jaffle_shop.stg_customers",
-                   "executionTime": 1.676571846008301
-               },
-               {
-                   "runId": 105297555,
-                   "uniqueId": "model.jaffle_shop.stg_orders",
-                   "executionTime": 1.631831407546997
-               }
-           ]
-       }
-   }
-}
-```
-
-</TabItem>
-</Tabs>
-
-<!--- End tabs for listing models and fetching details about specific model --->
-
-### Query metrics for a project
-
-Query metrics definitions or details for a project from a given job and refer to the following resources: 
-
-- [Metrics query](/docs/dbt-cloud-apis/discovery-schema-metrics) &mdash; Information on how to query the full list of metrics defined in a user’s project with the dbt Cloud Discovery API. 
-- [dbt Metrics docs](https://docs.getdbt.com/docs/build/metrics#available-properties) &mdash; Information on the available metric properties. 
-- [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) &mdash; Access to test the dbt Cloud Discovery API testing environment.
-
-<!--- tabs for listing, fetching, example, and querying metrics --->
-<Tabs>
-
-<TabItem value="list" label="Listing metrics">
-
-This is an example listing metrics from a given job:
-
-```
-{
-  metrics(jobId: 123) {
-    name
-    label
-    description
-    model
-    dependsOn
-    calculation_method
-    expression
-    timestamp
-    timeGrains
-    dimensions
-    window
-    filters
-    tags
-    meta
-  }
-}
-```
-</TabItem>
-
-<TabItem value="query" label="Query details about a specific metric">
-
-The `metric` query supports all metric properties listed in **Listing metrics**. 
-The following abbreviated example is querying details about the metric `new_customers` from job `123`:
-
-This is an example of querying details about a specific metric `new_customers` from a given job `123`.
-
-```
-{
-  metric(jobId: 123) {
-    label
-    calculation_method
-    timestamp
-    timeGrains
-    dimensions
-  }
-}
-```
-
-</TabItem>
-
-<TabItem value="exampleapi" label="Example Discovery API response with metrics">
-
-```
-{
-  "data": {
-    "metrics": [
-      {
-        "uniqueId": "metric.claim_to_fame.total_claim_charges",
-        "name": "total_claim_charges",
-        "tags": [],
-        "label": "Total Claim Charges",
-        "calculation_method": "sum",
-        "expression": "total_charge_amount",
-        "timestamp": "created_at",
-        "timeGrains":[
-          "day",
-          "week",
-          "month"
-        ],
-        "meta": {},  
-        "resourceType": "metric",
-        "model": {
-          "name": "fct_billed_patient_claims"
-        }
-       },
-      {
-        "uniqueId": "metric.claim_to_fame.total_billed_diagnoses",
-        "name": "total_billed_diagnoses",
-        "tags": [],
-        "label": "Total Billed Diagnoses",
-        "calculation_method": "count_distinct",
-        "expression": "diagnosis_id",
-        "timestamp": "created_at",
-        "timeGrains":[
-          "week",
-          "month",
-          "year"
-        ],
-        "meta": {},  
-        "resourceType": "metric",
-        "model": {
-          "name": "fct_billed_patient_claims"
-        },
-      }
-     ]
-  }
-}
-```
-
-</TabItem>
-
-<TabItem value="example" label="Example metric definition">
-
-```
-metrics:
-  - name: total_claim_charges
-    label: Total Claim Charges
-    model: ref('fct_billed_patient_claims')
-    calculation_method: sum
-    expression: total_charge_amount
-    timestamp: created_at
-    time_grains: [day, week, month, all_time]
-
-
-  - name: total_billed_diagnoses
-    label: Total Billed Diagnoses
-    model: ref('fct_billed_patient_claims')
-    calculation_method: count_distinct
-    expression: diagnosis_id
-    timestamp: created_at
-    time_grains: [day, week, month]
-```
-
-</TabItem>
-
-</Tabs>
-
-<!--- End tabs for listing, fetching, example, and querying metrics --->
-
-
-## Query the dbt Semantic Layer
-
-This section explains how to connect to or query the dbt Semantic Layer Proxy Server to return model data, metric data, and so on. 
-
-When you configure the dbt Semantic Layer, dbt Cloud provides a Proxy Server endpoint that users can connect to as though it's a Snowflake-hosted endpoint. Once the queries are submitted, dbt Cloud will:
-
-1. Compile dbt-sql queries into valid Snowflake SQL,
-2. Execute the compiled SQL against the Snowflake data platform,
-3. Return the results to the client.
-
-Replace the hostname in your existing data platform connection with the relevant dbt Cloud Proxy Server URL (for example, `abc123.proxy.cloud.getdbt.com`). All queries you submit through the endpoint will be compiled en route to the data platform.* 
-
-*_Note: This approach will change with the new Semantic Layer connection in mid-2023, which will be able to query all data platforms supported in dbt Cloud through dedicated JDBC/ODBC drivers, and eventually an API._
-
-
-<!--- tabs for running models and running metrics --->
-<Tabs>
-
-<TabItem value="metrics" label="Metric queries">
-
-Users can compile and execute metric queries using macros defined in the [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics).  This package:
-
-- Generates the SQL required to accurately calculate the metric definition,
-- Supplies helper macros for derived calculations (like month over month, year to date, and so on) time series operations
-
-
-```
-select * 
-from {{ metrics.calculate(
-    metric_list=[metric('customers'), metric(‘revenue’)],
-    grain='week',
-    dimensions=['plan', 'country'],
-    secondary_calculations=[
-        metrics.period_to_date(aggregate="sum", period="year"),
-        metrics.rolling(aggregate="average", interval=4, alias="avg_past_4wks")
-    ],
-    start_date='2020-01-01',
-    end_date="date_trunc('day', getdate())"
-) }}
-```
-
-</TabItem>
-
-<TabItem value="models" label="Model queries">
-
-Model queries allow users to query models and use macros from their dbt project.
-
-```
-select cents_to_dollars('amount_cents') as amount_dollars 
-from {{ ref('orders') }}
-```
-</TabItem>
-</Tabs>
-
-### Entities 
-<!-- rewrite a little and include a summary for each tab entry -->
-
-dbt Labs will introduce a new node type, **[entity](https://github.com/dbt-labs/dbt-core/issues/6379)**, when dbt Core version 1.5 launches. It introduces a new and efficient way to define metrics by reusing logic (for example, `time_grains`).  
-
-Entities are semantic objects made up of curated dimensions from models with more metadata defined. Over time, users can standardize metric and entity definitions with packages to speed up development. 
-
-For integrations, entities will provide information like:
-
-- a way to organize metrics based on the entity they reference, and 
-- a new consumable and dynamically generated dataset (versus finding a table in the data platform). 
-
-This information will be available alongside the Discovery API, and entities can be directly queried through the dbt Semantic Layer. 
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/entity-lineage.jpg" title="Entity lineage graph" />
-
-:::caution 🚧
-
-Entities are a work in progress &mdash; expect continuous changes and improvements. To stay up-to-date, refer to the [entity discussions](https://github.com/dbt-labs/dbt-core/issues/6379) page. 
-
-:::
-
-<!--- tabs for running entity def, entity metadata response, metric def, metric metadata response, entity queries --->
-
-<Tabs>
-
-<TabItem value="entitydef" label="Entity definition">
-
-Define entities in your dbt project.
-
-```
-entities: ## The top-level path of the new node
-  - name: [Required] ## The name of the entity
-    model: [Required] ## The name of the model that the entity is dependent on
-    description: [Optional] ## The description of the entity
-    
-    dimensions: [Optional] ## The list of dimensions & properties associated with the entity. 
-      - include: [Optional] *
-      - exclude: [Optional]
-      - name: [Required] ## The name of the dimension 
-		column_name: [Optional] ## The name of the column in the model if not 1:1. Serves as mapping
-		data_type: [Optional] ## The data type of the dimension
-		description: [Optional] ## Description of the dimension
-		default_timestamp: [Optional] ## Setting datetime dimension as default for metrics
-		time_grains: [Optional] ## Acceptable time grains for the datetime dimension
-		primary_key: [Optional] ## Whether this dimension is part of the primary key
-```
-</TabItem>
-
-<TabItem value="entityapi" label="Entity Discovery API response">
-
-Query entities via the Discovery API.
-
-```
-"entity.project_name.entity_name": {
-      "unique_id": "entity.project_name.entity_name",
-      "package_name": "project_name",
-      "original_file_path": "models/metric_definitions/ratio_metric.yml",
-      "name": "entity_name",
-      "model": "ref('model_name')",
-      "description": "some description",
-      "dimensions": {
-				"dimension_name": {
-            "name": "dimension_name",
-						"column_name": "column_name",
-						"default_timestamp": "true",
-						"time_grains": "[day, week, month, year]"
-            "primary_key": true,
-            "data_type": null,
-            "description": "TBD",
-            "meta": {},
-						}
-      },
-      "resource_type": "entity",
-      "meta": {},
-      "tags": [],
-      "config": {
-          "enabled": true,
-      },
-      "depends_on": {
-          "macros": [],
-          "nodes": [
-              "model.project_name.model_name",
-          ]
-      },
-			"docs": {
-        "show": true,
-        "node_color": null
-      },
-      "refs": [
-					[
-						"model_name",
-					]
-			],
-      "created_at": 1669653016.522599
-  },
-  ```
-</TabItem>
-
-<TabItem value="newmetricdef" label="New metric definition">
-
-How to define new [metrics](/docs/build/metrics) in your dbt project. The metric definition and metadata response will change accordingly once entities are introduced, notably with metrics referencing entities instead of models and inheriting entity dimensions. 
-
-    ```
-    metrics: 
-		## Always required
-	     - name: [Required] ## The name of the metric
-		label: [Required] ## The human-readable name of the metric
-		calculation_method: [Required] ## The calculation/aggregation used for the metric
-		expression: [Required] ## The SQL expression being aggregated/calculated
-		entity: [Required] ## The entity being used as the source of the metric
-
-		## Always optional
-		description: [Optional] ## Any description about the metric
-		timestamp: [Optional] ## The name of the timestamp field to use
-		time_grains: [Optional] ## The list of time grains that are permitted
-		filters: [Optional] ## The filters of the metric
-		window: [Optional] ## The ability to make a metric cumulative over a time period
-		config: [Optional] ## Additional information for configuring the output
-		
-		## Either or dimensions:
-			include: [Optional] ## The list of dimensions to be included. Either * or list
-			exclude: [Optional] ## The list of dimensions to be excluded from the inherited list
-    ```
-    
-</TabItem>      
-
-<TabItem value="newmetricapi" label="New metric Discovery API response">
-
-```
-"metric.project_name.metric_name": {
-      "fqn": [
-          "project_name",
-          "folder_name",
-          "metric_name"
-      ],
-      "unique_id": "metric.project_name.metric_name",
-      "package_name": "project_name",
-      "root_path": "file_path",
-      "path": "file_path",
-      "original_file_path": "file_path",
-      "name": "metric_name",
-      "description": "description",
-			"entity": "entity_name",
-      "label": "Human readable version",
-      "calculation_method": "the calc method",
-      "timestamp": "the timestamp field",
-      "time_grains": [
-          "day",
-          "week"
-      ],
-      "expression": "a field name or sql expression",
-      "dimensions": [
-				{
-				"entity_name": [
-          "had_discount",
-          "order_country"
-					]
-				}
-      ],
-      "window": null,
-      "resource_type": "metric",
-      "filters": [],
-      "meta": {},
-      "tags": [],
-      "config": {
-          "enabled": true
-      },
-      "unrendered_config": {},
-      "sources": [],
-      "depends_on": {
-          "macros": [],
-          "nodes": [
-              "entity.projet_name.entity_name",
-          ]
-      },
-      "entities": [
-				[
-					"entity_name"
-				]
-			],
-      "metrics": ["used for derived metrics"],
-      "created_at": 1669653027.290001
-  },
-  ```
-</TabItem>
-
-<TabItem value="entityquery" label="Entity queries">
-
-Query an entity using dbt-SQL. Eventually, users will be able to query entities and dynamically generate datasets using a macro (like with metrics), without having to find specific tables or columns. 
-
-```
-select *
-from {{ entities.calculate(
-	entity_list=[...], [Required, one to start]
-       dimensions: [...], [Optional, default is all]
-       metrics: [...], [Optional, default is all at finest grain]
-       filters: ...
-	)}}
-  ```
-</TabItem>
-</Tabs>
-
-### dbt Semantic Layer Connector 
-
-In order to support more data platforms and enhance the user experience, users will be able to connect to a [dbt Cloud-supported data platform](/docs/cloud/connect-data-platform/about-connections) with the dbt Semantic Layer.
-
-Integration partners need to install the [Arrow FlightSQL](https://arrow.apache.org/docs/format/FlightSql.html) JDBC/ODBC driver, which will authenticate with dbt Cloud and the data platform that it queries. 
-
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/connection-architecture.jpg" title="Envisioned connection architecture" />
-
-
-### dbt Semantic Layer API 
-
-dbt Cloud will provide a web API that supports:
-
-- Compiling dbt-SQL queries to return their compiled SQL.
-- Executing dbt-SQL queries and returning the queried results from the data platform.
-
-The API will be a viable integration point with the dbt Semantic Layer. It will be authorized by a [dbt Cloud service token](/docs/dbt-cloud-apis/service-tokens) and eventually support the invocation of dbt commands (e.g., `dbt run`, `dbt test`, etc.) in the future.
-
-
-## Contact us
-
-### For dbt Semantic Layer support
-
-For partner and customer support, please email the [Support team](mailto:support@getdbt.com). Please ensure the message includes:
-
-- "Semantic Layer"
-- The name of the partner software
-- The dbt Cloud account ID of the customer, if you are a partner making the inquiry
-
-### For product and partnerships
-
-If you'd like to become a formal partner, have product feedback/questions, or are interested in integrating, email the [Product and Partnership team](mailto:semantic-layer@dbtlabs.com). 
-
-<!-- rewrite a little and would like to see if i can include a button -->
-
-## Related docs
-
-- [dbt Semantic Layer docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
-- [dbt Metrics docs](https://docs.getdbt.com/docs/building-a-dbt-project/metrics) for more information about its components.
-- [dbt Semantic Layer intro blog](https://www.getdbt.com/blog/dbt-semantic-layer/) and [launch blog](https://www.getdbt.com/blog/frontiers-of-the-dbt-semantic-layer/) to learn more about the product vision and purpose. 
-- [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.
-
-

From 08be4eb8ce0221604e1ab04919478ef82f9b3ce5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 18 Jul 2023 11:37:08 +0100
Subject: [PATCH 146/703] callout updates

---
 .../docs/docs/build/build-metrics-intro.md    |  5 +--
 website/docs/docs/build/metrics.md            | 36 ++++++++++++++++---
 .../docs/dbt-cloud-apis/service-tokens.md     |  8 +++--
 .../docs/dbt-cloud-apis/sl-api-overview.md    |  6 ++--
 .../docs/docs/dbt-cloud-apis/sl-graphql.md    |  6 ++--
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md   |  6 ++--
 .../docs/docs/dbt-cloud-apis/sl-manifest.md   |  5 +--
 .../avail-sl-integrations.md                  |  3 +-
 .../docs/use-dbt-semantic-layer/dbt-sl.md     | 15 +++-----
 .../use-dbt-semantic-layer/quickstart-sl.md   | 15 +++-----
 .../docs/use-dbt-semantic-layer/setup-sl.md   |  1 +
 .../use-dbt-semantic-layer/sl-architecture.md | 14 ++++----
 website/docs/guides/migration/sl-migration.md |  2 +-
 website/snippets/_legacy-sl-callout.md        | 10 +++---
 website/snippets/_new-sl-changes.md           |  4 +--
 website/snippets/_upgrade-new-sl.md           |  8 -----
 website/snippets/sl-prerequisites.md          |  6 ++--
 website/static/_redirects                     |  2 +-
 18 files changed, 84 insertions(+), 68 deletions(-)
 delete mode 100644 website/snippets/_upgrade-new-sl.md

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index b5a1ae3265c..99dcd8c52c8 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -19,8 +19,9 @@ To fully experience the dbt Semantic Layer, including the ability to query dbt m
 
 Before you start, keep the following considerations in mind:
 - Define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
-- You must be on dbt v1.6 beta or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
-- MetricFlow currently only supports Snowflake, Postgres, BigQuery, Databricks, and Redshift
+- You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
+- MetricFlow currently supports Snowflake, Postgres, BigQuery, Databricks, and Redshift.
+- Unlock insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/build/metrics.md b/website/docs/docs/build/metrics.md
index d605ef590b3..dad8c7d3918 100644
--- a/website/docs/docs/build/metrics.md
+++ b/website/docs/docs/build/metrics.md
@@ -4,11 +4,34 @@ id: "metrics"
 description: "When you define metrics in dbt projects, you encode crucial business logic in tested, version-controlled code. The dbt metrics layer helps you standardize metrics within your organization."
 keywords:
   - dbt metrics layer
+tags: [Metrics]
 ---
 
-import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+:::caution Upgrade to access MetricFlow and the new dbt Semantic Layer
 
-<LegacyInfo />
+The dbt_metrics package has been deprecated and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6). If you're using the dbt_metrics package or the legacy Semantic Layer (available on v1.5 or lower), we **highly** recommend [upgrading your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to access MetricFlow and the new [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl?version=1.6).
+
+To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+
+:::
+ 
+<VersionBlock firstVersion="1.6">
+
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing new APIs, and making it more efficient to define/query metrics. This revamp means the dbt_metrics package and the legacy Semantic Layer, available in dbt v1.5 or lower, are no longer supported and won't receive any code fixes.
+
+**What’s changed?** <br /> <br />
+The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new framework for defining metrics in dbt. This means dbt_metrics is no longer supported after dbt v1.5 and won't receive any code fixes. We will also remove the dbt_metrics spec and docs when it's fully deprecated. 
+
+**Who does this affect?** <br /> <br />
+Anyone who uses the dbt_metrics package or is integrated with the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. All users can define metrics using MetricFlow. Users on dbt Cloud Developer plans or dbt Core can only use it to define and test metrics locally, but can't dynamically query them with integrated tools.
+
+**What should you do?** <br /> <br />
+If you've defined metrics using dbt_metrics or integrated with the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use MetricFlow or the new dbt Semantic Layer. To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+
+
+</VersionBlock>
+ 
+<VersionBlock lastVersion="1.5">
 
 <Changelog>
 
@@ -16,8 +39,6 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 * **v1.0.0**: Metrics are new and experimental
 
 </Changelog>
- 
-<VersionBlock lastVersion="1.5">
 
 A metric is an aggregation over a <Term id="table" /> that supports zero or more dimensions. Some examples of metrics include:
 - active users
@@ -46,7 +67,7 @@ You can define metrics in `.yml` files nested under a `metrics:` key. Metric nam
 - begin with a letter
 - contain no more than 250 characters
 
-For a short human-friendly name with title casing, spaces, and special characters, use the `label` property. More examples and guidance for how to [define and structure metrics can be found here.](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics).
+For a short human-friendly name with title casing, spaces, and special characters, use the `label` property. 
 
 ### Example definition
 
@@ -418,6 +439,11 @@ The following is the list of currently accepted metric configs:
 </VersionBlock>
 
 ## Querying Your Metric
+
+:::caution dbt_metrics is no longer supported
+The dbt_metrics package has been deprecated and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt. This means dbt_metrics is no longer supported after dbt v1.5 and won't receive any code fixes.
+:::
+
 You can dynamically query metrics directly in dbt and verify them before running a job in the deployment environment.  To query your defined metric, you must have the [dbt_metrics package](https://github.com/dbt-labs/dbt_metrics) installed. Information on how to [install packages can be found here](https://docs.getdbt.com/docs/build/packages#how-do-i-add-a-package-to-my-project).
 
 Use the following [metrics package](https://hub.getdbt.com/dbt-labs/metrics/latest/) installation code in your packages.yml file and run `dbt deps` to install the metrics package:
diff --git a/website/docs/docs/dbt-cloud-apis/service-tokens.md b/website/docs/docs/dbt-cloud-apis/service-tokens.md
index 139eff8fd07..3848244e0bb 100644
--- a/website/docs/docs/dbt-cloud-apis/service-tokens.md
+++ b/website/docs/docs/dbt-cloud-apis/service-tokens.md
@@ -4,8 +4,6 @@ id: "service-tokens"
 description: "Service account tokens help you define permissions for securing access to your dbt Cloud account and its projects."
 ---
 
-## About service tokens
-
 Service account tokens enable you to securely authenticate with the dbt Cloud API by assigning each token a narrow set of permissions that more precisely manages access to the API. While similar to [User API tokens](user-tokens), service account tokens belong to an account rather than a user.
 
 You can use service account tokens for system-level integrations that do not run on behalf of any one user. Assign any permission sets available in dbt Cloud to your service account token, which can vary slightly depending on your plan:
@@ -38,6 +36,9 @@ Account Admin service tokens have full `read + write` access to an account, so p
 **Metadata Only**<br/>
 Metadata-only service tokens authorize requests to the Discovery API.
 
+**Semantic Layer Only**<br/>
+Semantic Layer-only service tokens authorize requests to the Semantic Layer APIs.
+
 **Job Admin**<br/>
 Job admin service tokens can authorize requests for viewing, editing, and creating environments, triggering runs, and viewing historical runs.  
 
@@ -63,6 +64,9 @@ Billing Admin service tokens have certain account-level permissions.  For more o
 **Metadata Only**<br/>
 Metadata-only service tokens authorize requests to the Discovery API.
 
+**Semantic Layer Only**<br/>
+Semantic Layer-only service tokens authorize requests to the Semantic Layer APIs.
+
 **Job Admin**<br/>
 Job Admin service tokens can authorize requests for viewing, editing, and creating environments, triggering runs, and viewing historical runs. For more on these permissions, see [Job Admin](/docs/cloud/manage-access/enterprise-permissions#job-admin).
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 4f46f37173f..0490615e491 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -2,15 +2,15 @@
 title: "Semantic Layer APIs"
 id: sl-api-overview
 description: "Integrate and query using the Semantic Layer API."
-tags: ["semantic-layer, apis"]
+tags: [Semantic Layer, APIs]
 hide_table_of_contents: true
 ---
 
 <VersionBlock lastVersion="1.5">
 
-import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
-<UpgradeSL />
+<LegacyInfo />
 
 </VersionBlock>
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 24783f89065..286ed79621c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -2,14 +2,14 @@
 title: "GraphQL"
 id: sl-graphql
 description: "Integrate and use the GraphQL API to query your metrics."
-tags: ["semantic-layer, apis"]
+tags: [Semantic Layer, APIs]
 ---
 
 <VersionBlock lastVersion="1.5">
 
-import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
-<UpgradeSL />
+<LegacyInfo />
 
 </VersionBlock>
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 2cbf7d957de..d8595a24572 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -2,15 +2,15 @@
 title: "JDBC"
 id: sl-jdbc
 description: "Integrate and use the JDBC API to query your metrics."
-tags: ["semantic-layer, apis"]
+tags: [Semantic Layer, APIs]
 ---
 
 
 <VersionBlock lastVersion="1.5">
 
-import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
-<UpgradeSL />
+<LegacyInfo />
 
 </VersionBlock>
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index a36e17d48fe..031e634668b 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -2,14 +2,15 @@
 title: "Semantic manifest"
 id: sl-manifest
 description: "Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."
+tags: [Semantic Layer, APIs]
 sidebar_label: "Semantic manifest"
 ---
 
 <VersionBlock lastVersion="1.5">
 
-import UpgradeSL from '/snippets/_upgrade-new-sl.md';
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
-<UpgradeSL />
+<LegacyInfo />
 
 </VersionBlock>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 86bf89c9cbe..08fc1cacd6f 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -2,6 +2,7 @@
 title: "Available integrations"
 id: avail-sl-integrations
 description: "Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem."
+tags: [Semantic Layer]
 sidebar_label: "Available integrations"
 ---
 
@@ -45,7 +46,7 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="85%" title="The universal dbt Semantic Layer connecting to integration tools."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" width="75%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Related docs
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index f294b93acc9..6a2d3ea392d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -3,6 +3,7 @@ title: "dbt Semantic Layer"
 id: dbt-sl
 description: "Introducing the improved dbt Semantic Layer, which allows data teams to centrally define and query metrics."
 sidebar_label: "About the dbt Semantic Layer"
+tags: [Semantic Layer]
 hide_table_of_contents: true
 ---
 
@@ -21,9 +22,7 @@ To read more about why you need a universal Semantic Layer, read this [blog post
 
 ## Explore the dbt Semantic Layer
 
-The universal dbt Semantic Layer is available to all Team and Enterprise Plans during [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta). Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
-
-Start exploring the dbt Semantic Layer by visiting the following pages:
+During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or newer. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
 
 <div className="grid--3-col">
 
@@ -52,7 +51,7 @@ Start exploring the dbt Semantic Layer by visiting the following pages:
     icon="dbt-bit"/>
 
 <Card
-    title="Semantic Layer API"
+    title="Semantic Layer APIs"
     body="Use the Semantic Layer APIs to query metrics in downstream tools for consistent, reliable data metrics."
     link="/docs/dbt-cloud-apis/sl-api-overview"
     icon="dbt-bit"/>
@@ -117,16 +116,10 @@ A great example of this is **revenue** &mdash; it can be aggregated on multiple
 
 ### Design and define metrics
 
-**Design metrics**
-To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.
-
-**Define metrics**
-You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
+You can design and define your metrics in a `.yml` files nested under a metrics key in your dbt project, review the following documents: <br />
 
 - [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics, and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
 - [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
-- [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
-    
 
 ## Related questions
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 24922ce668d..d8a6608e84d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -3,6 +3,7 @@ title: "Get started with the dbt Semantic Layer"
 id: quickstart-sl
 description: "Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
 sidebar_label: "Get started with the dbt Semantic Layer"
+tags: [Semantic Layer]
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -189,12 +190,14 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 To try out the features of the dbt Semantic Layer, you first need to have a dbt project set up. This quickstart guide will lay out the following steps, and recommends a workflow that demonstrates some of its essential features:
 
-- Install dbt metrics package
+- Install dbt metrics package 
+  * Note: this package will be deprecated very soon and we highly recommend you to use the new [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl?version=1.6), available in dbt v 1.6 or higher. 
 - Define metrics
 - Query, and run metrics
 - Configure the dbt Semantic Layer
 
 ## Prerequisites
+
 To use the dbt Semantic Layer, you’ll need to meet the following:
 
 <Snippet path="sl-prerequisites" />
@@ -209,6 +212,7 @@ New to dbt or metrics? Check out our [quickstart guide](/quickstarts) to build
 :::
 
 ## Installing dbt metrics package
+
 The dbt Semantic Layer supports the calculation of metrics by using the [dbt metrics package](https://hub.getdbt.com/dbt-labs/metrics/latest/). You can install the dbt metrics package in your dbt project by copying the below code blocks.
 
 <VersionBlock firstVersion="1.3" lastVersion="1.3">
@@ -255,11 +259,6 @@ Review our helpful metrics video below, which explains what metrics are, why the
     
 <LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
 
-### Design metrics
-    
-To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.
-
-### Define metrics
 Now that you've organized your metrics folder and files, you can define your metrics in `.yml` files nested under a `metrics` key.  
 
 1. Add the metric definitions found in the [Jaffle Shop](https://github.com/dbt-labs/jaffle_shop_metrics) example to your dbt project. For example, to add an expenses metric, reference the following metrics you can define directly in your metrics folder: 
@@ -331,8 +330,6 @@ metrics:
 3. If you'd like to further design and define your own metrics, review the following documentation:
 
     - [dbt metrics](/docs/build/metrics) will provide you in-depth detail on attributes, properties, filters, and how to define and query metrics.
-   
-    - Review [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project.
 
 ## Develop and query metrics
 
@@ -449,9 +446,7 @@ The reason you're experiencing this error is because we changed the <code>type</
 
 Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer: 
 
-- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) to understand best practices for designing and structuring metrics in your dbt project
 - [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, properties, filters, and how to define and query metrics
-- [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
 - [dbt Server repo](https://github.com/dbt-labs/dbt-server), which is a persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. 
 
 </VersionBlock>
diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 7775ff86d97..6c2212081e5 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -3,6 +3,7 @@ title: "Set up the dbt Semantic Layer"
 id: setup-sl
 description: "Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive nagivation."
 sidebar_label: "Set up your Semantic Layer"
+tags: [Semantic Layer]
 ---
 
 <VersionBlock firstVersion="1.6">
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 4c6c4b4804f..8e76c8daca0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -3,18 +3,20 @@ title: "dbt Semantic Layer product architecture"
 id: sl-architecture
 description: "dbt Semantic Layer product architecture and related questions."
 sidebar_label: "Product architecture"
+tags: [Semantic Layer]
 ---
 
 <VersionBlock firstVersion="1.6">
 
-At a high level, the dbt Semantic Layer enables you to define metrics and query them in various interfaces. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). This functionality is enabled with the following components:
+At a high level, the dbt Semantic Layer allows you to define metrics and query them using various interfaces. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). This is possible with the following components:
+
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
-| **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with the YAML specifications  | ✅ | ✅ |  ✅  | BSL package (code is source available) |
-| **MetricFlow Server**| A proprietary server that takes metric requests and generates optimized SQL for the specific data platform| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with the YAML specifications. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
+| **MetricFlow Server**| A proprietary server that takes metric requests and generates optimized SQL for the specific data platform. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **Semantic Layer APIs** |  The interfaces that allow users to submit metric queries -- for example: the MetricFlow CLI, JDBC API, and GraphQL. These APIs are also the mechanisms in which tools can build first-class integrations to the dbt Semantic Layer| ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
+| **Semantic Layer APIs** | The interfaces that allow users to submit metric queries include the MetricFlow CLI, JDBC API, and GraphQL. These APIs also serve as the foundation for building first-class integrations with various tools. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 
 
 
@@ -52,10 +54,10 @@ The dbt Semantic Layer product architecture includes four primary components:
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
-| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
+| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. <br /> *Note, we will deprecate and no longer support the dbt_metrics package. | ✅ | ✅ |  ✅  | Open source, Core |
 | **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
 | **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
+| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
     
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
 
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 0fcfa557a5f..fc4bcf76204 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -3,7 +3,7 @@ title: "Legacy dbt Semantic Layer migration guide"
 id: sl-migration
 sidebar_label: "Legacy dbt Semantic Layer migration"
 description: "This migration guide will help you migrate from the legacy dbt Semantic Layer to the new one."
-tags: ["semantic-layer"]
+tags: [Semantic Layer]
 ---
 
 The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and supported on dbt v1.6 or higher. It's available to all dbt Cloud plans (Developer, Team, and Enterprise) and introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index cd861b7405d..67517bb4342 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,11 +1,11 @@
-:::info Upgrade your dbt version to access the new dbt Semantic Layer
+:::tip Upgrade to access the new dbt Semantic Layer
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. We've introduced improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing new APIs, and making it more efficient to define/query metrics. This means the legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
 
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt. The dbt_metrics package is no longer supported and won't receive any code fixes.
+**Who does this affect?** Anyone who uses the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 
-**What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new framework for defining metrics in dbt. This means dbt_metrics is no longer supported after dbt v1.5 and won't receive any code fixes.
 
-**Who does this affect?** The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
+**What should you do?** If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
 :::
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index 9d6ee48bc08..a5f8c2210bb 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -1,8 +1,8 @@
 
 :::tip Introducing the new dbt Semantic Layer 🎉
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and available open to all dbt Cloud plans. It introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs. 
+The dbt Semantic Layer has been re-released with [significant improvements](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. 
 
-**What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), one of the Semantic Layer's key component and a new way framework for defining metrics in dbt.
+The new version is available in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and introduces [MetricFlow](/docs/build/about-metricflow), an essential component. It also includes new semantic elements, better governance, improved efficiency, easier data access, and new Semantic Layer APIs.
 
 :::
diff --git a/website/snippets/_upgrade-new-sl.md b/website/snippets/_upgrade-new-sl.md
deleted file mode 100644
index 765b0db8dac..00000000000
--- a/website/snippets/_upgrade-new-sl.md
+++ /dev/null
@@ -1,8 +0,0 @@
-:::tip Upgrade to access the new dbt Semantic Layer
-
-The new dbt Semantic Layer has been re-released and is now available for users on a [Team or Enterprise plans](https://www.getdbt.com/pricing/) and you must be on dbt v1.6 and higher. 
-
-If you're using the legacy Semantic Layer, you **must** [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. The dbt_metrics package has been deprecated and replaced with MetricFlow.
-Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info. 
-
-:::
diff --git a/website/snippets/sl-prerequisites.md b/website/snippets/sl-prerequisites.md
index 09ede745431..0c100c299b0 100644
--- a/website/snippets/sl-prerequisites.md
+++ b/website/snippets/sl-prerequisites.md
@@ -7,7 +7,7 @@
   * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
 - Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
   * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> <br />
 
 </VersionBlock>
 
@@ -20,7 +20,7 @@
   * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
 - Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
   * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> <br />
 
 </VersionBlock>
 
@@ -33,6 +33,6 @@
   * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
 - Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
   * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
+- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> <br />
 
 </VersionBlock>
diff --git a/website/static/_redirects b/website/static/_redirects
index a9add52bac4..851030088aa 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,6 +1,6 @@
 ## semantic layer
 
-
+/guides/dbt-ecosystem/sl-partner-integration-guide  /docs/use-dbt-semantic-layer/avail-sl-integrations 301
 /docs/build/sl-getting-started  /docs/use-dbt-semantic-layer/quickstart-semantic-layer 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
 /docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301

From 5b941632a5d0ca621483c52939e99d3c397cc631 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 11:44:28 +0100
Subject: [PATCH 147/703] Update sidebars.js

---
 website/sidebars.js | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/sidebars.js b/website/sidebars.js
index a2659fdfff8..4dcc48c8d62 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -1083,7 +1083,6 @@ const sidebarSettings = {
             "guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project",
           ],
         },
-        "guides/dbt-ecosystem/sl-partner-integration-guide",
       ],
     },
     {

From e049a0e3ebe7edb40b312737363c0e173357e7ad Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 11:46:36 +0100
Subject: [PATCH 148/703] Update _redirects

---
 website/static/_redirects | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/static/_redirects b/website/static/_redirects
index 851030088aa..1b77d12b841 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,5 +1,7 @@
 ## semantic layer
 
+https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
+https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
 /guides/dbt-ecosystem/sl-partner-integration-guide  /docs/use-dbt-semantic-layer/avail-sl-integrations 301
 /docs/build/sl-getting-started  /docs/use-dbt-semantic-layer/quickstart-semantic-layer 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301

From af265cc155e7150edbd5e42ee8860e142596c354 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 12:08:37 +0100
Subject: [PATCH 149/703] adding browser session time

per [slack thread](https://dbt-labs.slack.com/archives/C0159442M18/p1689060702243859?thread_ts=1689054992.750199&cid=C0159442M18), adding a little section on browser time as this question has been asked a few times in slack from revenue teammates/users.

[] Needs security/technical review
---
 website/docs/docs/cloud/about-cloud/browsers.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index 4a04f70171b..d5a54fc6b4d 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -22,3 +22,8 @@ You may still be able to access and use dbt Cloud even without using the latest
 To improve your experience using dbt Cloud, we suggest that you turn off ad blockers.
 :::
 
+### Browser sessions
+
+A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, once you close your browser, you end your dbt Cloud session. This action logs you out and you'll need to re-login the next time you try to access dbt Cloud. 
+
+If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which may vary depending on your identity provider (IdP).

From 7f4b2c20fba027807a2cfb17fa75fb1a963e7af0 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 12:36:22 +0100
Subject: [PATCH 150/703] clarify project creator

clarify project creating per [slack thread](https://dbt-labs.slack.com/archives/CSSHB76QL/p1689607764718399?thread_ts=1689599777.396739&cid=CSSHB76QL)
---
 .../docs/docs/cloud/manage-access/enterprise-permissions.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index fb929bf2d59..5e80de449f0 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -17,7 +17,7 @@ control (RBAC).
 
 The following permission sets are available for assignment in dbt Cloud Enterprise accounts. They
 can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
-can be associated with more than one permission sets.
+can be associated with more than one permission set.
 
 ### Account Admin
 
@@ -72,13 +72,13 @@ Billing Admins have access to modify certain account-level settings related to b
 - **Has permissions on:** Authorized projects, account-level settings
 - **License restrictions:** must have a developer license
 
-Project Creators have write and read-only access to dbt Cloud accounts, but do not have the permissions required to modify SSO settings and account integrations. 
+Project Creators can access, create, or modify projects and other settings in dbt Cloud. However, they don't have permission to modify SSO settings or account integrations.
 
 Users with Project Creator permissions can:
 
 - View Account Settings
 - View and modify project users
-- Create, delete and modify all projects in an account
+- Create, delete, and modify all projects in an account
 - Create, delete, and modify Connections
 - Create, delete, and modify Environments
 - Create, delete, and modify Jobs

From 3f549bbad16ef8407a9632103bb7dca708410818 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 12:43:09 +0100
Subject: [PATCH 151/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index d5a54fc6b4d..b7dddf9fd11 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -24,6 +24,6 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 ### Browser sessions
 
-A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, once you close your browser, you end your dbt Cloud session. This action logs you out and you'll need to re-login the next time you try to access dbt Cloud. 
+A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, if you close your browser, this action ends your dbt Cloud session and logs you out. You'll need to re-login the next time you try to access dbt Cloud. 
 
 If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which may vary depending on your identity provider (IdP).

From 8d485f47c923e81add9284a99e29cb6564760dbb Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 18 Jul 2023 15:53:20 +0100
Subject: [PATCH 152/703] adding info on rowsaffected

---
 website/docs/reference/artifacts/run-results-json.md | 9 ++++++++-
 website/docs/reference/artifacts/sources-json.md     | 6 +++++-
 website/docs/reference/dbt-classes.md                | 4 ++++
 website/snippets/_run-result.md                      | 2 ++
 4 files changed, 19 insertions(+), 2 deletions(-)
 create mode 100644 website/snippets/_run-result.md

diff --git a/website/docs/reference/artifacts/run-results-json.md b/website/docs/reference/artifacts/run-results-json.md
index c421e8ea5f3..dd92a9c4e53 100644
--- a/website/docs/reference/artifacts/run-results-json.md
+++ b/website/docs/reference/artifacts/run-results-json.md
@@ -35,5 +35,12 @@ Each entry in `results` is a [`Result` object](/reference/dbt-classes#result-obj
 - `thread_id`: Which thread executed this node? E.g. `Thread-1`
 - `execution_time`: Total time spent executing this node
 - `timing`: Array that breaks down execution time into steps (often `compile` + `execute`)
-- `adapter_response`: Dictionary of metadata returned from the database, which varies by adapter. E.g. success `code`, number of `rows_affected`, total `bytes_processed`, etc. Not populated by tests, as of v0.19.0; we plan to fix in a future release ([dbt#2580](https://github.com/dbt-labs/dbt-core/issues/2580)).
 - `message`: How dbt will report this result on the CLI, based on information returned from the database
+
+import RowsAffected from '/snippets/_run-result.md'; 
+
+<RowsAffected/>
+
+<!-- this partial comes from https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/snippets/_run-result-->
+
+
diff --git a/website/docs/reference/artifacts/sources-json.md b/website/docs/reference/artifacts/sources-json.md
index 884aab6a5c5..3b7f7539896 100644
--- a/website/docs/reference/artifacts/sources-json.md
+++ b/website/docs/reference/artifacts/sources-json.md
@@ -23,6 +23,10 @@ Each entry in `results` is a dictionary with the following keys:
 - `max_loaded_at_time_ago_in_s`: Interval between `max_loaded_at` and `snapshotted_at`, calculated in python to handle timezone complexity.
 - `criteria`: The freshness threshold(s) for this source, defined in the project.
 - `status`: The freshness status of this source, based on `max_loaded_at_time_ago_in_s` + `criteria`, reported on the CLI. One of `pass`, `warn`, or `error` if the query succeeds, `runtime error` if the query fails.
-- `adapter_response`: Dictionary of information returned from the database, which varies by adapter. Not populated by source freshness checks, as of v0.19.0; we plan to fix in a future release ([dbt#2580](https://github.com/dbt-labs/dbt-core/issues/2580)).
 - `execution_time`: Total time spent checking freshness for this source
 - `timing`: Array that breaks down execution time into steps (`compile` + `execute`)
+
+import RowsAffected from '/snippets/_run-result.md'; 
+
+<RowsAffected/>
+
diff --git a/website/docs/reference/dbt-classes.md b/website/docs/reference/dbt-classes.md
index e4072a67be7..18569fce3b0 100644
--- a/website/docs/reference/dbt-classes.md
+++ b/website/docs/reference/dbt-classes.md
@@ -199,3 +199,7 @@ The execution of a resource in dbt generates a `Result` object. This object cont
 - `timing`: Array that breaks down execution time into steps (often `compile` + `execute`)
 - `adapter_response`: Dictionary of metadata returned from the database, which varies by adapter. E.g. success `code`, number of `rows_affected`, total `bytes_processed`, etc.
 - `message`: How dbt will report this result on the CLI, based on information returned from the database
+
+import RowsAffected from '/snippets/_run-result.md'; 
+
+<RowsAffected/>
diff --git a/website/snippets/_run-result.md b/website/snippets/_run-result.md
new file mode 100644
index 00000000000..2a209190365
--- /dev/null
+++ b/website/snippets/_run-result.md
@@ -0,0 +1,2 @@
+- `adapter_response`: Dictionary of metadata returned from the database, which varies by adapter. For example, success `code`, number of `rows_affected`, total `bytes_processed`, and so on. Not populated by tests, as of v0.19.0; we plan to fix in a future release ([dbt#2580](https://github.com/dbt-labs/dbt-core/issues/2580)).
+    * `rows_affected` returns the number of rows modified by the last statement executed. In cases where the query's row count can't be determined or isn't applicable (such as when creating a <Term id="view">view</Term>), a [standard value](https://peps.python.org/pep-0249/#rowcount) of `-1` is returned for `rowcount`.

From c20d6f4c7f60d1cf7fc8b718ba3882a0035d93de Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 18 Jul 2023 16:37:26 +0100
Subject: [PATCH 153/703] Update website/snippets/_run-result.md

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 website/snippets/_run-result.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_run-result.md b/website/snippets/_run-result.md
index 2a209190365..77a35676e86 100644
--- a/website/snippets/_run-result.md
+++ b/website/snippets/_run-result.md
@@ -1,2 +1,2 @@
-- `adapter_response`: Dictionary of metadata returned from the database, which varies by adapter. For example, success `code`, number of `rows_affected`, total `bytes_processed`, and so on. Not populated by tests, as of v0.19.0; we plan to fix in a future release ([dbt#2580](https://github.com/dbt-labs/dbt-core/issues/2580)).
+- `adapter_response`: Dictionary of metadata returned from the database, which varies by adapter. For example, success `code`, number of `rows_affected`, total `bytes_processed`, and so on. Not applicable for [tests](/docs/build/tests).
     * `rows_affected` returns the number of rows modified by the last statement executed. In cases where the query's row count can't be determined or isn't applicable (such as when creating a <Term id="view">view</Term>), a [standard value](https://peps.python.org/pep-0249/#rowcount) of `-1` is returned for `rowcount`.

From 3aebfd3a0f6f17b03d1d912d25a818df8be1f7e1 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Tue, 18 Jul 2023 09:56:35 -0700
Subject: [PATCH 154/703] Update
 website/docs/docs/dbt-cloud-apis/admin-cloud-api.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/admin-cloud-api.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
index 06d44fcbbc9..8a5712f40df 100644
--- a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
+++ b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
@@ -10,7 +10,7 @@ The dbt Cloud Administrative API is enabled by default for [Team and Enterprise
 - Manage your dbt Cloud account
 - and more
 
-dbt Cloud currently supports two versions of the Administrative API: v2 and v3. In general, v3 is the recommended version to use, but we don't yet have all our v2 routes upgraded to v3. We're currently working on this. If you can't find what you're looking for in our v3 docs, check out the shorter list of v2 endpoints and see if that helps. 
+dbt Cloud currently supports two versions of the Administrative API: v2 and v3. In general, v3 is the recommended version to use, but we don't yet have all our v2 routes upgraded to v3. We're currently working on this. If you can't find something in our v3 docs, check out the shorter list of v2 endpoints because you might find it there. 
 
 <div className="grid--2-col">
 

From 4a6c632da3b349df4add7afafb90037422a79cdf Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 10:12:26 -0700
Subject: [PATCH 155/703] Update sl-graphql.md

Adding a note on authentication for graphql
---
 website/docs/docs/dbt-cloud-apis/sl-graphql.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 286ed79621c..d468bdb71f6 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -24,6 +24,16 @@ GraphQL has several advantages, such as self-documenting, having a strong typing
 
 The dbt Semantic Layer GraphQL API offers capabilities like the JDBC API. It allows you to explore and execute commands conveniently through its self-documenting nature using the schema explorer (need link from roxi). 
 
+
+### Authentication 
+
+Authentication uses a dbt Cloud Service token passed through a header as follows:
+
+```
+{"Authorization": "Bearer <SERVICE TOKEN>"}
+```
+
+
 ### Metric metadata calls
 
 Use the following example calls to provide you with an idea of the types of commands you can use:

From 487dd053943e8050720083219df9f166157e51da Mon Sep 17 00:00:00 2001
From: Ajay Thomas <ajaythomas.inc@gmail.com>
Date: Tue, 18 Jul 2023 13:00:29 -0400
Subject: [PATCH 156/703] New screenshot for gsuite migration to Auth0

---
 .../set-up-sso-google-workspace.md            |   2 +-
 .../access-control/google-enable.png          | Bin 118831 -> 119283 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
index 1cfa9a49286..8d52096d4bf 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
@@ -49,7 +49,7 @@ Client Secret for use in dbt Cloud.
 | **Application type**   | internal     | required |
 | **Application name**   | dbt Cloud    | required |
 | **Application logo**   | Download the logo <a href="https://www.getdbt.com/ui/img/dbt-icon.png" target="_blank" rel="noopener noreferrer">here</a> | optional |
-| **Authorized domains** | `getdbt.com` | If deploying into a VPC, use the domain for your deployment |
+| **Authorized domains** | `getdbt.com` | If you are in EMEA or AU multi-tenant, use `dbt.com`. If deploying into a VPC, use the domain for your deployment |
 | **Scopes** | `email, profile, openid` | The default scopes are sufficient |
 
 <Lightbox src="/img/docs/dbt-cloud/dbt-cloud-enterprise/gsuite/gsuite-sso-consent-top.png" title="GSuite Consent Screen configuration"/>
diff --git a/website/static/img/docs/dbt-cloud/access-control/google-enable.png b/website/static/img/docs/dbt-cloud/access-control/google-enable.png
index 9de449b7944c2e13c804ed96a9612cdce85accde..f87858ff7519173bcf0f15f98433e22e084915c8 100644
GIT binary patch
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From 3fb68e638691de6cdef525ad73a8217a06519cdf Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 18 Jul 2023 14:10:49 -0400
Subject: [PATCH 157/703] Update set-up-sso-google-workspace.md

---
 .../docs/cloud/manage-access/set-up-sso-google-workspace.md     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
index 8d52096d4bf..a206d359270 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-google-workspace.md
@@ -49,7 +49,7 @@ Client Secret for use in dbt Cloud.
 | **Application type**   | internal     | required |
 | **Application name**   | dbt Cloud    | required |
 | **Application logo**   | Download the logo <a href="https://www.getdbt.com/ui/img/dbt-icon.png" target="_blank" rel="noopener noreferrer">here</a> | optional |
-| **Authorized domains** | `getdbt.com` | If you are in EMEA or AU multi-tenant, use `dbt.com`. If deploying into a VPC, use the domain for your deployment |
+| **Authorized domains** | `getdbt.com` (US) `dbt.com` (EMEA or AU) | If deploying into a VPC, use the domain for your deployment |
 | **Scopes** | `email, profile, openid` | The default scopes are sufficient |
 
 <Lightbox src="/img/docs/dbt-cloud/dbt-cloud-enterprise/gsuite/gsuite-sso-consent-top.png" title="GSuite Consent Screen configuration"/>

From 8e26091d82ecfcf5307f8c910a4ef263885540e5 Mon Sep 17 00:00:00 2001
From: Nick Handel <nicholas.handel@gmail.com>
Date: Tue, 18 Jul 2023 11:11:40 -0700
Subject: [PATCH 158/703] Add nicks additions to the sl-revamp-beta doc

---
 .../06-July-2023/sl-revamp-beta.md            | 31 ++++++++++---------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 78b24a89709..8d37cd78c5d 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -7,11 +7,11 @@ date: 2023-07-31
 sidebar_position: 9
 ---
 
-We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It revolutionizes data analysis by introducing:
+We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
 
-- [MetricFlow](/docs/build/about-metricflow), a new way to define metrics in dbt and one of the key components of the dbt Semantic Layer,
-- New semantic components like semantic models and metrics, which help lay the foundation for effective organization and interaction with data,
-- Improved governance, enhanced efficiency, and data accessibility,
+- [MetricFlow](/docs/build/about-metricflow), a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
+- New Semantic Layer Infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift and soon more) and improved performance.
+- New and Improved [developer workflows](/docs/guides/migration/sl-migration), governance, and collaboration features.
 - New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
@@ -20,32 +20,33 @@ With semantics at its core, the dbt Semantic Layer marks a crucial milestone tow
 
 ## Enhanced dbt Semantic Layer
 
-What sets the dbt Semantic Layer apart is its ability to centralize logic and data applications, streamlining processes and enabling efficient management and utilization of data models. It provides a consistent view of data, incorporating context and historical information, simplifying complex tasks, and reducing costs.
+What sets the dbt Semantic Layer apart is its ability to centralize logic for many downstream data applications, streamlining access and governance and enabling more efficient utilization of data models. It provides a consistent view of data while simplifying complex tasks in downstream applications and reducing the costs of and barriers to data access.
 
 We are excited to present several important capabilities with the enhanced dbt Semantic Layer:
 
-- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
+- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization and teh breadth of interfaces where data is consumed. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
 
 - **Improved governance**: The dbt Semantic Layer ensures proper governance and auditing of data changes, providing an auditable record of modifications and clear ownership. This saves time by making it clear who can create and manage new metrics, ensuring accountability and data integrity.
 
-- **Reduce costs**: The dbt Semantic Layer simplifies complex tasks, such as bridging entities across a semantic graph. Often users duplicate slices and dice of data and make them available in a data platform, making it difficult to manage and causing high computation. The dbt Semantic Layer minimizes duplication of work and reducing computational costs - allowing users to focus on analyzing data rather than navigating intricate technical processes or duplicating work. 
+- **Reduce costs**: The dbt Semantic Layer simplifies complex tasks, such as bridging entities across a semantic graph. Often users duplicate slices and dice of data and make them available in a data platform, making it difficult to manage and causing high computation. The dbt Semantic Layer minimizes duplication of work and reduces computational costs - allowing users to focus on analyzing data rather than navigating intricate technical processes or duplicating work.
 
-- **Enhanced efficiency**: With the dbt Semantic Layer, data teams can create and update metrics in one central location, saving time and reducing the risk of errors and downstream impacts. The agnostic approach allows seamless integration with various data platforms and downstream BI tools, making migration easier and more flexible.
+- **Enhanced efficiency**: With the dbt Semantic Layer, data teams can create and update metrics using a new set of validations that make defining and iterating on metrics efficient. The streamlined development workflows makes it simpler for a data team to serve large organizations with broad data needs.
 
-- **Accessible data**: Without the dbt Semantic Layer, there would be limited access to critical information which stifles effective communication. An accessible semantic layer allows all users to have equal opportunities to leverage data insights, fostering collaboration and driving innovation. 
+- **Accessible data**: Defining common metrics and dimensions and making them joinable, makes access simpler for users with less expertise in the specifics of a companies data modeling work. This creates opportunities to leverage data insights, fostering collaboration and driving innovation in a more inclusive data environment.
 
-By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows. 
+By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows.
 
-## Public beta 
+## Public beta
 
 The dbt Semantic Layer is currently available as a public beta, which means:
 
-- **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. 
-  * Developer plans or dbt Core users can use MetricFlow to define and test metrics using the CLI only. 
+- **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. Look out for announcements on removing the location requirement soon.
 
-- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced). DO WE MENTION THAT WE'RE NOT CHARGING?
+  - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt-metricflow CLI only.
 
-- **When** &mdash; Public beta will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will make pricing information available.  DO WE MENTION THAT WE'LL BEGIN CHARGING?
+- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account rep.
+
+- **When** &mdash; Public beta starts on July 31 and will end once the dbt Semantic Layer is available for GA in October 2023.
 
 - **Where** &mdash; You can experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
 

From 19db6f3d760bfa58c4551340f7b8b05c60ae9c6f Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Tue, 18 Jul 2023 12:26:12 -0600
Subject: [PATCH 159/703] Expanded docs for `deprecation_date` (#3754)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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## What are you changing in this pull request and why?

Resolves #3745

- Updated docs for [model
versions](https://docs.getdbt.com/docs/collaborate/govern/model-versions)
including link back to the reference docs on `deprecation_date`
- Add examples with different accepted formats, including an aware
datetime, a naive datetime, and a plain date
   - `1999-01-01 00:00:00.00+00:00`
   - `2023-12-31 23:59:59.99`
   - `2024-01-01`

## Previews
-
[deprecation_date](https://deploy-preview-3754--docs-getdbt-com.netlify.app/reference/resource-properties/deprecation_date)
-
[model-versions](https://deploy-preview-3754--docs-getdbt-com.netlify.app/docs/collaborate/govern/model-versions)

## 🎩

<img width="651" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/17a3db91-5842-4611-9f66-60ac5c0605b2">

## Key questions to answer

### Why would I set a `deprecation_date` for my models?

Declaring a deprecation date is a way of signaling the maturity level of
a model, and communicating plans for long-term support and maintenance.
It provides a mechanism to communicate plans and timelines for
sunsetting models so they don't need to be maintained and supported
indefinitely and their build and storage costs can be freed up.

### What happens after a model is deprecated?

Does it stop building/being selected? What are the warning/error
messages I could expect to see, for myself and for downstream queriers?

Deprecated models can still continue to be built by producers and be
selected by consumers until they are
[disabled](https://docs.getdbt.com/reference/resource-configs/enabled)
or removed.

Models marked for deprecation will raise informative warnings when they
are parsed or referenced. (With cross-project references, dbt raises
these warnings in downstream projects that are ref'ing a deprecated
version of a public model from an upstream project.)

When a project references a model that's slated for deprecation, a
warning is generated. If it's a versioned model, with a newer version
available, then the warning says so. This added bit of communication,
from producers to consumers, is an advantage of using dbt's built-in
functionality around model versions to facilitate migrations.

Additionally,
[`WARN_ERROR_OPTIONS`](https://docs.getdbt.com/reference/global-configs/warnings)
gives a mechanism whereby users can promote these warnings to actual
runtime errors.

**Warning messages:**
* DeprecatedModel (warning when parsing a project that defines
deprecated model(s)): `[WARNING] Model {model_name}[.v{version} if
versioned] has passed its deprecation date of {deprecation_date}. This
model should be disabled or removed.`
* UpcomingDeprecationReference (warning when referencing a model with a
future deprecation date): `[WARNING] While compiling
'{this_model_name}': Found a reference to {ref_model_name}[.v{version}
if versioned], which is slated for deprecation on
'{ref_model_deprecation_date}'. [if versioned: A new version of
'{ref_model_name}' is available. Try it out: {{ ref('ref_model_package',
'ref_model_name', v='{ref_model_latest_version}') }}.`
* DeprecatedReference (warning when referencing a model with a past
deprecation date): `[WARNING] While compiling '{this_model_name}': Found
a reference to {ref_model_name}[.v{version} if versioned], which was
deprecated on '{ref_model_deprecation_date}'. [if versioned: A new
version of '{ref_model_name}' is available. Migrate now: {{
ref('ref_model_package', 'ref_model_name',
v='{ref_model_latest_version}') }}.`

### How does this relate to model versions?

Do I have to be using model contracts/versions/etc to set a
`deprecation_date`?

(no!)

Setting a `deprecation_date` works well in conjunction with other [model
governance](https://docs.getdbt.com/docs/collaborate/govern/about-model-governance)
features like [model
versions](https://docs.getdbt.com/docs/collaborate/govern/model-versions),
but can also be used independently from them.

### Is there specific selection syntax I can use, to select only
deprecated models?

(I don't think we've implemented this)

There is not specific [node selection
syntax](https://docs.getdbt.com/reference/node-selection/syntax) for
`deprecation_date`. [Programmatic
invocations](https://docs.getdbt.com/reference/programmatic-invocations)
is one way to identify deprecated models (potentially in conjunction
with [dbt list](https://docs.getdbt.com/reference/commands/list)). e.g.,
`dbt -q ls --output json --output-keys database schema alias
deprecation_date`.

### How can I clean up / remove tables from the DWH associated with
deprecated models?

(nothing built-in/automated, but is there a discourse/pattern we could
point people toward?)

Just like it [does not automatically drop relations when models are
deleted](https://docs.getdbt.com/faqs/models/removing-deleted-models),
dbt does not removed tables for deprecated models.

Strategies similar to
[here](https://discourse.getdbt.com/t/faq-cleaning-up-removed-models-from-your-production-schema/113)
or
[here](https://discourse.getdbt.com/t/clean-your-warehouse-of-old-and-deprecated-models/1547)
can be used to drop relations that have been deprecated and are no
longer in use.

### How does this relate to [table expiration on
BQ](https://docs.getdbt.com/reference/resource-configs/bigquery-configs#controlling-table-expiration)?

(they're different things, but maybe you'd want to use them in
synchrony)

dbt-bigquery can set an
[`hours_to_expiration`](https://docs.getdbt.com/reference/resource-configs/bigquery-configs#controlling-table-expiration)
that [translates to
`expiration_timestamp`](https://github.com/dbt-labs/dbt-bigquery/blob/ea258bb76169375ded8f7ff9e596a436a5ed165a/dbt/adapters/bigquery/impl.py#L844-L846)
(expired tables in BigQuery will be deleted and their storage
reclaimed).

dbt does not automatically synchronize `deprecation_date` and
`hours_to_expiration`, but users may want to coordinate them in some
fashion (such as setting a model to expire 48 hours after its
`deprecation_date`).

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
---
 .../docs/collaborate/govern/model-versions.md |  6 +-
 .../resource-properties/deprecation_date.md   | 56 ++++++++++++++++++-
 2 files changed, 58 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index b38ed13289d..12599d0b65f 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -37,7 +37,7 @@ Instead, for mature models at larger organizations, powering queries inside & ou
 
 During that migration window, anywhere that model is being used downstream, it can continue to be referenced at a specific version.
 
-In the future, dbt will also offer first-class support for **deprecating models** ([dbt-core#7433](https://github.com/dbt-labs/dbt-core/issues/7433)). Taken together, model versions and deprecation offer a pathway for model producers to _sunset_ old models, and consumers the time to _migrate_ across breaking changes. It's a way of managing change across an organization: develop a new version, bump the latest, slate the old version for deprecation, update downstream references, and then remove the old version.
+dbt Core 1.6 introduced first-class support for **deprecating models** by specifying a [`deprecation_date`](/reference/resource-properties/deprecation_date). Taken together, model versions and deprecation offer a pathway for model producers to _sunset_ old models, and consumers the time to _migrate_ across breaking changes. It's a way of managing change across an organization: develop a new version, bump the latest, slate the old version for deprecation, update downstream references, and then remove the old version.
 
 There is a real trade-off that exists here—the cost to frequently migrate downstream code, and the cost (and clutter) of materializing multiple versions of a model in the data warehouse. Model versions do not make that problem go away, but by setting a deprecation date, and communicating a clear window for consumers to gracefully migrate off old versions, they put a known boundary on the cost of that migration.
 
@@ -73,7 +73,7 @@ As the **producer** of a versioned model:
 - You keep track of all live versions in one place, rather than scattering them throughout the codebase
 - You can reuse the model's configuration, and highlight just the diffs between versions
 - You can select models to build (or not) based on whether they're a `latest`, `prerelease`, or `old` version
-- dbt will notify consumers of your versioned model when new versions become available, or (in the future) when they are slated for deprecation
+- dbt will notify consumers of your versioned model when new versions become available, or when they are slated for deprecation
 
 As the **consumer** of a versioned model:
 - You use a consistent `ref`, with the option of pinning to a specific live version
@@ -109,7 +109,7 @@ selectors:
 
 </File>
 
-Because dbt knows that these models are _actually the same model_, it can notify downstream consumers as new versions become available, and (in the future) as older versions are slated for deprecation.
+Because dbt knows that these models are _actually the same model_, it can notify downstream consumers as new versions become available, and as older versions are slated for deprecation.
 
 ```bash
 Found an unpinned reference to versioned model 'dim_customers'.
diff --git a/website/docs/reference/resource-properties/deprecation_date.md b/website/docs/reference/resource-properties/deprecation_date.md
index 9fe9e2e1098..830412d2af6 100644
--- a/website/docs/reference/resource-properties/deprecation_date.md
+++ b/website/docs/reference/resource-properties/deprecation_date.md
@@ -28,5 +28,59 @@ models:
 
 ## Definition
 
-The deprecation date of the model in YAML DateTime format. 
+The deprecation date of the model is formatted as a date, optionally with a timezone offset. Supported RFC 3339 formats include:
+- `YYYY-MM-DD hh:mm:ss.sss±hh:mm`
+- `YYYY-MM-DD hh:mm:ss.sss`
+- `YYYY-MM-DD`
 
+When `deprecation_date` does not include an offset from UTC, then it is interpreted as being in the system time zone of the dbt execution environment.
+
+## Explanation
+
+### Purpose
+
+Declaring a `deprecation_date` for a dbt model provides a mechanism to communicate plans and timelines for long-term support and maintenance and to facilitate change management.
+
+Setting a `deprecation_date` works well in conjunction with other [model governance](/docs/collaborate/govern/about-model-governance) features like [model versions](/docs/collaborate/govern/model-versions), but can also be used independently from them.
+
+### Warning messages
+
+When a project references a model that's slated for deprecation or the deprecation date has passed, a warning is generated. If it's a versioned model, with a newer version available, then the warning says so. This added bit of cross-team communication, from producers to consumers, is an advantage of using dbt's built-in functionality around model versions to facilitate migrations.
+
+Additionally, [`WARN_ERROR_OPTIONS`](/reference/global-configs/warnings) gives a mechanism whereby users can promote these warnings to actual runtime errors:
+
+| Warning                        | Scenario                                           | Affected projects      |
+|--------------------------------|----------------------------------------------------|------------------------|
+|        `DeprecatedModel`       | Parsing a project that defines a deprecated model  | Producer               |
+| `DeprecatedReference`          | Referencing a model with a past deprecation date   | Producer and consumers |
+| `UpcomingDeprecationReference` | Referencing a model with a future deprecation date | Producer and consumers |
+
+** Example **
+
+Example output for an `UpcomingDeprecationReference` warning:
+```
+$ dbt parse
+15:48:14  Running with dbt=1.6.0
+15:48:14  Registered adapter: postgres=1.6.0
+15:48:14  [WARNING]: While compiling 'my_model_ref': Found a reference to my_model, which is slated for deprecation on '2038-01-19T03:14:07-00:00'.
+```
+
+### Selection syntax
+
+There is not specific [node selection syntax](/reference/node-selection/syntax) for `deprecation_date`. [Programmatic invocations](/reference/programmatic-invocations) is one way to identify deprecated models (potentially in conjunction with [dbt list](/reference/commands/list)). e.g., `dbt -q ls  --output json --output-keys database schema alias deprecation_date`.
+
+### Deprecation process
+
+Additional steps are necessary to save on build-related compute and storage costs for a deprecated model.
+
+Deprecated models can continue to be built by producers and be selected by consumers until they are [disabled](/reference/resource-configs/enabled) or removed.
+
+Just like it does not automatically [drop relations when models are deleted](/faqs/models/removing-deleted-models), dbt does not drop relations for deprecated models.
+
+Strategies similar to [here](https://discourse.getdbt.com/t/faq-cleaning-up-removed-models-from-your-production-schema/113) or [here](https://discourse.getdbt.com/t/clean-your-warehouse-of-old-and-deprecated-models/1547) can be used to drop relations that have been deprecated and are no longer in use.
+
+### Table expiration on BigQuery
+
+dbt-bigquery can set an [`hours_to_expiration`](/reference/resource-configs/bigquery-configs#controlling-table-expiration) that translates to `expiration_timestamp` within BigQuery.
+
+dbt does not automatically synchronize `deprecation_date` and `hours_to_expiration`, but users may want to coordinate them in some fashion (such as setting a model to expire 48 hours after its `deprecation_date`). Expired tables in BigQuery will be deleted and their storage reclaimed.

From 5d02a3d14b4a37507bede45f9fe2ec2c1b3100d8 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 18 Jul 2023 16:14:02 -0400
Subject: [PATCH 160/703] Service token update text

---
 .../docs/dbt-cloud-apis/service-tokens.md     |  19 ++++++++++++++++++
 .../service-token-date.png                    | Bin 0 -> 50236 bytes
 2 files changed, 19 insertions(+)
 create mode 100644 website/static/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png

diff --git a/website/docs/docs/dbt-cloud-apis/service-tokens.md b/website/docs/docs/dbt-cloud-apis/service-tokens.md
index 139eff8fd07..e33ed772c00 100644
--- a/website/docs/docs/dbt-cloud-apis/service-tokens.md
+++ b/website/docs/docs/dbt-cloud-apis/service-tokens.md
@@ -3,6 +3,11 @@ title: "Service account tokens"
 id: "service-tokens"
 description: "Service account tokens help you define permissions for securing access to your dbt Cloud account and its projects."
 ---
+:::info Important service account token update
+
+If you have service tokens created prior to July 11, 2023, please read [this important update](/docs/dbt-cloud-apis/service-tokens#service-token-update).
+
+:::
 
 ## About service tokens
 
@@ -92,3 +97,17 @@ Analyst admin service tokens have all the permissions listed in [Analyst](/docs/
 
 **Stakeholder**<br/>
 Stakeholder service tokens have all the permissions listed in [Stakeholder](/docs/cloud/manage-access/enterprise-permissions#stakeholder) on the Enterprise Permissions page.
+
+
+## Service token update
+
+On July 11, 2023, dbt Labs made critical infrastructure changes to service account tokens. These enhancements improve the security and performance of all tokens created after July 11, 2023. To ensure security best practices are in place, we recommend you rotate your service tokens created before this date.
+
+To rotate your token:
+1. Navigate to **Account settings** and click **Service tokens** on the left side pane.
+2. Verify the **Created** date for the token is _before_ July 11, 2023. 
+    <Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png" title="Service token created date"/>
+3. Click **+ New Token** on the top right side of the screen. Ensure the new token has the same permissions as the old one. 
+4. Copy the new token and replace the old one in your systems. Store it in a safe place, as it will not be available again once the creation screen is closed.
+5. Delete the old token in dbt Cloud by clicking the **trash can icon**. -Only take this action before the new token is in place to avoid service disruptions_.
+
diff --git a/website/static/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png b/website/static/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png
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literal 0
HcmV?d00001


From 30a856a2c6011f4914cde0f4ec55c782b9dd296b Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Tue, 18 Jul 2023 13:28:56 -0700
Subject: [PATCH 161/703] add initial defer docs

---
 website/docs/reference/commands/clone.md       | 10 +++++++++-
 website/docs/reference/node-selection/defer.md |  3 +++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 150d3b12e62..07b9259eeb7 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -27,4 +27,12 @@ dbt clone --state path/to/artifacts --full-refresh
 
 # clone all of my models from specified state to my target schema(s), running up to 50 clone statements in parallel
 dbt clone --state path/to/artifacts --threads 50
-```
\ No newline at end of file
+```
+
+### When to use `dbt clone` instead of [deferral](/reference/node-selection/defer)?
+
+Unlike deferral, `dbt clone` requires some compute and creation of additional objects in your data warehouse. In many cases, deferral is a cheaper and simpler alternative to `dbt clone`. However, `dbt clone` covers additional use cases where deferral may not be possible.
+
+For example, by creating actual data warehouse objects, `dbt clone` allows you to test out your code changes on downstream dependencies _outside of dbt_ (such as a BI tool). 
+
+As another example, you could `clone` your modified incremental models as the first step of your dbt Cloud CI job to prevent costly `full-refresh` builds for warehouses that support zero-copy cloning.
\ No newline at end of file
diff --git a/website/docs/reference/node-selection/defer.md b/website/docs/reference/node-selection/defer.md
index a6ef6261cf1..e33f95a5142 100644
--- a/website/docs/reference/node-selection/defer.md
+++ b/website/docs/reference/node-selection/defer.md
@@ -14,6 +14,9 @@ Deferral is a powerful, complex feature that enables compelling workflows. As th
 Defer is a powerful feature that makes it possible to run a subset of models or tests in a [sandbox environment](/docs/environments-in-dbt) without having to first build their upstream parents. This can save time and computational resources when you want to test a small number of models in a large project.
 
 Defer requires that a manifest from a previous dbt invocation be passed to the `--state` flag or env var. Together with the `state:` selection method, these features enable "Slim CI". Read more about [state](/reference/node-selection/syntax#about-node-selection).
+
+An alternative command that accomplishes similar functionality for different use cases is `dbt clone` - see the docs for [clone](/reference/commands/clone#when-to-use-dbt-clone-instead-of-deferral) for more information.
+
 ### Usage
 
 ```shell

From e1b997289b9d41965f043b2ca0fcd42a935422d5 Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Tue, 18 Jul 2023 13:30:54 -0700
Subject: [PATCH 162/703] add caveats for useful bullets

---
 website/docs/reference/commands/clone.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 07b9259eeb7..3f74d7cd88a 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -11,9 +11,10 @@ The `dbt clone` command clones selected nodes from the [specified state](/refere
 - You may want to specify a higher number of [threads](/docs/running-a-dbt-project/using-threads) to decrease execution time since individual clone statements are independent of one another.
 
 The `clone` command is useful for:
-- blue/green continuous deployment
+- blue/green continuous deployment (on data warehouses that support zero-copy cloning tables)
 - cloning current production state into development schema(s)
-- handling incremental models in Slim CI dbt Cloud jobs
+- handling incremental models in Slim CI dbt Cloud jobs (on data warehouses that support zero-copy cloning tables)
+- testing code changes on downstream dependencies in your BI tool
 
 ```bash
 # clone all of my models from specified state to my target schema(s)

From 1a087c7c47c3a2268ab3d085947a204851fe29e3 Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Tue, 18 Jul 2023 14:04:28 -0700
Subject: [PATCH 163/703] error note

---
 website/docs/reference/commands/clone.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 3f74d7cd88a..6b58a107a3b 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -4,7 +4,7 @@ sidebar_label: "clone"
 id: "clone"
 ---
 
-The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). This command makes use of the `clone` materialization:
+The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). You must always use different directories for source and target otherwise you will get an error message. This command makes use of the `clone` materialization:
 - If your data platform supports zero-copy cloning of tables, and this model exists as a table in the source environment, dbt will create it in your target environment as a clone
 - Otherwise, dbt will create a simple pointer view (`select * from` the source object)
 - By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 

From 72b217b8c375037a791bea195e2cf8bac605659c Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 18 Jul 2023 17:08:43 -0400
Subject: [PATCH 164/703] Adding date

---
 website/docs/docs/dbt-cloud-apis/service-tokens.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/service-tokens.md b/website/docs/docs/dbt-cloud-apis/service-tokens.md
index e33ed772c00..f60cfe61a9b 100644
--- a/website/docs/docs/dbt-cloud-apis/service-tokens.md
+++ b/website/docs/docs/dbt-cloud-apis/service-tokens.md
@@ -5,7 +5,7 @@ description: "Service account tokens help you define permissions for securing ac
 ---
 :::info Important service account token update
 
-If you have service tokens created prior to July 11, 2023, please read [this important update](/docs/dbt-cloud-apis/service-tokens#service-token-update).
+If you have service tokens created on or before July 18, 2023, please read [this important update](/docs/dbt-cloud-apis/service-tokens#service-token-update).
 
 :::
 
@@ -101,13 +101,13 @@ Stakeholder service tokens have all the permissions listed in [Stakeholder](/doc
 
 ## Service token update
 
-On July 11, 2023, dbt Labs made critical infrastructure changes to service account tokens. These enhancements improve the security and performance of all tokens created after July 11, 2023. To ensure security best practices are in place, we recommend you rotate your service tokens created before this date.
+On July 18, 2023, dbt Labs made critical infrastructure changes to service account tokens. These enhancements improve the security and performance of all tokens created after July 18, 2023. To ensure security best practices are in place, we recommend you rotate your service tokens created before this date.
 
 To rotate your token:
 1. Navigate to **Account settings** and click **Service tokens** on the left side pane.
-2. Verify the **Created** date for the token is _before_ July 11, 2023. 
+2. Verify the **Created** date for the token is _on or before_ July 18, 2023. 
     <Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png" title="Service token created date"/>
 3. Click **+ New Token** on the top right side of the screen. Ensure the new token has the same permissions as the old one. 
 4. Copy the new token and replace the old one in your systems. Store it in a safe place, as it will not be available again once the creation screen is closed.
-5. Delete the old token in dbt Cloud by clicking the **trash can icon**. -Only take this action before the new token is in place to avoid service disruptions_.
+5. Delete the old token in dbt Cloud by clicking the **trash can icon**. _Only take this action before the new token is in place to avoid service disruptions_.
 

From 91b51afa70b75894b93d2801aeda73f81f1a6771 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 18 Jul 2023 17:14:23 -0400
Subject: [PATCH 165/703] Update
 website/docs/docs/dbt-cloud-apis/service-tokens.md

---
 website/docs/docs/dbt-cloud-apis/service-tokens.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/service-tokens.md b/website/docs/docs/dbt-cloud-apis/service-tokens.md
index f60cfe61a9b..811bfaea29d 100644
--- a/website/docs/docs/dbt-cloud-apis/service-tokens.md
+++ b/website/docs/docs/dbt-cloud-apis/service-tokens.md
@@ -109,5 +109,5 @@ To rotate your token:
     <Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/service-token-date.png" title="Service token created date"/>
 3. Click **+ New Token** on the top right side of the screen. Ensure the new token has the same permissions as the old one. 
 4. Copy the new token and replace the old one in your systems. Store it in a safe place, as it will not be available again once the creation screen is closed.
-5. Delete the old token in dbt Cloud by clicking the **trash can icon**. _Only take this action before the new token is in place to avoid service disruptions_.
+5. Delete the old token in dbt Cloud by clicking the **trash can icon**. _Only take this action after the new token is in place to avoid service disruptions_.
 

From c42a5300ceaa8cef70659462a33c37a419db1480 Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Tue, 18 Jul 2023 14:22:12 -0700
Subject: [PATCH 166/703] error message begone

---
 website/docs/reference/commands/clone.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 6b58a107a3b..3f74d7cd88a 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -4,7 +4,7 @@ sidebar_label: "clone"
 id: "clone"
 ---
 
-The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). You must always use different directories for source and target otherwise you will get an error message. This command makes use of the `clone` materialization:
+The `dbt clone` command clones selected nodes from the [specified state](/reference/node-selection/syntax#establishing-state) to the target schema(s). This command makes use of the `clone` materialization:
 - If your data platform supports zero-copy cloning of tables, and this model exists as a table in the source environment, dbt will create it in your target environment as a clone
 - Otherwise, dbt will create a simple pointer view (`select * from` the source object)
 - By default, `dbt clone` will not recreate pre-existing relations in the current target. To override this, use the `--full-refresh` flag. 

From 8cbbcf818899f1a529579ca1f5f840878e37b736 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 14:27:11 -0700
Subject: [PATCH 167/703] Update setup-sl.md

making a few other changes to setup to incorporate the graphql pai
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 6c2212081e5..7a5488a92ea 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -34,7 +34,7 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 3. To configure the new Semantic Layer, you must have a successful run in your new environment. We recommend running `dbt ls` since `dbt build` won’t succeed until you’ve created and defined semantic models and metrics.
 4. To enable the dbt Semantic Layer, go to the **Account Settings** page and then select the specific project you want to enable the Semantic Layer for.
 5. In the **Project Details** page, select **Configure Semantic Layer.** This will prompt you to enter data platform connection credentials for the Semantic Layer and select the environment where you want to enable the Semantic Layer. We recommend using a less privileged set of credentials when setting up your connection. The semantic layer requires SELECT and CREATE TABLE permissions.
-6. After you’ve entered your credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the semantic layer JDBC.
+6. After you’ve entered your credentials, you should see connection information that will allow you to connect to downstream tools. If the tool you are using can connect with JDBC, you can save the **JDBC URL** or each of the individual components provided (e.g., environment id, host). Alternatively, if the tool you connect to uses the Semantic Layer GraphQL API, save the GraphQL API host information.
 7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
 8. You’re done 🎉! The semantic layer should is now enabled for your project. 
 -->

From d123c595f5471290f5f0abbdbbe260ee44212cc2 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 14:38:48 -0700
Subject: [PATCH 168/703] Update sl-revamp-beta.md

Trying to fix this broken link
---
 .../dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 8d37cd78c5d..9c0b7f34828 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -11,7 +11,7 @@ We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use
 
 - [MetricFlow](/docs/build/about-metricflow), a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
 - New Semantic Layer Infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift and soon more) and improved performance.
-- New and Improved [developer workflows](/docs/guides/migration/sl-migration), governance, and collaboration features.
+- New and Improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
 - New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.

From 0f830c11b376e1444d382dd02dee6995209c75dc Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 15:13:06 -0700
Subject: [PATCH 169/703] Update sl-graphql.md

More GQL info
---
 website/docs/docs/dbt-cloud-apis/sl-graphql.md | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index d468bdb71f6..4c29838bf0d 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -14,25 +14,32 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 </VersionBlock>
 
 
-[GraphQL](https://graphql.org/) is an open-source query language for APIs. It offers a more efficient and flexible approach compared to traditional RESTful APIs. 
+[GraphQL](https://graphql.org/) (GQL) is an open-source query language for APIs. It offers a more efficient and flexible approach compared to traditional RESTful APIs. 
 
 With GraphQL, users can request specific data using a single query, reducing the need for many server round trips. This improves performance and minimizes network overhead.
 
 GraphQL has several advantages, such as self-documenting, having a strong typing system, supporting versioning and evolution, enabling rapid development, and having a robust ecosystem. These features make GraphQL a powerful choice for APIs that prioritize flexibility, performance, and developer productivity.
 
+dbt Partners can use the Semantic Layer GraphQL API to build and integration with the dbt Semantic Layer.
+
 ## dbt Semantic Layer GraphQL API
 
-The dbt Semantic Layer GraphQL API offers capabilities like the JDBC API. It allows you to explore and execute commands conveniently through its self-documenting nature using the schema explorer (need link from roxi). 
+The dbt Semantic Layer GraphQL API allows you to explore and query metrics and dimensions. Due to it's self-documenting nature, you can explore the calls conveniently through the [schema explorer](https://cloud.getdbt.com/semantic-layer/api/graphql). 
+
+## Using the GraphQL API
 
+If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API in your oown instance by configuring the Semantic Layer and obtaining the right GQL onnection parameters provided. (PROVIDE link to set up?) 
 
 ### Authentication 
 
-Authentication uses a dbt Cloud Service token passed through a header as follows:
+Authentication uses a dbt Cloud Service token passed through a header as follows. To explore the schema, you can enter this information in the "header" section.
 
 ```
 {"Authorization": "Bearer <SERVICE TOKEN>"}
 ```
 
+Each GQL request also comes with a dbt Cloud Environment Id. Our API will use the combination of the Service Token in the header and Environment Id to authenticate.
+
 
 ### Metric metadata calls
 

From 91435da0ad7b080545d3d674c1d63cf351175d91 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 15:18:09 -0700
Subject: [PATCH 170/703] Update sl-jdbc.md

A note on "using the jdbc api"
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index d8595a24572..84610bd4d3b 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -24,9 +24,16 @@ A JDBC driver is a software component enabling a Java application to interact wi
 - You can embed the driver into your application stack as needed, and you can use dbt Labs' [example project](https://github.com/dbt-labs/example-semantic-layer-clients) for reference.
 - If you’re a partner or user building a homegrown application, you’ll need to install an AWS root CA to the Java Trust [documentation](https://www.amazontrust.com/repository/) (specific to Java and JDBC call).
 
+Partners of dbt Labs can use the JDBC API to build integrations in their tools with the dbt Semantic Layer
+
+
+## Using the JDBC API
+
+If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document. (PROVIDE link to set up?). 
+
 ## Connection parameters
 
-The JDBC connection requires a few different connection parameters. We provide the full JDBC string that you can connect with as well as the individual components required.
+The JDBC connection requires a few different connection parameters. 
 
 This is an example of a URL connection string and the individual components: 
 
@@ -43,6 +50,7 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 
 *Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable at the end or beginning of the JDBC URL `&disableCertificateVerification=true`.
 
+
 ## Querying the API for metric metadata
 
 The Semantic Layer JDBC API has built-in metadata calls which can provide a user with information about their metrics and dimensions. Here are some metadata commands and examples:

From dd5c6092bda4671aa2c34c1a651c7edb3453f7f4 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 18 Jul 2023 15:18:52 -0700
Subject: [PATCH 171/703] Update sl-graphql.md

adding the same note for GQL
---
 website/docs/docs/dbt-cloud-apis/sl-graphql.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 4c29838bf0d..1d9eb08b769 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -28,7 +28,7 @@ The dbt Semantic Layer GraphQL API allows you to explore and query metrics and d
 
 ## Using the GraphQL API
 
-If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API in your oown instance by configuring the Semantic Layer and obtaining the right GQL onnection parameters provided. (PROVIDE link to set up?) 
+If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. (PROVIDE link to set up?). 
 
 ### Authentication 
 

From 1991ca0bf45c956a88481c345eff6a0acec40fa8 Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Wed, 19 Jul 2023 01:04:19 +0200
Subject: [PATCH 172/703] Revise v1.6 migration guide (#3728)

resolves #3696
resolves #3723

Revise v1.6 migration guide for readability. Still a handful of TODOs.

### Materialized views
- Add `VersionBlock` components for adapter MVs, missed in
https://github.com/dbt-labs/docs.getdbt.com/pull/3593
- We seem to be missing docs on Snowflake dynamic tables. I don't know
why https://github.com/dbt-labs/docs.getdbt.com/issues/3494 was closed.

### Other updates
- Add manifest v10 (will need addition to schemas.getdbt.com:
https://github.com/dbt-labs/schemas.getdbt.com/issues/24)
- Add note to upgrade guide for `state:old` and `state:unmodified`
(missed in https://github.com/dbt-labs/docs.getdbt.com/pull/3596). I
haven't done a thorough comb-through of other closed v1.6 PRs to see
what needs addition to the migration guide.
---
 website/dbt-versions.js                       |  2 +-
 .../govern/project-dependencies.md            |  4 +-
 .../versions/01-upgrading-to-v1.6.md          | 64 +++++++++++++++----
 .../docs/reference/artifacts/dbt-artifacts.md |  1 +
 .../docs/reference/artifacts/manifest-json.md | 57 ++++-------------
 website/docs/reference/commands/cmd-docs.md   | 21 ++++--
 .../docs/reference/node-selection/defer.md    |  8 ++-
 .../docs/reference/node-selection/syntax.md   | 14 +++-
 8 files changed, 100 insertions(+), 71 deletions(-)

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 1c6cb52dad1..01d1bf5d128 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -1,7 +1,7 @@
 exports.versions = [
   {
     version: "1.6",
-    EOLDate: "2024-07-20", // placeholder - need to confirm the final date
+    EOLDate: "2024-07-31",
     isPrerelease: true
   },
   {
diff --git a/website/docs/docs/collaborate/govern/project-dependencies.md b/website/docs/docs/collaborate/govern/project-dependencies.md
index 3469ec1a7b6..158c405e4a7 100644
--- a/website/docs/docs/collaborate/govern/project-dependencies.md
+++ b/website/docs/docs/collaborate/govern/project-dependencies.md
@@ -5,8 +5,8 @@ sidebar_label: "Project dependencies"
 description: "Reference public models across dbt projects"
 ---
 
-:::info
-"Project" dependencies and cross-project `ref` is currently in closed beta and are features of dbt Cloud Enterprise. To access these features, please contact your account team.
+:::caution Closed Beta - dbt Cloud Enterprise
+"Project" dependencies and cross-project `ref` are features of dbt Cloud Enterprise, currently in Closed Beta. To access these features while they are in beta, please contact your account team at dbt Labs.
 :::
 
 For a long time, dbt has supported code reuse and extension by installing other projects as [packages](/docs/build/packages). When you install another project as a package, you are pulling in its full source code, and adding it to your own. This enables you to call macros and run models defined in that other project.
diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index 52850239a53..cb1e9af603d 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -1,17 +1,19 @@
 ---
-title: "Upgrading to v1.6 (beta)"
+title: "Upgrading to v1.6 (prerelease)"
 description: New features and changes in dbt Core v1.6
 ---
 
-:::warning Beta Functionality
+:::warning Prerelease
 
-dbt Core v1.6 is in beta, and the features and functionality on this page are subject to change.
+dbt Core v1.6 is available as a release candidate. [Final release is planned for July 31.](https://github.com/dbt-labs/dbt-core/issues/7990)
+
+Test it out, and [let us know](https://github.com/dbt-labs/dbt-core/issues/new/choose) if you run into any issues!
 
 :::
 
 ## Resources
 
-- [Changelog](https://github.com/dbt-labs/dbt-core/blob/main/CHANGELOG.md)
+- [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.6.latest/CHANGELOG.md)
 - [CLI Installation guide](/docs/core/installation)
 - [Cloud upgrade guide](/docs/dbt-versions/upgrade-core-in-cloud)
 - [Release schedule](https://github.com/dbt-labs/dbt-core/issues/7481)
@@ -22,24 +24,58 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 
 ### Behavior changes
 
-**Coming soon**
+- dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
+- As part of the Semantic layer re-launch (in beta), the spec for `metrics` has changed significantly. Migration guide coming soon: https://github.com/dbt-labs/docs.getdbt.com/pull/3705
+- Manifest schema version is now v10, reflecting [TODO] changes
+
+### For consumers of dbt artifacts (metadata)
+
+The [manifest](/reference/artifacts/manifest-json) schema version has updated to `v10`. Specific changes:
+- Addition of `semantic_models` and changes to `metrics` attributes
+- Addition of `deprecation_date` as a model property
+- Addition of `on_configuration_change` as default node configuration (to support materialized views)
+- Small type changes to `contracts` and `constraints`
+- Manifest `metadata` includes `project_name`
+
+### For maintainers of adapter plugins
 
+For more detailed information and to ask questions, please read and comment on the GH discussion: [dbt-labs/dbt-core#7958](https://github.com/dbt-labs/dbt-core/discussions/7958).
 
 ## New and changed documentation
 
-[`dbt retry`](/reference/commands/retry) is a new command that executes the previously run command from the point of failure. This convenient command enables you to continue a failed command without rebuilding all upstream dependencies. 
+### Materialized views
 
-**Materialized view** support (for model and project configs) has been added for three data warehouses:
-    - [Bigquery](/reference/resource-configs/bigquery-configs#materialized-view)
-    - [Postgres](/reference/resource-configs/postgres-configs#materialized-view)
-    - [Redshift](/reference/resource-configs/redshift-configs#materialized-view)
+Supported on:
+- [Postgres](/reference/resource-configs/postgres-configs#materialized-view)
+- [Redshift](/reference/resource-configs/redshift-configs#materialized-view)
+- Snowflake (docs forthcoming)
 
-[**Namespacing:**](/faqs/Models/unique-model-names) Model names can be duplicated across different namespaces (packages/projects), so long as they are unique within each package/project. We strongly encourage using [two-argument `ref`](/reference/dbt-jinja-functions/ref#two-argument-variant) when referencing a model from a different package/project.
+Support for BigQuery and Databricks forthcoming.
 
-[**Project dependencies**](/docs/collaborate/govern/project-dependencies): Introduces `dependencies.yml` and dependent `projects` as a feature of dbt Cloud Enterprise. Allows enforcing model access (public vs. protected/private) across project/package boundaries. Enables cross-project `ref` of public models, without requiring the installation of upstream source code.
+### New commands for mature deployment
 
-### Quick hits
+[`dbt retry`](/reference/commands/retry) executes the previously run command from the point of failure. Rebuild just the nodes that errored or skipped in a previous run/build/test, rather than starting over from scratch.
+
+[`dbt clone`](/reference/commands/clone) leverages each data platform's functionality for creating lightweight copies of dbt models from one environment into another. Useful when quickly spinning up a new development environment, or promoting specific models from a staging environment into production.
+
+### Multi-project collaboration
 
-More consistency and flexibility around packages! Resources defined in a package will respect variable and global macro definitions within the scope of that package.
+[**Deprecation date**](/reference/resource-properties/deprecation_date): Models can declare a deprecation date that will warn model producers and downstream consumers. This enables clear migration windows for versioned models, and provides a mechanism to facilitate removal of immature or little-used models, helping to avoid project bloat.
+
+[Model names](/faqs/Models/unique-model-names) can be duplicated across different namespaces (projects/packages), so long as they are unique within each project/package. We strongly encourage using [two-argument `ref`](/reference/dbt-jinja-functions/ref#two-argument-variant) when referencing a model from a different package/project.
+
+More consistency and flexibility around packages. Resources defined in a package will respect variable and global macro definitions within the scope of that package.
 - `vars` defined in a package's `dbt_project.yml` are now available in the resolution order when compiling nodes in that package, though CLI `--vars` and the root project's `vars` will still take precedence. See ["Variable Precedence"](/docs/build/project-variables#variable-precedence) for details.
 - `generate_x_name` macros (defining custom rules for database, schema, alias naming) follow the same pattern as other "global" macros for package-scoped overrides. See [macro dispatch](/reference/dbt-jinja-functions/dispatch) for an overview of the patterns that are possible.
+
+:::caution Closed Beta - dbt Cloud Enterprise
+[**Project dependencies**](/docs/collaborate/govern/project-dependencies): Introduces `dependencies.yml` and dependent `projects` as a feature of dbt Cloud Enterprise. Allows enforcing model access (public vs. protected/private) across project/package boundaries. Enables cross-project `ref` of public models, without requiring the installation of upstream source code.
+:::
+
+### Quick hits
+
+- [`state:unmodified` and `state:old`](/reference/node-selection/methods#the-state-method) for [MECE](https://en.wikipedia.org/wiki/MECE_principle) stateful selection
+- [`invocation_args_dict`](/reference/dbt-jinja-functions/flags#invocation_args_dict) includes full `invocation_command` as string
+- [`dbt debug --connection`](/reference/commands/debug) to test just the data platform connection specified in a profile
+- [`dbt docs generate --empty-catalog`](/reference/commands/cmd-docs) to skip catalog population while generating docs
+- [`--defer-state`](/reference/node-selection/defer) enables more-granular control 
diff --git a/website/docs/reference/artifacts/dbt-artifacts.md b/website/docs/reference/artifacts/dbt-artifacts.md
index 2fbcc329484..b20c1548d99 100644
--- a/website/docs/reference/artifacts/dbt-artifacts.md
+++ b/website/docs/reference/artifacts/dbt-artifacts.md
@@ -39,6 +39,7 @@ All artifacts produced by dbt include a `metadata` dictionary with these propert
 
 In the manifest, the `metadata` may also include:
 - `send_anonymous_usage_stats`: Whether this invocation sent [anonymous usage statistics](/reference/global-configs/usage-stats) while executing.
+- `project_name`: The `name` defined in the root project's `dbt_project.yml`. (Added in manifest v10 / dbt Core v1.6)
 - `project_id`: Project identifier, hashed from `project_name`, sent with anonymous usage stats if enabled.
 - `user_id`: User identifier, stored by default in `~/dbt/.user.yml`, sent with anonymous usage stats if enabled.
 
diff --git a/website/docs/reference/artifacts/manifest-json.md b/website/docs/reference/artifacts/manifest-json.md
index c71c073d842..3a916ed6d4c 100644
--- a/website/docs/reference/artifacts/manifest-json.md
+++ b/website/docs/reference/artifacts/manifest-json.md
@@ -2,53 +2,18 @@
 title: "Manifest JSON file"
 sidebar_label: "Manifest"
 ---
-<VersionBlock firstVersion="1.5">
 
-**dbt Core v1.5 produces schema**: [`v9`](https://schemas.getdbt.com/dbt/manifest/v9/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.4" lastVersion="1.4">
-
-**dbt Core v1.4 produces schema**: [`v8`](https://schemas.getdbt.com/dbt/manifest/v8/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.3" lastVersion="1.3">
-
-**dbt Core v1.3 produces schema**: [`v7`](https://schemas.getdbt.com/dbt/manifest/v7/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.2" lastVersion="1.2">
-
-**dbt Core v1.2 produces schema**: [`v6`](https://schemas.getdbt.com/dbt/manifest/v6/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.1" lastVersion="1.1">
-
-**dbt Core v1.1 produces schema**: [`v5`](https://schemas.getdbt.com/dbt/manifest/v5/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.0" lastVersion="1.0">
-
-**dbt Core v1.0 produces schema**: [`v4`](https://schemas.getdbt.com/dbt/manifest/v4/index.html)
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.5">
-
-**Produced by:** [`build`](/reference/commands/build) [`compile`](/reference/commands/compile) [`docs generate`](/reference/commands/cmd-docs) [`list`](/reference/commands/list) [`seed`](/reference/commands/seed) [`snapshot`](/reference/commands/snapshot) [`source freshness`](/reference/commands/source) [`test`](/reference/commands/test) [`run`](/reference/commands/run) [`run-operation`](/reference/commands/run-operation)
-
-</VersionBlock>
-
-<VersionBlock lastVersion="1.4">
-
-**Produced by:** [`build`](commands/build) [`compile`](commands/compile) [`docs generate`](commands/cmd-docs) [`list`](commands/list) [`parse`](commands/parse) [`run`](commands/run) [`run-operation`](commands/run-operation) [`seed`](commands/seed) [`show`](commands/show) [`snapshot`](commands/snapshot) [`source freshness`](commands/source) [`test`](commands/test) 
-
-</VersionBlock>
+| dbt Core version | Manifest version                                              |
+|------------------|---------------------------------------------------------------|
+| v1.6             | [v10](https://schemas.getdbt.com/dbt/manifest/v10/index.html) |
+| v1.5             | [v9](https://schemas.getdbt.com/dbt/manifest/v9/index.html)   |
+| v1.4             | [v8](https://schemas.getdbt.com/dbt/manifest/v8/index.html)   |
+| v1.3             | [v7](https://schemas.getdbt.com/dbt/manifest/v7/index.html)   |
+| v1.2             | [v6](https://schemas.getdbt.com/dbt/manifest/v6/index.html)   |
+| v1.1             | [v5](https://schemas.getdbt.com/dbt/manifest/v5/index.html)   |
+| v1.0             | [v4](https://schemas.getdbt.com/dbt/manifest/v4/index.html)   |
+
+**Produced by:** Any command that parses your project. This includes all commands **except** [`deps`](/reference/commands/deps), [`clean`](/reference/commands/clean), [`debug`](/reference/commands/debug), [`init`](/reference/commands/init)
 
 This single file contains a full representation of your dbt project's resources (models, tests, macros, etc), including all node configurations and resource properties. Even if you're only running some models or tests, all resources will appear in the manifest (unless they are disabled) with most of their properties. (A few node properties, such as `compiled_sql`, only appear for executed nodes.)
 
diff --git a/website/docs/reference/commands/cmd-docs.md b/website/docs/reference/commands/cmd-docs.md
index e96f825d2d9..754c5e93baf 100644
--- a/website/docs/reference/commands/cmd-docs.md
+++ b/website/docs/reference/commands/cmd-docs.md
@@ -10,22 +10,35 @@ id: "cmd-docs"
 
 The command is responsible for generating your project's documentation website by
 
-1. copying the website `index.html` file into the `target/` directory 
-2. compiling the project to `target/manifest.json`
-3. producing the `target/catalog.json` file, which contains metadata about the tables and <Term id="view">views</Term> produced by the models in your project.
+1. Copying the website `index.html` file into the `target/` directory 
+2. Compiling the resources in your project, so that their `compiled_code` will be included in [`manifest.json`](/reference/artifacts/manifest-json)
+3. Running queries against database metadata to produce the [`catalog.json`](/reference/artifacts/catalog-json) file, which contains metadata about the tables and <Term id="view">views</Term> produced by the models in your project.
 
 **Example**:
 ```
 dbt docs generate
 ```
 
-Use the `--no-compile` argument to skip re-compilation. When this flag is provided, `dbt docs generate` will only execute steps (1) and (3), as described above.
+Use the `--no-compile` argument to skip re-compilation. When this flag is provided, `dbt docs generate` will skip step (2) described above.
 
 **Example**:
 ```
 dbt docs generate --no-compile
 ```
 
+<VersionBlock firstVersion="1.6">
+
+Use the `--empty-catalog` argument to skip running the database queries to populate `catalog.json`. When this flag is provided, `dbt docs generate` will skip step (3) described above.
+
+This is not recommended for production environments, as it means that your documentation will be missing information gleaned from database metadata (the full set of columns in each table, and statistics about those tables). It can speed up `docs generate` in development, when you just want to visualize lineage and other information defined within your project.
+
+**Example**:
+```
+dbt docs generate --empty-catalog
+```
+
+</VersionBlock>
+
 ### dbt docs serve
 This command starts a webserver on port 8080 to serve your documentation locally and opens the documentation site in your default browser. The webserver is rooted in your `target/` directory. Be sure to run `dbt docs generate` before `dbt docs serve` because the  `generate` command produces a [catalog metadata artifact](/reference/artifacts/catalog-json) that the `serve` command depends upon. You will see an error message if the catalog is missing.  
 
diff --git a/website/docs/reference/node-selection/defer.md b/website/docs/reference/node-selection/defer.md
index e33f95a5142..6079e53793a 100644
--- a/website/docs/reference/node-selection/defer.md
+++ b/website/docs/reference/node-selection/defer.md
@@ -9,14 +9,18 @@ title: "Defer"
 
 </Changelog>
 
-Deferral is a powerful, complex feature that enables compelling workflows. As the use cases for `--defer` evolve, dbt Labs might make enhancements to the feature, but commit to providing backward compatibility for supported versions of dbt Core.  For details, see [dbt#5095](https://github.com/dbt-labs/dbt-core/discussions/5095).
-
 Defer is a powerful feature that makes it possible to run a subset of models or tests in a [sandbox environment](/docs/environments-in-dbt) without having to first build their upstream parents. This can save time and computational resources when you want to test a small number of models in a large project.
 
 Defer requires that a manifest from a previous dbt invocation be passed to the `--state` flag or env var. Together with the `state:` selection method, these features enable "Slim CI". Read more about [state](/reference/node-selection/syntax#about-node-selection).
 
 An alternative command that accomplishes similar functionality for different use cases is `dbt clone` - see the docs for [clone](/reference/commands/clone#when-to-use-dbt-clone-instead-of-deferral) for more information.
 
+<VersionBlock firstVersion="1.6">
+
+It is possible to use separate state for `state:modified` and `--defer`, by passing paths to different manifests to each of the `--state`/`DBT_STATE` and `--defer-state`/`DBT_DEFER_STATE`. This enables more granular control in cases where you want to compare against logical state from one environment or past point in time, and defer to applied state from a different environment or point in time. If `--defer-state` is not specified, deferral will use the manifest supplied to `--state`. In most cases, you will want to use the same state for both: compare logical changes against production, and also "fail over" to the production environment for unbuilt upstream resources.
+
+</VersionBlock>
+
 ### Usage
 
 ```shell
diff --git a/website/docs/reference/node-selection/syntax.md b/website/docs/reference/node-selection/syntax.md
index 36e3f03c422..1a43a32e2bc 100644
--- a/website/docs/reference/node-selection/syntax.md
+++ b/website/docs/reference/node-selection/syntax.md
@@ -81,7 +81,7 @@ Note that when you're using `--selector`, most other flags (namely `--select` an
 <Snippet path="discourse-help-feed-header" />
 <DiscourseHelpFeed tags="node-selection"/>
 
-## About node selection 
+## Stateful selection
 
 One of the greatest underlying assumptions about dbt is that its operations should be **stateless** and **<Term id="idempotent" />**. That is, it doesn't matter how many times a model has been run before, or if it has ever been run before. It doesn't matter if you run it once or a thousand times. Given the same raw data, you can expect the same transformed result. A given run of dbt doesn't need to "know" about _any other_ run; it just needs to know about the code in the project and the objects in your database as they exist _right now_.
 
@@ -106,7 +106,7 @@ State and defer can be set by environment variables as well as CLI flags:
 
 </VersionBlock>
 
-<VersionBlock firstVersion="1.5">
+<VersionBlock firstVersion="1.5" lastVersion="1.6">
 
 - `--state` or `DBT_STATE`: file path
 - `--defer` or `DBT_DEFER`: boolean
@@ -119,6 +119,16 @@ In dbt v1.5, we deprecated the original syntax for state (`DBT_ARTIFACT_STATE_PA
 
 </VersionBlock>
 
+<VersionBlock firstVersion="1.6">
+
+- `--state` or `DBT_STATE`: file path
+- `--defer` or `DBT_DEFER`: boolean
+- `--defer-state` or `DBT_DEFER_STATE`: file path to use for deferral only (optional)
+
+If `--defer-state` is not specified, deferral will use the artifacts supplied by `--state`. This enables more granular control in cases where you want to compare against logical state from one environment or past point in time, and defer to applied state from a different environment or point in time.
+
+</VersionBlock>
+
 If both the flag and env var are provided, the flag takes precedence.
 
 #### Notes:

From f05cf490b24d4fa94cef85fe19561d760485086c Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Tue, 18 Jul 2023 19:50:57 -0700
Subject: [PATCH 173/703] add cli docs

---
 website/docs/docs/build/metricflow-cli.md | 247 ++++++++++++++++++++++
 1 file changed, 247 insertions(+)
 create mode 100644 website/docs/docs/build/metricflow-cli.md

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
new file mode 100644
index 00000000000..36abd524c8d
--- /dev/null
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -0,0 +1,247 @@
+---
+title: MetricFlow CLI
+id: metricflow-cli
+description: "Query metrics and metadata in your dbt project with the metricflow cli"
+sidebar_label: "CLI"
+tags: [Metrics, Semantic Layer]
+---
+
+After you have defined metrics in your dbt project, you can easily query metrics, dimensions, dimension values, and validate your configs using the MetricFlow CLI.
+
+## Installation
+1. Create or activate your virtual environment.`python -m venv venv`
+2. Run `pip install dbt-metricflow`
+
+## CLI commands
+
+## List
+Retrieve metadata values about metrics/dimensions/entities/dimension values
+
+# List metrics
+List the metrics with their available dimensions
+```
+mf list metrics <metric_name>
+Options:
+  --search TEXT          Filter available metrics by this search term
+  --show-all-dimensions  Show all dimensions associated with a metric.
+  --help                 Show this message and exit.
+```
+
+# List dimensions
+List all unique dimensions for a metric or multiple metrics. Only common dimensions are shown if querying multiple metrics
+```
+mf list dimensions --metrics <metric_name>
+Options:
+  --metrics SEQUENCE  List dimensions by given metrics (intersection). Ex.
+                      --metrics bookings,messages
+  --help              Show this message and exit.
+```
+
+# List dimension-values
+List all dimension values with the corresponding metric
+```
+mf list dimension-values --metrics <metric_name> --dimension <dimension_name>
+Options:
+  --dimension TEXT    Dimension to query values from  [required]
+  --metrics SEQUENCE  Metrics that are associated with the dimension
+                      [required]
+  --end-time TEXT     Optional iso8601 timestamp to constraint the end time of
+                      the data (inclusive)
+  --start-time TEXT   Optional iso8601 timestamp to constraint the start time
+                      of the data (inclusive)
+  --help              Show this message and exit.
+```
+# List entities
+List all unique entities.
+```
+mf list entities --metrics <metric_name>
+Options:
+  --metrics SEQUENCE  List entities by given metrics (intersection). Ex.
+                      --metrics bookings,messages
+  --help              Show this message and exit.
+```
+
+## Validate configs
+ Perform validations against the defined semantic model configurations.
+```
+mf validate-configs
+Options:
+  --dw-timeout INTEGER            Optional timeout for data warehouse
+                                  validation steps. Default None.
+  --skip-dw                       If specified, skips the data warehouse
+                                  validations
+  --show-all                      If specified, prints warnings and future-
+                                  errors
+  --verbose-issues                If specified, prints any extra details
+                                  issues might have
+  --semantic-validation-workers INTEGER
+                                  Optional. Uses the number of workers
+                                  specified to run the semantic validations.
+                                  Should only be used for exceptionally large
+                                  configs
+  --help                          Show this message and exit.
+```
+
+## Health checks
+Performs a health check against the DW provided in the configs.
+```
+mf health-checks
+```
+
+## Tutorial
+Runs through a getting started tutorial. 
+
+```
+mf tutorial
+```
+
+## Query
+Create a new query with MetricFlow, execute that query agaisnt the users data platform and return the result. 
+```
+mf query --metrics <metric_name> --group-by <dimension_name>
+Options:
+  --metrics SEQUENCE       Metrics to query for: syntax is --metrics bookings
+                           or for multiple metrics --metrics bookings,messages
+  --group-by SEQUENCE      Dimensions and/or entities to group by: syntax is
+                           --group-by ds or for multiple group bys --group-by
+                           ds,org
+  --end-time TEXT          Optional iso8601 timestamp to constraint the end
+                           time of the data (inclusive)
+  --start-time TEXT        Optional iso8601 timestamp to constraint the start
+                           time of the data (inclusive)
+  --where TEXT             SQL-like where statement provided as a string. For
+                           example: --where "revenue > 100"
+  --limit TEXT             Limit the number of rows out using an int or leave
+                           blank for no limit. For example: --limit 100
+  --order SEQUENCE         Metrics or group bys to order by ("-" prefix for
+                           DESC). For example: --order -ds or --order
+                           ds,-revenue
+  --csv FILENAME           Provide filepath for dataframe output to csv
+  --explain                In the query output, show the query that was
+                           executed against the data warehouse
+  --show-dataflow-plan     Display dataflow plan in explain output
+  --display-plans          Display plans (e.g. metric dataflow) in the browser
+  --decimals INTEGER       Choose the number of decimal places to round for
+                           the numerical values
+  --show-sql-descriptions  Shows inline descriptions of nodes in displayed SQL
+  --help                   Show this message and exit.
+  ```
+
+
+## Query Examples
+
+Query metrics by dimensions.
+
+The following query will retrun the order_amount metric by metric time. 
+```
+mf query --metrics order_amount --group-by metric_time
+✔ Success 🦄 - query completed after 1.24 seconds
+| METRIC_TIME   |   ORDER_AMOUNT |
+|:--------------|---------------:|
+| 2017-06-16    |         792.17 |
+| 2017-06-17    |         458.35 |
+| 2017-06-18    |         490.69 |
+| 2017-06-19    |         749.09 |
+| 2017-06-20    |         712.51 |
+| 2017-06-21    |         541.65 |
+```
+
+You can include multiple dimensions in a query. For example, we can also group by the `is_food_order` dimension to see if orders we're for food or not. 
+
+```
+mf query --metrics order_amount --group-by metric_time, is_food_order
+ Success 🦄 - query completed after 1.70 seconds
+| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
+|:--------------|:----------------|---------------:|
+| 2017-06-16    | True            |         499.27 |
+| 2017-06-16    | False           |         292.90 |
+| 2017-06-17    | True            |         431.24 |
+| 2017-06-17    | False           |          27.11 |
+| 2017-06-18    | True            |         466.45 |
+| 2017-06-18    | False           |          24.24 |
+| 2017-06-19    | False           |         300.98 |
+| 2017-06-19    | True            |         448.11 |
+```
+
+You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records, and orders by metric time descending. 
+
+```
+mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time
+✔ Success 🦄 - query completed after 1.41 seconds
+| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
+|:--------------|:----------------|---------------:|
+| 2017-08-31    | True            |         459.90 |
+| 2017-08-31    | False           |         327.08 |
+| 2017-08-30    | False           |         348.90 |
+| 2017-08-30    | True            |         448.18 |
+| 2017-08-29    | True            |         479.94 |
+| 2017-08-29    | False           |         333.65 |
+| 2017-08-28    | False           |         334.73 |
+```
+
+You can futher filter the data set by adding a where clause to your query.
+
+```
+ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True"
+ ✔ Success 🦄 - query completed after 1.06 seconds
+| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
+|:--------------|:----------------|---------------:|
+| 2017-08-31    | True            |         459.90 |
+| 2017-08-30    | True            |         448.18 |
+| 2017-08-29    | True            |         479.94 |
+| 2017-08-28    | True            |         513.48 |
+| 2017-08-27    | True            |         568.92 |
+| 2017-08-26    | True            |         471.95 |
+| 2017-08-25    | True            |         452.93 |
+| 2017-08-24    | True            |         384.40 |
+| 2017-08-23    | True            |         423.61 |
+| 2017-08-22    | True            |         401.91 |
+```
+
+To filter by time, there are dedicated start and end time options. Using these options to filter by time allows MetricFlow to further optimize query performance by pushing down the where filter when appropriate. 
+
+```
+ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27'
+✔ Success 🦄 - query completed after 1.53 seconds
+| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
+|:--------------|:----------------|---------------:|
+| 2017-08-27    | True            |         568.92 |
+| 2017-08-26    | True            |         471.95 |
+| 2017-08-25    | True            |         452.93 |
+| 2017-08-24    | True            |         384.40 |
+| 2017-08-23    | True            |         423.61 |
+| 2017-08-22    | True            |         401.91 |
+```
+
+You can see the SQL metricflow is generating by adding --explain to your query. 
+
+```
+ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --explain
+ ✔ Success 🦄 - query completed after 0.28 seconds
+🔎 SQL (remove --explain to see data or add --show-dataflow-plan to see the generated dataflow plan):
+SELECT
+  metric_time
+  , is_food_order
+  , SUM(order_cost) AS order_amount
+FROM (
+  SELECT
+    cast(ordered_at as date) AS metric_time
+    , is_food_order
+    , order_cost
+  FROM ANALYTICS.js_dbt_sl_demo.orders orders_src_1
+  WHERE cast(ordered_at as date) BETWEEN CAST('2017-08-22' AS TIMESTAMP) AND CAST('2017-08-27' AS TIMESTAMP)
+) subq_3
+WHERE is_food_order = True
+GROUP BY
+  metric_time
+  , is_food_order
+ORDER BY metric_time DESC
+LIMIT 10
+```
+To export the results of your query to a csv, simply add the `--csv file_name.csv` flag.
+
+```
+mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --csv query_example.csv
+✔ Success 🦄 - query completed after 0.83 seconds
+🖨 Successfully written query output to query_example.csv
+```
\ No newline at end of file

From bc686db975ccb077fe59d4a7bdf6665c5363a9c8 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Tue, 18 Jul 2023 20:07:03 -0700
Subject: [PATCH 174/703] add spec for metrics and measures

---
 website/docs/docs/build/measures.md         | 20 ++++++++++++++++++++
 website/docs/docs/build/metrics-overview.md | 14 ++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index f85d21e3dcb..e9922bda8a9 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -15,6 +15,26 @@ Measures are aggregations performed on columns in your model. They can be used a
 | [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `count_distinct`, and `sum_boolean`. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
+|[agg_params]| specific aggregation properties such as a percentile. | [Optional]|
+|[agg_time_dimension]| The time field. Defaults to the default agg time dimension for the semantic model.  | [Optional] |
+|[non_additive_dimension]| configs used if needing non-additive dimensions | [Optional]|
+|[label]| How the metric will be displayed in docs and downstream integrations. | [Required]|
+
+
+## Measyres Spec
+An example of the complete yaml measures spec is below. The actual configuration of your measures will depend on the aggregation you're using.
+
+```
+measures:
+      - name: The name of the measure # think transaction_total. If `expr` not present then this is the expected name of the column [Required]
+        description: same as always [Optional]
+        agg: the aggregation type.  #think average, sum, max, min, etc.[Required]
+        expr: the field # think transaction_total or some other name you might want to alias [Optional]
+        agg_params: specific aggregation properties such as a percentile [Optional]
+        agg_time_dimension: The time field. Defaults to the default agg time dimension for the semantic model. [Optional]
+        non_additive_dimension: configs used if needing non-additive dimensions [Optional]
+        label: How the metric will be displayed in docs and downstream integrations. [Required]
+`
 
 ### Name 
 
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index e7271ecf417..7658926cb19 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -16,6 +16,20 @@ The keys for metrics definitions are:
 * `constraint`: For any type of metric, you may optionally include a constraint string, which applies a dimensional filter when computing the metric. You may think of this as your WHERE clause.  
 * `meta`: Additional metadata you want to add to your metric. 
 
+An example of the complete metrics spec configuration is below:
+```
+metrics:
+  - name: metric name
+    description: same as always
+    type: the type of the metric
+    type_params:
+        - specific properties for the metric type
+    configs: here for `enabled`
+    label: The display name for your metric. This value will be shown in downstream tools.
+    filter: |
+      {{  dimension('name') }} > 0 and {{ dimension(' another name') }} is not null
+
+```
 This page explains the different supported metric types you can add to your dbt project. 
 <!--
 - [Cumulative](#cumulative-metrics) — Cumulative metrics aggregate a measure over a given window.

From 1f867b755f9eba03af553f1c7a4bd17e0b4a0da6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 08:59:26 +0100
Subject: [PATCH 175/703] Update clone.md

fix typo
---
 website/docs/reference/commands/clone.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/clone.md b/website/docs/reference/commands/clone.md
index 3f74d7cd88a..32c8a89be04 100644
--- a/website/docs/reference/commands/clone.md
+++ b/website/docs/reference/commands/clone.md
@@ -23,7 +23,7 @@ dbt clone --state path/to/artifacts
 # clone one_specific_model of my models from specified state to my target schema(s)
 dbt clone --select one_specific_model --state path/to/artifacts
 
-# clone all of my models from specified state to my target schema(s) and recreate all pre-exisiting relations in the current target
+# clone all of my models from specified state to my target schema(s) and recreate all pre-existing relations in the current target
 dbt clone --state path/to/artifacts --full-refresh
 
 # clone all of my models from specified state to my target schema(s), running up to 50 clone statements in parallel
@@ -36,4 +36,4 @@ Unlike deferral, `dbt clone` requires some compute and creation of additional ob
 
 For example, by creating actual data warehouse objects, `dbt clone` allows you to test out your code changes on downstream dependencies _outside of dbt_ (such as a BI tool). 
 
-As another example, you could `clone` your modified incremental models as the first step of your dbt Cloud CI job to prevent costly `full-refresh` builds for warehouses that support zero-copy cloning.
\ No newline at end of file
+As another example, you could `clone` your modified incremental models as the first step of your dbt Cloud CI job to prevent costly `full-refresh` builds for warehouses that support zero-copy cloning.

From a9b2505ff27cc22c7f8b6c6ea9a4282551f13333 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 09:13:28 +0100
Subject: [PATCH 176/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index b7dddf9fd11..36960c36742 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -24,6 +24,6 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 ### Browser sessions
 
-A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, if you close your browser, this action ends your dbt Cloud session and logs you out. You'll need to re-login the next time you try to access dbt Cloud. 
+A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, if you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
 
 If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which may vary depending on your identity provider (IdP).

From 45d2c19195b8bc7e1dbd8233b91b9a1c0ae963c6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 09:13:47 +0100
Subject: [PATCH 177/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index 36960c36742..4aa1e273f33 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -26,4 +26,4 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, if you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
 
-If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which may vary depending on your identity provider (IdP).
+If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which might vary depending on your identity provider (IdP).

From 6370f348d553ea659ef1bf3bfdfbab589e20c69c Mon Sep 17 00:00:00 2001
From: Ryan Hill <rlh1994@users.noreply.github.com>
Date: Wed, 19 Jul 2023 11:04:42 +0100
Subject: [PATCH 178/703] Update snowflake-permissions.md

---
 website/docs/reference/snowflake-permissions.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/reference/snowflake-permissions.md b/website/docs/reference/snowflake-permissions.md
index 80dbec25cc8..6a469d12230 100644
--- a/website/docs/reference/snowflake-permissions.md
+++ b/website/docs/reference/snowflake-permissions.md
@@ -15,9 +15,11 @@ grant usage on schema database.an_existing_schema to role role_name;
 grant create table on schema database.an_existing_schema to role role_name;
 grant create view on schema database.an_existing_schema to role role_name;
 grant usage on future schemas in database database_name to role role_name;
+grant monitor on future schemas in database database_name to role role_name;
 grant select on future tables in database database_name to role role_name;
 grant select on future views in database database_name to role role_name;
 grant usage on all schemas in database database_name to role role_name;
+grant monitor on all schemas in database database_name to role role_name;
 grant select on all tables in database database_name to role role_name;
 grant select on all views in database database_name to role role_name;
 ```

From 8919e39457087acf646488b29fb9edc66a789cc5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 11:54:39 +0100
Subject: [PATCH 179/703] Update website/docs/docs/build/metrics-overview.md

---
 website/docs/docs/build/metrics-overview.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 7658926cb19..b4495ecdfd5 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -16,7 +16,8 @@ The keys for metrics definitions are:
 * `constraint`: For any type of metric, you may optionally include a constraint string, which applies a dimensional filter when computing the metric. You may think of this as your WHERE clause.  
 * `meta`: Additional metadata you want to add to your metric. 
 
-An example of the complete metrics spec configuration is below:
+Here's a complete example of the metrics spec configuration:
+
 ```
 metrics:
   - name: metric name

From 7c148bf8f4b909b0804977eecb146ce1e1a6c006 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 11:59:47 +0100
Subject: [PATCH 180/703] Update measures.md

active voice and fix typos
---
 website/docs/docs/build/measures.md | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index e9922bda8a9..3483e4d823d 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -15,26 +15,27 @@ Measures are aggregations performed on columns in your model. They can be used a
 | [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `count_distinct`, and `sum_boolean`. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
-|[agg_params]| specific aggregation properties such as a percentile. | [Optional]|
-|[agg_time_dimension]| The time field. Defaults to the default agg time dimension for the semantic model.  | [Optional] |
-|[non_additive_dimension]| configs used if needing non-additive dimensions | [Optional]|
-|[label]| How the metric will be displayed in docs and downstream integrations. | [Required]|
+| [`agg_params`] | specific aggregation properties such as a percentile. | [Optional]|
+| [`agg_time_dimension`] | The time field. Defaults to the default agg time dimension for the semantic model.  | [Optional] |
+| [`non_additive_dimension`] | Use these configs when you need non-additive dimensions. | [Optional]|
+| [`label`] | How the metric appears in project docs and downstream integrations. | [Required]|
 
 
-## Measyres Spec
-An example of the complete yaml measures spec is below. The actual configuration of your measures will depend on the aggregation you're using.
+## Measure spec
 
-```
+An example of the complete YAML measures spec is below. The actual configuration of your measures will depend on the aggregation you're using.
+
+```bash
 measures:
-      - name: The name of the measure # think transaction_total. If `expr` not present then this is the expected name of the column [Required]
+      - name: The name of the measure # think transaction_total. If `expr` isn't present then this is the expected name of the column [Required]
         description: same as always [Optional]
         agg: the aggregation type.  #think average, sum, max, min, etc.[Required]
         expr: the field # think transaction_total or some other name you might want to alias [Optional]
         agg_params: specific aggregation properties such as a percentile [Optional]
         agg_time_dimension: The time field. Defaults to the default agg time dimension for the semantic model. [Optional]
-        non_additive_dimension: configs used if needing non-additive dimensions [Optional]
-        label: How the metric will be displayed in docs and downstream integrations. [Required]
-`
+        non_additive_dimension: Use these configs when you need non-additive dimensions. [Optional]
+        label: How the metric appears in project docs and downstream integrations. [Required]
+```
 
 ### Name 
 
@@ -82,7 +83,7 @@ If you use the `dayofweek` function in the `expr` parameter with the legacy Snow
 ```yaml
 semantic_models:
  - name: transactions
-    description: A record for every transaction that takes place. Carts are considered  multiple transactions for each SKU.
+    description: A record of every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
     defaults:
       agg_time_dimensions:
@@ -210,14 +211,14 @@ semantic_models:
           name: metric_time
           window_choice: min 
       - name: mrr_end_of_month
-        description: Aggregate by summing all users active subscription plans at end of month 
+        description: Aggregate by summing all users' active subscription plans at the end of month 
         expr: subscription_value
         agg: sum 
         non_additive_dimension: 
           name: metric_time
           window_choice: max
       - name: mrr_by_user_end_of_month
-        description: Group by user_id to achieve each users MRR at the end of the month 
+        description: Group by user_id to achieve each user's MRR at the end of the month 
         expr: subscription_value
         agg: sum  
         non_additive_dimension: 

From 0bacc97c00b00720af3f2c04abbb6f5dc8ef5082 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 12:14:36 +0100
Subject: [PATCH 181/703] Update website/docs/docs/build/metricflow-cli.md

---
 website/docs/docs/build/metricflow-cli.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 36abd524c8d..d504c73bc16 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -15,7 +15,11 @@ After you have defined metrics in your dbt project, you can easily query metrics
 ## CLI commands
 
 ## List
-Retrieve metadata values about metrics/dimensions/entities/dimension values
+
+This command retrieves metadata values related to [Metrics](/docs/build/metrics-overview), [Dimensions](/docs/build/dimensions), and [Entites](/docs/build/entities) values. 
+
+For example, if you're trying to retrieve the `name` metadata value for a metric, you can use the following command:
+
 
 # List metrics
 List the metrics with their available dimensions

From e55c535d12ffe80ff3bb449031ddb45130428f7b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 12:19:07 +0100
Subject: [PATCH 182/703] adding tabs and reformatting

---
 website/docs/docs/build/about-metricflow.md   |   4 +-
 .../docs/docs/build/build-metrics-intro.md    |   2 +-
 website/docs/docs/build/metricflow-cli.md     | 193 ++++++++++++++----
 website/docs/docs/build/sl-getting-started.md |   5 +-
 website/sidebars.js                           |   1 +
 5 files changed, 156 insertions(+), 49 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 6ec7ecfe4b5..aa532179700 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -33,7 +33,9 @@ There are a few key principles:
 
 - MetricFlow, as a part of the dbt Semantic Layer, allows organizations to define company metrics logic through YAML abstractions, as described in the following sections.
 
-- You can install MetricFlow via PyPI as an extension of your [dbt adapter](/docs/supported-data-platforms) in the CLI. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`.
+- You can install MetricFlow using PyPI as an extension of your [dbt adapter](/docs/supported-data-platforms) in the CLI. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`.
+
+- To query metrics dimensions, dimension values, and validate your configurations; install the [MetricFlow CLI](/docs/build/metricflow-cli).
 
 ### Semantic graph 
 
diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index e98ee013d0b..a87d4567a2b 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -18,7 +18,7 @@ To fully experience the dbt Semantic Layer, including the ability to query dbt m
 :::
 
 Before you start, keep the following considerations in mind:
-- Use the CLI to define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
+- Define metrics in YAML and query them using the [MetricFlow CLI](/docs/build/metricflow-cli).
 - You must be on dbt Core v1.6 beta or higher to use MetricFlow. [Upgrade your dbt version](/docs/core/pip-install#change-dbt-core-versions) to get started.
   * Note: Support for dbt Cloud and querying via external integrations coming soon.
 - MetricFlow currently only supports Snowflake and Postgres.
diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 36abd524c8d..ee048539ac4 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -2,24 +2,45 @@
 title: MetricFlow CLI
 id: metricflow-cli
 description: "Query metrics and metadata in your dbt project with the metricflow cli"
-sidebar_label: "CLI"
+sidebar_label: "MetricFlow CLI"
 tags: [Metrics, Semantic Layer]
 ---
 
-After you have defined metrics in your dbt project, you can easily query metrics, dimensions, dimension values, and validate your configs using the MetricFlow CLI.
+After you have defined metrics in your dbt project, you can query metrics, dimensions, dimension values, and validate your configs using the MetricFlow command line (CLI).
 
 ## Installation
+
+You can install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install the MetricFlow CLI on Windows or Linux operating systems:
+
 1. Create or activate your virtual environment.`python -m venv venv`
 2. Run `pip install dbt-metricflow`
 
+The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
+
 ## CLI commands
 
-## List
+The MetricFlow CLI provides the following commands to retrieve metadata and query metrics. 
+
+To execute the commands, use the `mf` prefix before the command name. For example, to list all metrics, run `mf list metrics`:
+
+- [`list`](#list) &mdash; Retrieves metadata values 
+- [`list metrics`](#list-metrics) &mdash; Lists metrics with dimensions
+- [`list dimensions`](#list) &mdash; Lists unique dimensions for metrics
+- [`list dimension-values`](#list-dimension-values) &mdash; List dimensions with metrics
+- [`list entities`](#list-entities) &mdash; Lists all unique entities
+- [`validate-configs`](#validate-configs) &mdash; Validates semantic model configurations
+- [`health-checks`](#health-checks) &mdash; Performs data platform health check
+- [`tutorial`](#tutorial) &mdash; Dedicated MetricFlow tutorial to help get you started
+- [`query`](#query) &mdash; Query metrics and dimensions you want to see in the CLI. You can also refer to [query examples](#query-examples) to help you get started.
+### List
+
 Retrieve metadata values about metrics/dimensions/entities/dimension values
 
-# List metrics
-List the metrics with their available dimensions
-```
+### List metrics
+
+This command lists the metrics with their available dimensions:
+
+```bash
 mf list metrics <metric_name>
 Options:
   --search TEXT          Filter available metrics by this search term
@@ -27,9 +48,11 @@ Options:
   --help                 Show this message and exit.
 ```
 
-# List dimensions
-List all unique dimensions for a metric or multiple metrics. Only common dimensions are shown if querying multiple metrics
-```
+### List dimensions
+
+This command lists all unique dimensions for a metric or multiple metrics. It displays only common dimensions when querying multiple metrics:
+
+```bash
 mf list dimensions --metrics <metric_name>
 Options:
   --metrics SEQUENCE  List dimensions by given metrics (intersection). Ex.
@@ -37,9 +60,11 @@ Options:
   --help              Show this message and exit.
 ```
 
-# List dimension-values
-List all dimension values with the corresponding metric
-```
+### List dimension-values
+
+This command lists all dimension values with the corresponding metric:
+
+```bash
 mf list dimension-values --metrics <metric_name> --dimension <dimension_name>
 Options:
   --dimension TEXT    Dimension to query values from  [required]
@@ -51,9 +76,11 @@ Options:
                       of the data (inclusive)
   --help              Show this message and exit.
 ```
-# List entities
-List all unique entities.
-```
+### List entities
+
+This command lists all unique entities:
+
+```bash
 mf list entities --metrics <metric_name>
 Options:
   --metrics SEQUENCE  List entities by given metrics (intersection). Ex.
@@ -61,9 +88,11 @@ Options:
   --help              Show this message and exit.
 ```
 
-## Validate configs
- Perform validations against the defined semantic model configurations.
-```
+### Validate-configs
+
+This command performs validations against the defined semantic model configurations:
+
+```bash
 mf validate-configs
 Options:
   --dw-timeout INTEGER            Optional timeout for data warehouse
@@ -82,22 +111,27 @@ Options:
   --help                          Show this message and exit.
 ```
 
-## Health checks
-Performs a health check against the DW provided in the configs.
-```
+### Health checks
+
+This command performs a health check against the data platform you provided in the configs:
+
+```bash
 mf health-checks
 ```
 
-## Tutorial
-Runs through a getting started tutorial. 
+### Tutorial
 
-```
+Follow the dedicated MetricFlow tutorial to help you get started:
+
+```bash
 mf tutorial
 ```
 
-## Query
-Create a new query with MetricFlow, execute that query agaisnt the users data platform and return the result. 
-```
+### Query
+
+Create a new query with MetricFlow, execute that query against the user's data platform, and return the result:
+
+```bash
 mf query --metrics <metric_name> --group-by <dimension_name>
 Options:
   --metrics SEQUENCE       Metrics to query for: syntax is --metrics bookings
@@ -116,7 +150,7 @@ Options:
   --order SEQUENCE         Metrics or group bys to order by ("-" prefix for
                            DESC). For example: --order -ds or --order
                            ds,-revenue
-  --csv FILENAME           Provide filepath for dataframe output to csv
+  --csv FILENAME           Provide filepath for data frame output to csv
   --explain                In the query output, show the query that was
                            executed against the data warehouse
   --show-dataflow-plan     Display dataflow plan in explain output
@@ -128,13 +162,23 @@ Options:
   ```
 
 
-## Query Examples
+### Query examples
 
-Query metrics by dimensions.
+The following tabs presents various different types of query examples that you can use to query metrics and dimensions. Select the tab that best suits your needs:
 
-The following query will retrun the order_amount metric by metric time. 
-```
+<Tabs>
+
+<TabItem value="eg1" label="Metrics">
+
+**Example 1** &mdash; Use the example to query metrics by dimension and return the `order_amount` metric by `metric_time.` 
+
+**Query**
+```bash
 mf query --metrics order_amount --group-by metric_time
+```
+
+**Result**
+```bash
 ✔ Success 🦄 - query completed after 1.24 seconds
 | METRIC_TIME   |   ORDER_AMOUNT |
 |:--------------|---------------:|
@@ -145,11 +189,19 @@ mf query --metrics order_amount --group-by metric_time
 | 2017-06-20    |         712.51 |
 | 2017-06-21    |         541.65 |
 ```
+</TabItem>
 
-You can include multiple dimensions in a query. For example, we can also group by the `is_food_order` dimension to see if orders we're for food or not. 
+<TabItem value="eg2" label="Dimensions">
 
-```
+**Example 2** &mdash; You can include multiple dimensions in a query. For example, you can group by the `is_food_order` dimension to confirm if orders were for food or not. 
+
+**Query**
+```bash
 mf query --metrics order_amount --group-by metric_time, is_food_order
+```
+
+**Result**
+```bash
  Success 🦄 - query completed after 1.70 seconds
 | METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
 |:--------------|:----------------|---------------:|
@@ -163,10 +215,19 @@ mf query --metrics order_amount --group-by metric_time, is_food_order
 | 2017-06-19    | True            |         448.11 |
 ```
 
-You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records, and orders by metric time descending. 
+</TabItem>
 
-```
+<TabItem value="eg3" label="Order/limit">
+
+**Example 3** &mdash; You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records and orders them by `metric_time`, descending.
+
+**Query**
+```bash
 mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time
+```
+
+**Result**
+```bash
 ✔ Success 🦄 - query completed after 1.41 seconds
 | METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
 |:--------------|:----------------|---------------:|
@@ -178,11 +239,19 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 | 2017-08-29    | False           |         333.65 |
 | 2017-08-28    | False           |         334.73 |
 ```
+</TabItem>
 
-You can futher filter the data set by adding a where clause to your query.
+<TabItem value="eg4" label="where clause">
 
-```
+**Example 4** &mdash; You can further filter the data set by adding a `where` clause to your query.
+
+**Query**
+```bash
  mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True"
+```
+
+**Result**
+```bash
  ✔ Success 🦄 - query completed after 1.06 seconds
 | METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
 |:--------------|:----------------|---------------:|
@@ -198,10 +267,19 @@ You can futher filter the data set by adding a where clause to your query.
 | 2017-08-22    | True            |         401.91 |
 ```
 
-To filter by time, there are dedicated start and end time options. Using these options to filter by time allows MetricFlow to further optimize query performance by pushing down the where filter when appropriate. 
+</TabItem>
 
-```
+<TabItem value="eg5" label=" Filter by time">
+
+**Example 5** &mdash; To filter by time, there are dedicated start and end time options. Using these options to filter by time allows MetricFlow to further optimize query performance by pushing down the where filter when appropriate. 
+
+**Query**
+```bash
  mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27'
+```
+
+ **Result**
+```bash
 ✔ Success 🦄 - query completed after 1.53 seconds
 | METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_AMOUNT |
 |:--------------|:----------------|---------------:|
@@ -213,10 +291,21 @@ To filter by time, there are dedicated start and end time options. Using these o
 | 2017-08-22    | True            |         401.91 |
 ```
 
-You can see the SQL metricflow is generating by adding --explain to your query. 
+</TabItem>
 
-```
+
+<TabItem value="eg6" label="--explain flag">
+
+**Example 6** &mdash; Add `--explain` to your query to view the SQL generated by MetricFlow. 
+
+**Query**
+
+```bash
  mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --explain
+```
+
+ **Result**
+ ```bash
  ✔ Success 🦄 - query completed after 0.28 seconds
 🔎 SQL (remove --explain to see data or add --show-dataflow-plan to see the generated dataflow plan):
 SELECT
@@ -238,10 +327,24 @@ GROUP BY
 ORDER BY metric_time DESC
 LIMIT 10
 ```
-To export the results of your query to a csv, simply add the `--csv file_name.csv` flag.
 
-```
+</TabItem>
+
+<TabItem value="eg7" label=" Export to CSV">
+
+**Example 7** &mdash; Add the `--csv file_name.csv` flag to export the results of your query to a csv.
+
+**Query**
+
+```bash
 mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --csv query_example.csv
+```
+
+**Result**
+```bash
 ✔ Success 🦄 - query completed after 0.83 seconds
 🖨 Successfully written query output to query_example.csv
-```
\ No newline at end of file
+```
+
+</TabItem>
+</Tabs>
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index ff0e6006921..5b8a40904f5 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -20,7 +20,7 @@ This getting started page recommends a workflow to help you get started creating
 - Have a dbt project connected to Snowflake or Postgres. 
   * Note: Support for BigQuery, Databricks, and Redshift coming soon.
 - Have an understanding of key concepts in [MetricFlow](/docs/build/about-metricflow), which powers the revamped dbt Semantic Layer.
-- Recommended &mdash; dbt Labs recommends you install the [MetricFlow CLI package](https://github.com/dbt-labs/metricflow) to test your metrics.
+- Recommended &mdash; Install the [MetricFlow CLI](/docs/build/metricflow-cli) to query and test your metrics.
 
 :::tip 
 New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
@@ -108,7 +108,7 @@ Interact and test your metric using the CLI before committing it to your MetricF
 
 Follow these steps to test and query your metrics using MetricFlow:
 
-1. If you haven't done so already, make sure you [install MetricFlow](#install-metricflow).
+1. If you haven't done so already, make sure you [install MetricFlow](#install-metricflow). Refer to [MetricFlow CLI](/docs/build/metricflow-cli) for more info on commands and how to install the CLI.
 
 2. Run `mf --help` to confirm you have MetricFlow installed, and to see the available commands. If you don't have the CLI installed, run `pip install --upgrade "dbt-metricflow[your_adapter_name]"`.  For example, if you have a Snowflake adapter, run `pip install --upgrade "dbt-metricflow[snowflake]"`.
 
@@ -130,3 +130,4 @@ ANY COMMON TROUBLESHOOTING QUESTIONS?-->
 - [About MetricFlow](/docs/build/about-metricflow)
 - [Semantic models](/docs/build/semantic-models)
 - [Metrics](/docs/build/metrics-overview)
+- [MetricFlow CLI](/docs/build/metricflow-cli)
diff --git a/website/sidebars.js b/website/sidebars.js
index bf992619dbc..e11f51feca9 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -257,6 +257,7 @@ const sidebarSettings = {
                 "docs/build/join-logic",
                 "docs/build/validation",
                 "docs/build/metricflow-time-spine",
+                "docs/build/metricflow-cli",
               ]
             },
             "docs/build/sl-getting-started",

From 9de4f66fa062bc14c5034a882bba82e589c04d3b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 12:20:03 +0100
Subject: [PATCH 183/703] change sidebar

---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index ee048539ac4..8dff28680d4 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -2,7 +2,7 @@
 title: MetricFlow CLI
 id: metricflow-cli
 description: "Query metrics and metadata in your dbt project with the metricflow cli"
-sidebar_label: "MetricFlow CLI"
+sidebar_label: "MetricFlow CLI commands"
 tags: [Metrics, Semantic Layer]
 ---
 

From eab30e5aeb226f0ae2d4fbf8b9afa7885df9d489 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 12:32:39 +0100
Subject: [PATCH 184/703] active voice

---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 9cab298fd59..6a49f11878f 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -6,7 +6,7 @@ sidebar_label: "MetricFlow CLI commands"
 tags: [Metrics, Semantic Layer]
 ---
 
-After you have defined metrics in your dbt project, you can query metrics, dimensions, dimension values, and validate your configs using the MetricFlow command line (CLI).
+Once you define metrics in your dbt project, you can query metrics, dimensions, dimension values, and validate your configs using the MetricFlow command line (CLI).
 
 ## Installation
 

From 2022dd372ad677ff100bdb6de472dcf742a8f477 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 13:34:41 +0100
Subject: [PATCH 185/703] mirna's tweaks to nick and roxi's updates

---
 .../docs/dbt-cloud-apis/sl-api-overview.md     |  2 +-
 website/docs/docs/dbt-cloud-apis/sl-graphql.md | 11 ++++++-----
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md    |  4 +++-
 .../06-July-2023/sl-revamp-beta.md             | 18 +++++++++---------
 .../docs/docs/use-dbt-semantic-layer/dbt-sl.md |  2 +-
 .../use-dbt-semantic-layer/quickstart-sl.md    |  2 +-
 website/snippets/_new-sl-setup.md              |  8 +++++---
 website/snippets/_v2-sl-prerequisites.md       |  6 +++---
 8 files changed, 29 insertions(+), 24 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 0490615e491..bc5472a2c01 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -26,7 +26,7 @@ You can use the dbt Semantic Layer for a variety of tools and applications of da
 * Data discovery and cataloging
 * Machine learning and data science
 
-The dbt Semantic Layer APIs are available for accounts on [Team or Enterprise plans](https://www.getdbt.com/pricing/), allowing them to query metrics and build integrations. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally. 
+During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools. 
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 1d9eb08b769..19154eda216 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -19,16 +19,17 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 With GraphQL, users can request specific data using a single query, reducing the need for many server round trips. This improves performance and minimizes network overhead.
 
 GraphQL has several advantages, such as self-documenting, having a strong typing system, supporting versioning and evolution, enabling rapid development, and having a robust ecosystem. These features make GraphQL a powerful choice for APIs that prioritize flexibility, performance, and developer productivity.
-
-dbt Partners can use the Semantic Layer GraphQL API to build and integration with the dbt Semantic Layer.
-
 ## dbt Semantic Layer GraphQL API
 
 The dbt Semantic Layer GraphQL API allows you to explore and query metrics and dimensions. Due to it's self-documenting nature, you can explore the calls conveniently through the [schema explorer](https://cloud.getdbt.com/semantic-layer/api/graphql). 
 
+dbt Partners can use the Semantic Layer GraphQL API to build and integration with the dbt Semantic Layer.
+
 ## Using the GraphQL API
 
-If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. (PROVIDE link to set up?). 
+If you're a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. 
+
+Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ### Authentication 
 
@@ -38,7 +39,7 @@ Authentication uses a dbt Cloud Service token passed through a header as follows
 {"Authorization": "Bearer <SERVICE TOKEN>"}
 ```
 
-Each GQL request also comes with a dbt Cloud Environment Id. Our API will use the combination of the Service Token in the header and Environment Id to authenticate.
+Each GQL request also comes with a dbt Cloud environmentId. The API uses both the service token in the header and environmentId for authentication.
 
 
 ### Metric metadata calls
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 84610bd4d3b..25163eb05d6 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -29,7 +29,9 @@ Partners of dbt Labs can use the JDBC API to build integrations in their tools w
 
 ## Using the JDBC API
 
-If you are a dbt user or partner with access to dbt Cloud and the Semantic Layer, you can setup and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document. (PROVIDE link to set up?). 
+If you are a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
+
+Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ## Connection parameters
 
diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 9c0b7f34828..ec2ebeca7f1 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -7,11 +7,11 @@ date: 2023-07-31
 sidebar_position: 9
 ---
 
-We are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
+dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
 
-- [MetricFlow](/docs/build/about-metricflow), a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
-- New Semantic Layer Infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift and soon more) and improved performance.
-- New and Improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
+- [MetricFlow](/docs/build/about-metricflow) is a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
+- New Semantic Layer infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift, and soon more), along with improved performance.
+- New and improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
 - New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
@@ -24,7 +24,7 @@ What sets the dbt Semantic Layer apart is its ability to centralize logic for ma
 
 We are excited to present several important capabilities with the enhanced dbt Semantic Layer:
 
-- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization and teh breadth of interfaces where data is consumed. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
+- **Consistent organization**: Provides a consistent view of data, ensuring that metrics and definitions match across the organization and the breadth of interfaces where data is consumed. This fosters trust in data and drives better decision-making by eliminating inconsistencies and errors that come up when individual users define metrics independently.
 
 - **Improved governance**: The dbt Semantic Layer ensures proper governance and auditing of data changes, providing an auditable record of modifications and clear ownership. This saves time by making it clear who can create and manage new metrics, ensuring accountability and data integrity.
 
@@ -32,7 +32,7 @@ We are excited to present several important capabilities with the enhanced dbt S
 
 - **Enhanced efficiency**: With the dbt Semantic Layer, data teams can create and update metrics using a new set of validations that make defining and iterating on metrics efficient. The streamlined development workflows makes it simpler for a data team to serve large organizations with broad data needs.
 
-- **Accessible data**: Defining common metrics and dimensions and making them joinable, makes access simpler for users with less expertise in the specifics of a companies data modeling work. This creates opportunities to leverage data insights, fostering collaboration and driving innovation in a more inclusive data environment.
+- **Accessible data**: Defining common metrics and dimensions and making them joinable, makes access simpler for users with less expertise in the specifics of a company's data modeling work. This creates opportunities to leverage data insights, fostering collaboration and driving innovation in a more inclusive data environment.
 
 By bringing these enhancements to the dbt Semantic Layer, we enable organizations of all sizes and industries to leverage the power of semantics in their data workflows.
 
@@ -42,11 +42,11 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
 - **Who** &mdash; To experience the new dbt Semantic Layer, you must be on a dbt Cloud [Team and Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plan, [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America and on dbt v1.6 and higher. Look out for announcements on removing the location requirement soon.
 
-  - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt-metricflow CLI only.
+  - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt MetricFlow CLI only.
 
-- **What** &mdash; Public beta provides early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account rep.
+- **What** &mdash; Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account representative.
 
-- **When** &mdash; Public beta starts on July 31 and will end once the dbt Semantic Layer is available for GA in October 2023.
+- **When** &mdash; Public beta starts on July 31, 2023 and will end once the dbt Semantic Layer is available for GA in October 2023.
 
 - **Where** &mdash; You can experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 6a2d3ea392d..d79e4ddf6fb 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -22,7 +22,7 @@ To read more about why you need a universal Semantic Layer, read this [blog post
 
 ## Explore the dbt Semantic Layer
 
-During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or newer. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
+During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index d8a6608e84d..70e6300df02 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -150,7 +150,7 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query APIs
 
-add content here
+This step requires you to connect to the [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview). Once you've connected to an API, you should then set up and query metrics in your downstream tool of choice. Refer to [dbt Semantic Layer Apis](/docs/dbt-cloud-apis/sl-api-overview) and [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 1e8631db14e..3e8fdf5d867 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -11,9 +11,11 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
     * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
 5. Select the deployment environment you want for the Semantic Layer
-6. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
-7. Save & copy your environment ID, service token, and host for inputting into a downstream tool
-8. Great job, you've configured the Semantic Layer 🎉! 
+6. You should see connection information that allows you to connect to downstream tools. 
+    * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.
+7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+8. Save & copy your environment ID, service token, and host for inputting into a downstream tool
+9. Great job, you've configured the Semantic Layer 🎉! 
 
 
 
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 1063e8e961d..d602e5d0d3b 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -1,15 +1,15 @@
 
 <VersionBlock firstVersion="1.6">
 
-- Have a [multi-tenant dbt Cloud](/docs/deploy/regions) instance, hosted in North America
+- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
-- Install the [MetricFlow command line (CLI)](https://github.com/dbt-labs/metricflow) package
+- Install the [MetricFlow CLI](/docs/build/metricflow-cli)
   * Note &mdash; After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query manually via the CLI using SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
+  * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
 
 
 </VersionBlock>

From 36ad75ee7d32d142d33df568ab3a32c8230d7ee3 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 13:44:32 +0100
Subject: [PATCH 186/703] fix space

---
 website/docs/docs/dbt-cloud-apis/sl-graphql.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index 19154eda216..e834346d016 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -27,7 +27,7 @@ dbt Partners can use the Semantic Layer GraphQL API to build and integration wit
 
 ## Using the GraphQL API
 
-If you're a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. 
+If you're a dbt user or partner with access to dbt Cloud and the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right GQL connection parameters described in this document. 
 
 Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 

From a067a4a8213def3004ba6e32df606977b69fa6d7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 14:28:29 +0100
Subject: [PATCH 187/703] put partner guide back in

---
 .../sl-partner-integration-guide.md           | 660 ++++++++++++++++++
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+---
+title: "dbt Semantic Layer integration" 
+id: "sl-partner-integration-guide"
+description: Learn about partner integration guidelines, roadmap, and connectivity. 
+---
+
+# dbt Semantic Layer partner integration
+
+:::info Coming soon
+The dbt Semantic Layer is undergoing some sophisticated changes, enabling more complex metric definitions and efficient querying. As part of these changes, the dbt_metrics package will be deprecated and replaced with MetricFlow. For more info, check out the [The dbt Semantic Layer: what's next?](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) and [dbt_metrics deprecation](https://docs.getdbt.com/blog/deprecating-dbt-metrics) blog.
+:::
+
+This guide is for dbt Semantic Layer integration partners and explains integration guidelines, and connectivity.<br />
+
+To become a formal partner, integrate with the API, or have questions/feedback &mdash; **[contact us](mailto:semantic-layer@dbtlabs.com)** for more info.
+
+The dbt Semantic Layer allows users to dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics, models, and entities. It helps organizations manage complexities such as data, tools, and teams to make more efficient and trustworthy decisions.
+
+The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads. 
+
+To solve this, the dbt Semantic Layer provides a platform where users can confidently leverage their data from within their tools. dbt Cloud's change management capabilities ensure that any user modifications made to core business constructs, like metrics or entities, are distributed into all the tools connected to the data platform.
+
+The dbt Semantic Layer can be used for a variety of tools and applications of data. Here are some common use cases
+
+* Business intelligence (BI), reporting, and analytics,
+* Data quality and monitoring,
+* Governance and privacy,
+* Data discovery and cataloging,
+* Machine learning and data science.
+
+:::info Share your use case
+
+If you'd like to share other use cases for the dbt Semantic Layer, contact the [dbt Labs team](mailto:semantic-layer@dbtlabs.com).
+
+:::
+
+
+## Product overview
+
+The dbt Semantic Layer product architecture includes four primary components:
+
+| Components | Information | Developer plans | Team plans | Enterprise plans | License |
+| --- | --- | :---: | :---: | :---: | --- |
+| **[dbt Project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source in dbt Core |
+| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt Core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
+| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja-like query models and metrics, use macros), compiles the query into pure SQL, executes the query in the data platform, and returns the data. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary in dbt Cloud |
+| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, and metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary in dbt Cloud |
+    
+Review the following current architecture to understand how the components work together:
+  
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="Current product architecture" />
+
+
+## Integration guidelines
+
+In collaboration with dbt Labs, partners and users can build dbt Semantic Layer integrations that can import model metadata and metric definitions, query metrics, use macros, and more. 
+
+For more details, refer to the [Integration roadmap](#integration) and [Integration best practices](#best-practices) guidance.
+
+**Integration roadmap <a id="integration"></a>** 
+
+Integration partners generally build and approach their roadmap in the following stages:
+
+| Feature  |  Info | Availability |
+|----------|-------|:------------:|
+| **Model metadata**  |  Import/sync model metadata (descriptions, dimensions, test, freshness, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
+| **Metric definitions**  | Import/sync metric definitions (metric calculation, dimensions, description, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
+| **dbt Semantic Layer as a data source**  | Connect to the dbt Semantic Layer as a data source (for example, the Snowflake Proxy Server). Users can execute dbt-SQL to query metrics or models and use macros.* | ✅ |
+| **Query metrics**  | Query the imported metrics via a metric-centric UI (for example, a user can select a metric, time grain, and dimensions of interest). | ✅ |
+| **Entity definitions**   | Import/sync entity definitions (descriptions, dimensions, data types, relationships, metrics, and more) and query entities via the dbt Semantic Layer. | _*Coming soon |
+| **dbt Semantic Layer Connector**   | A dedicated connector with the ability to query any data platform supported in dbt Cloud. (Will replace (3).) | _*Coming soon |
+
+_*The coming soon features are expected to launch in 2023. 
+
+**Integration best practices <a id="best-practices"></a>**
+
+To build a successful and seamless dbt Semantic Layer integration, it should express the following:
+
+- **Consistent**: Have a consistent user experience (UX) incorporated into existing core user workflows.
+- **Trustworthy**: Treat dbt assets (metrics, models, and entities) as first-class objects and indicate that their definitions and resulting datasets come from dbt Cloud.
+- **Efficient**: Provide a clear advantage over the current approach to setting up metrics and analyses, and finding dimensions/datasets in the tool.
+- **Accessible**: Include a self-serve component so a data consumer can ask questions via the user interface (UI), if applicable.
+
+
+## Use the Discovery API
+
+This section will explain how to connect to and query the [Discovery API](/docs/dbt-cloud-apis/discovery-api) for model and metric definitions. 
+
+To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-semantic-layer/dbt-sl#prerequisites). 
+
+<details>
+  <summary><b>Discovery API authorization</b></summary>
+  <div>
+    <div>Refer to our <a href="/docs/dbt-cloud-apis/discovery-querying#authorization">Authorization documentation</a> to learn how to authorize requests to the Discovery API.<br></br><br></br>
+    
+    Metrics-specific queries work identical to existing Discovery API queries. This means existing integrations that query model metadata will work perfectly in the context of metrics.
+    </div>
+    </div>
+    </details>
+
+  <details>
+  <summary><b>Query the Discovery API</b></summary>
+  <div>
+    <div>Test out the Discovery API by using the <a href="https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql">GraphQL sandbox</a> and use this <a href="https://github.com/transform-data/dbt-metadata-client">Python client</a> as a starting point to develop.
+    </div>
+    </div>
+    </details>
+    <br></br>
+
+<!-- the following content was used for the <details> portion above. leaving this here for legacy purposes 
+**Metadata API authorization**
+
+To learn how to authorize requests to the Metadata API, review the [documentation](/docs/dbt-cloud-apis/discovery-querying#authorization) for more details. Metrics-specific queries work identically to existing Metadata API queries, so existing integrations used to query model metadata will work perfectly in the context of metrics. 
+
+**Query the Metadata API**
+
+Test out the Metadata API by using the [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) and use this [Python client](https://github.com/transform-data/dbt-metadata-client) as a starting point to develop. 
+
+-->
+
+### Query models for a project
+
+You can query model definitions or details about a specific model for a project from a given job.
+
+ 
+<!--- tabs for listing models and fetching details about specific model --->
+<Tabs>
+
+<TabItem value="list" label="Listing models for a job">
+
+This is an example of querying all models that utilize the schema`analytics` from a given job.
+
+```
+{  
+ models(jobId: 181329, schema: "analytics") {
+   name
+   status
+   compileCompletedAt
+   database
+   dbtVersion
+   runGeneratedAt
+   }
+}
+```
+</TabItem>
+
+<TabItem value="exampleresponse" label="Example response with models">
+
+```
+{
+   "data": {
+       "models": [
+           {
+               "name": "customers",
+               "status": "success",
+               "compileCompletedAt": "2022-12-15T06:37:24.186Z",
+               "database": "analytics",
+               "dbtVersion": "1.3.1",
+               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
+           },
+           {
+               "name": "stg_customers",
+               "status": "success",
+               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
+               "database": "analytics",
+               "dbtVersion": "1.3.1",
+               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
+           },
+           {
+               "name": "stg_orders",
+               "status": "success",
+               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
+               "database": "analytics",
+               "dbtVersion": "1.3.1",
+               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
+           }
+       ]
+   }
+}
+```
+</TabItem>
+
+<TabItem value="details" label="Query details about a specific model">
+
+This is an example of querying details about a specific model, `model.jaffle_shop.customers`, from a given job.
+
+```
+{
+ model(jobId: 181329, uniqueId: "model.jaffle_shop.customers") {
+   parentsModels {
+     runId
+     uniqueId
+     executionTime
+   }
+ }
+}
+{
+   "data": {
+       "model": {
+           "parentsModels": [
+               {
+                   "runId": 105297555,
+                   "uniqueId": "model.jaffle_shop.stg_customers",
+                   "executionTime": 1.676571846008301
+               },
+               {
+                   "runId": 105297555,
+                   "uniqueId": "model.jaffle_shop.stg_orders",
+                   "executionTime": 1.631831407546997
+               }
+           ]
+       }
+   }
+}
+```
+
+</TabItem>
+</Tabs>
+
+<!--- End tabs for listing models and fetching details about specific model --->
+
+### Query metrics for a project
+
+Query metrics definitions or details for a project from a given job and refer to the following resources: 
+
+- [Metrics query](/docs/dbt-cloud-apis/discovery-schema-metrics) &mdash; Information on how to query the full list of metrics defined in a user’s project with the dbt Cloud Discovery API. 
+- [dbt Metrics docs](https://docs.getdbt.com/docs/build/metrics#available-properties) &mdash; Information on the available metric properties. 
+- [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) &mdash; Access to test the dbt Cloud Discovery API testing environment.
+
+<!--- tabs for listing, fetching, example, and querying metrics --->
+<Tabs>
+
+<TabItem value="list" label="Listing metrics">
+
+This is an example listing metrics from a given job:
+
+```
+{
+  metrics(jobId: 123) {
+    name
+    label
+    description
+    model
+    dependsOn
+    calculation_method
+    expression
+    timestamp
+    timeGrains
+    dimensions
+    window
+    filters
+    tags
+    meta
+  }
+}
+```
+</TabItem>
+
+<TabItem value="query" label="Query details about a specific metric">
+
+The `metric` query supports all metric properties listed in **Listing metrics**. 
+The following abbreviated example is querying details about the metric `new_customers` from job `123`:
+
+This is an example of querying details about a specific metric `new_customers` from a given job `123`.
+
+```
+{
+  metric(jobId: 123) {
+    label
+    calculation_method
+    timestamp
+    timeGrains
+    dimensions
+  }
+}
+```
+
+</TabItem>
+
+<TabItem value="exampleapi" label="Example Discovery API response with metrics">
+
+```
+{
+  "data": {
+    "metrics": [
+      {
+        "uniqueId": "metric.claim_to_fame.total_claim_charges",
+        "name": "total_claim_charges",
+        "tags": [],
+        "label": "Total Claim Charges",
+        "calculation_method": "sum",
+        "expression": "total_charge_amount",
+        "timestamp": "created_at",
+        "timeGrains":[
+          "day",
+          "week",
+          "month"
+        ],
+        "meta": {},  
+        "resourceType": "metric",
+        "model": {
+          "name": "fct_billed_patient_claims"
+        }
+       },
+      {
+        "uniqueId": "metric.claim_to_fame.total_billed_diagnoses",
+        "name": "total_billed_diagnoses",
+        "tags": [],
+        "label": "Total Billed Diagnoses",
+        "calculation_method": "count_distinct",
+        "expression": "diagnosis_id",
+        "timestamp": "created_at",
+        "timeGrains":[
+          "week",
+          "month",
+          "year"
+        ],
+        "meta": {},  
+        "resourceType": "metric",
+        "model": {
+          "name": "fct_billed_patient_claims"
+        },
+      }
+     ]
+  }
+}
+```
+
+</TabItem>
+
+<TabItem value="example" label="Example metric definition">
+
+```
+metrics:
+  - name: total_claim_charges
+    label: Total Claim Charges
+    model: ref('fct_billed_patient_claims')
+    calculation_method: sum
+    expression: total_charge_amount
+    timestamp: created_at
+    time_grains: [day, week, month, all_time]
+
+
+  - name: total_billed_diagnoses
+    label: Total Billed Diagnoses
+    model: ref('fct_billed_patient_claims')
+    calculation_method: count_distinct
+    expression: diagnosis_id
+    timestamp: created_at
+    time_grains: [day, week, month]
+```
+
+</TabItem>
+
+</Tabs>
+
+<!--- End tabs for listing, fetching, example, and querying metrics --->
+
+
+## Query the dbt Semantic Layer
+
+This section explains how to connect to or query the dbt Semantic Layer Proxy Server to return model data, metric data, and so on. 
+
+When you configure the dbt Semantic Layer, dbt Cloud provides a Proxy Server endpoint that users can connect to as though it's a Snowflake-hosted endpoint. Once the queries are submitted, dbt Cloud will:
+
+1. Compile dbt-sql queries into valid Snowflake SQL,
+2. Execute the compiled SQL against the Snowflake data platform,
+3. Return the results to the client.
+
+Replace the hostname in your existing data platform connection with the relevant dbt Cloud Proxy Server URL (for example, `abc123.proxy.cloud.getdbt.com`). All queries you submit through the endpoint will be compiled en route to the data platform.* 
+
+*_Note: This approach will change with the new Semantic Layer connection in mid-2023, which will be able to query all data platforms supported in dbt Cloud through dedicated JDBC/ODBC drivers, and eventually an API._
+
+
+<!--- tabs for running models and running metrics --->
+<Tabs>
+
+<TabItem value="metrics" label="Metric queries">
+
+Users can compile and execute metric queries using macros defined in the [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics).  This package:
+
+- Generates the SQL required to accurately calculate the metric definition,
+- Supplies helper macros for derived calculations (like month over month, year to date, and so on) time series operations
+
+
+```
+select * 
+from {{ metrics.calculate(
+    metric_list=[metric('customers'), metric(‘revenue’)],
+    grain='week',
+    dimensions=['plan', 'country'],
+    secondary_calculations=[
+        metrics.period_to_date(aggregate="sum", period="year"),
+        metrics.rolling(aggregate="average", interval=4, alias="avg_past_4wks")
+    ],
+    start_date='2020-01-01',
+    end_date="date_trunc('day', getdate())"
+) }}
+```
+
+</TabItem>
+
+<TabItem value="models" label="Model queries">
+
+Model queries allow users to query models and use macros from their dbt project.
+
+```
+select cents_to_dollars('amount_cents') as amount_dollars 
+from {{ ref('orders') }}
+```
+</TabItem>
+</Tabs>
+
+### Entities 
+<!-- rewrite a little and include a summary for each tab entry -->
+
+dbt Labs will introduce a new node type, **[entity](https://github.com/dbt-labs/dbt-core/issues/6379)**, when dbt Core version 1.5 launches. It introduces a new and efficient way to define metrics by reusing logic (for example, `time_grains`).  
+
+Entities are semantic objects made up of curated dimensions from models with more metadata defined. Over time, users can standardize metric and entity definitions with packages to speed up development. 
+
+For integrations, entities will provide information like:
+
+- a way to organize metrics based on the entity they reference, and 
+- a new consumable and dynamically generated dataset (versus finding a table in the data platform). 
+
+This information will be available alongside the Discovery API, and entities can be directly queried through the dbt Semantic Layer. 
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/entity-lineage.jpg" title="Entity lineage graph" />
+
+:::caution 🚧
+
+Entities are a work in progress &mdash; expect continuous changes and improvements. To stay up-to-date, refer to the [entity discussions](https://github.com/dbt-labs/dbt-core/issues/6379) page. 
+
+:::
+
+<!--- tabs for running entity def, entity metadata response, metric def, metric metadata response, entity queries --->
+
+<Tabs>
+
+<TabItem value="entitydef" label="Entity definition">
+
+Define entities in your dbt project.
+
+```
+entities: ## The top-level path of the new node
+  - name: [Required] ## The name of the entity
+    model: [Required] ## The name of the model that the entity is dependent on
+    description: [Optional] ## The description of the entity
+    
+    dimensions: [Optional] ## The list of dimensions & properties associated with the entity. 
+      - include: [Optional] *
+      - exclude: [Optional]
+      - name: [Required] ## The name of the dimension 
+		column_name: [Optional] ## The name of the column in the model if not 1:1. Serves as mapping
+		data_type: [Optional] ## The data type of the dimension
+		description: [Optional] ## Description of the dimension
+		default_timestamp: [Optional] ## Setting datetime dimension as default for metrics
+		time_grains: [Optional] ## Acceptable time grains for the datetime dimension
+		primary_key: [Optional] ## Whether this dimension is part of the primary key
+```
+</TabItem>
+
+<TabItem value="entityapi" label="Entity Discovery API response">
+
+Query entities via the Discovery API.
+
+```
+"entity.project_name.entity_name": {
+      "unique_id": "entity.project_name.entity_name",
+      "package_name": "project_name",
+      "original_file_path": "models/metric_definitions/ratio_metric.yml",
+      "name": "entity_name",
+      "model": "ref('model_name')",
+      "description": "some description",
+      "dimensions": {
+				"dimension_name": {
+            "name": "dimension_name",
+						"column_name": "column_name",
+						"default_timestamp": "true",
+						"time_grains": "[day, week, month, year]"
+            "primary_key": true,
+            "data_type": null,
+            "description": "TBD",
+            "meta": {},
+						}
+      },
+      "resource_type": "entity",
+      "meta": {},
+      "tags": [],
+      "config": {
+          "enabled": true,
+      },
+      "depends_on": {
+          "macros": [],
+          "nodes": [
+              "model.project_name.model_name",
+          ]
+      },
+			"docs": {
+        "show": true,
+        "node_color": null
+      },
+      "refs": [
+					[
+						"model_name",
+					]
+			],
+      "created_at": 1669653016.522599
+  },
+  ```
+</TabItem>
+
+<TabItem value="newmetricdef" label="New metric definition">
+
+How to define new [metrics](/docs/build/metrics) in your dbt project. The metric definition and metadata response will change accordingly once entities are introduced, notably with metrics referencing entities instead of models and inheriting entity dimensions. 
+
+    ```
+    metrics: 
+		## Always required
+	     - name: [Required] ## The name of the metric
+		label: [Required] ## The human-readable name of the metric
+		calculation_method: [Required] ## The calculation/aggregation used for the metric
+		expression: [Required] ## The SQL expression being aggregated/calculated
+		entity: [Required] ## The entity being used as the source of the metric
+
+		## Always optional
+		description: [Optional] ## Any description about the metric
+		timestamp: [Optional] ## The name of the timestamp field to use
+		time_grains: [Optional] ## The list of time grains that are permitted
+		filters: [Optional] ## The filters of the metric
+		window: [Optional] ## The ability to make a metric cumulative over a time period
+		config: [Optional] ## Additional information for configuring the output
+		
+		## Either or dimensions:
+			include: [Optional] ## The list of dimensions to be included. Either * or list
+			exclude: [Optional] ## The list of dimensions to be excluded from the inherited list
+    ```
+    
+</TabItem>      
+
+<TabItem value="newmetricapi" label="New metric Discovery API response">
+
+```
+"metric.project_name.metric_name": {
+      "fqn": [
+          "project_name",
+          "folder_name",
+          "metric_name"
+      ],
+      "unique_id": "metric.project_name.metric_name",
+      "package_name": "project_name",
+      "root_path": "file_path",
+      "path": "file_path",
+      "original_file_path": "file_path",
+      "name": "metric_name",
+      "description": "description",
+			"entity": "entity_name",
+      "label": "Human readable version",
+      "calculation_method": "the calc method",
+      "timestamp": "the timestamp field",
+      "time_grains": [
+          "day",
+          "week"
+      ],
+      "expression": "a field name or sql expression",
+      "dimensions": [
+				{
+				"entity_name": [
+          "had_discount",
+          "order_country"
+					]
+				}
+      ],
+      "window": null,
+      "resource_type": "metric",
+      "filters": [],
+      "meta": {},
+      "tags": [],
+      "config": {
+          "enabled": true
+      },
+      "unrendered_config": {},
+      "sources": [],
+      "depends_on": {
+          "macros": [],
+          "nodes": [
+              "entity.projet_name.entity_name",
+          ]
+      },
+      "entities": [
+				[
+					"entity_name"
+				]
+			],
+      "metrics": ["used for derived metrics"],
+      "created_at": 1669653027.290001
+  },
+  ```
+</TabItem>
+
+<TabItem value="entityquery" label="Entity queries">
+
+Query an entity using dbt-SQL. Eventually, users will be able to query entities and dynamically generate datasets using a macro (like with metrics), without having to find specific tables or columns. 
+
+```
+select *
+from {{ entities.calculate(
+	entity_list=[...], [Required, one to start]
+       dimensions: [...], [Optional, default is all]
+       metrics: [...], [Optional, default is all at finest grain]
+       filters: ...
+	)}}
+  ```
+</TabItem>
+</Tabs>
+
+### dbt Semantic Layer Connector 
+
+In order to support more data platforms and enhance the user experience, users will be able to connect to a [dbt Cloud-supported data platform](/docs/cloud/connect-data-platform/about-connections) with the dbt Semantic Layer.
+
+Integration partners need to install the [Arrow FlightSQL](https://arrow.apache.org/docs/format/FlightSql.html) JDBC/ODBC driver, which will authenticate with dbt Cloud and the data platform that it queries. 
+
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/connection-architecture.jpg" title="Envisioned connection architecture" />
+
+
+### dbt Semantic Layer API 
+
+dbt Cloud will provide a web API that supports:
+
+- Compiling dbt-SQL queries to return their compiled SQL.
+- Executing dbt-SQL queries and returning the queried results from the data platform.
+
+The API will be a viable integration point with the dbt Semantic Layer. It will be authorized by a [dbt Cloud service token](/docs/dbt-cloud-apis/service-tokens) and eventually support the invocation of dbt commands (e.g., `dbt run`, `dbt test`, etc.) in the future.
+
+
+## Contact us
+
+### For dbt Semantic Layer support
+
+For partner and customer support, please email the [Support team](mailto:support@getdbt.com). Please ensure the message includes:
+
+- "Semantic Layer"
+- The name of the partner software
+- The dbt Cloud account ID of the customer, if you are a partner making the inquiry
+
+### For product and partnerships
+
+If you'd like to become a formal partner, have product feedback/questions, or are interested in integrating, email the [Product and Partnership team](mailto:semantic-layer@dbtlabs.com). 
+
+<!-- rewrite a little and would like to see if i can include a button -->
+
+## Related docs
+
+- [dbt Semantic Layer docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
+- [dbt Metrics docs](https://docs.getdbt.com/docs/building-a-dbt-project/metrics) for more information about its components.
+- [dbt Semantic Layer intro blog](https://www.getdbt.com/blog/dbt-semantic-layer/) and [launch blog](https://www.getdbt.com/blog/frontiers-of-the-dbt-semantic-layer/) to learn more about the product vision and purpose. 
+- [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.
+
+
diff --git a/website/sidebars.js b/website/sidebars.js
index 7a4e961c6be..5012c0c216f 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -1084,6 +1084,7 @@ const sidebarSettings = {
             "guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project",
           ],
         },
+        "guides/dbt-ecosystem/databricks-guides/sl-partner-integration-guide",
       ],
     },
     {

From e96674ae57a9ebaeb6d1b35e9d8be9d0827c44e7 Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Wed, 19 Jul 2023 10:23:17 -0400
Subject: [PATCH 188/703] update spotlight text

---
 website/docs/community/spotlight/fabiyi-opeyemi.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/community/spotlight/fabiyi-opeyemi.md b/website/docs/community/spotlight/fabiyi-opeyemi.md
index dee785cda11..f26ee27910b 100644
--- a/website/docs/community/spotlight/fabiyi-opeyemi.md
+++ b/website/docs/community/spotlight/fabiyi-opeyemi.md
@@ -38,4 +38,4 @@ I've learned how to show empathy as a data professional and be a great engineer
 
 ## Anything else interesting you want to tell us?
 
-Maybe, I will consider DevRel as a career sometime 😀 because of my innate passion and love for community and people. Several folks tell me I'm a strong DevRel talent any product-led company would be lucky to have, so you can reach out if you need someone to drive your community engagement, haha 😂.
+Maybe, I will consider DevRel as a career sometime because of my innate passion and love for community and people. Several folks tell me I'm a strong DevRel talent and a valuable asset for any product-led company. If you need someone to bounce ideas off of or discuss😃 your community engagement efforts, please feel free to reach out.

From 6963cea971257f3fb1a7d415d6eba30421306e0e Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 16:16:19 +0100
Subject: [PATCH 189/703] org tabs better

---
 website/docs/docs/build/metricflow-cli.md | 34 +++++++++++++++--------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 6a49f11878f..0098d470def 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Once you define metrics in your dbt project, you can query metrics, dimensions, dimension values, and validate your configs using the MetricFlow command line (CLI).
 
-## Installation
+# Installation
 
 You can install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install the MetricFlow CLI on Windows or Linux operating systems:
 
@@ -17,7 +17,7 @@ You can install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow#gett
 
 The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
 
-## CLI commands
+# CLI commands
 
 The MetricFlow CLI provides the following commands to retrieve metadata and query metrics. 
 
@@ -33,13 +33,13 @@ To execute the commands, use the `mf` prefix before the command name. For exampl
 - [`tutorial`](#tutorial) &mdash; Dedicated MetricFlow tutorial to help get you started.
 - [`query`](#query) &mdash; Query metrics and dimensions you want to see in the CLI. Refer to [query examples](#query-examples) to help you get started.
 
-### List
+## List
 
 This command retrieves metadata values related to [Metrics](/docs/build/metrics-overview), [Dimensions](/docs/build/dimensions), and [Entities](/docs/build/entities) values. 
 
 For example, if you're trying to retrieve the `name` metadata value for a metric, you can use the following command:
 
-### List metrics
+## List metrics
 
 This command lists the metrics with their available dimensions:
 
@@ -51,7 +51,7 @@ Options:
   --help                 Show this message and exit.
 ```
 
-### List dimensions
+## List dimensions
 
 This command lists all unique dimensions for a metric or multiple metrics. It displays only common dimensions when querying multiple metrics:
 
@@ -63,7 +63,7 @@ Options:
   --help              Show this message and exit.
 ```
 
-### List dimension-values
+## List dimension-values
 
 This command lists all dimension values with the corresponding metric:
 
@@ -79,7 +79,7 @@ Options:
                       of the data (inclusive)
   --help              Show this message and exit.
 ```
-### List entities
+## List entities
 
 This command lists all unique entities:
 
@@ -114,7 +114,7 @@ Options:
   --help                          Show this message and exit.
 ```
 
-### Health checks
+## Health checks
 
 This command performs a health check against the data platform you provided in the configs:
 
@@ -122,7 +122,7 @@ This command performs a health check against the data platform you provided in t
 mf health-checks
 ```
 
-### Tutorial
+## Tutorial
 
 Follow the dedicated MetricFlow tutorial to help you get started:
 
@@ -130,7 +130,7 @@ Follow the dedicated MetricFlow tutorial to help you get started:
 mf tutorial
 ```
 
-### Query
+## Query
 
 Create a new query with MetricFlow, execute that query against the user's data platform, and return the result:
 
@@ -165,7 +165,7 @@ Options:
   ```
 
 
-### Query examples
+## Query examples
 
 The following tabs presents various different types of query examples that you can use to query metrics and dimensions. Select the tab that best suits your needs:
 
@@ -220,6 +220,7 @@ mf query --metrics order_amount --group-by metric_time, is_food_order
 
 </TabItem>
 
+
 <TabItem value="eg3" label="Order/limit">
 
 **Example 3** &mdash; You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records and orders them by `metric_time`, descending.
@@ -297,6 +298,17 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 </TabItem>
 
 
+</Tabs>
+
+
+### Additional query examples
+
+The following tabs presents additional query examples, like exporting to a CSV. Select the tab that best suits your needs:
+
+<Tabs>
+
+
+
 <TabItem value="eg6" label="--explain flag">
 
 **Example 6** &mdash; Add `--explain` to your query to view the SQL generated by MetricFlow. 

From 6f9e3d7c71d71c0ffdd284d307468735c20efcd5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 17:07:46 +0100
Subject: [PATCH 190/703] fix headers

---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 0098d470def..32ec5864b7a 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -91,7 +91,7 @@ Options:
   --help              Show this message and exit.
 ```
 
-### Validate-configs
+## Validate-configs
 
 This command performs validations against the defined semantic model configurations:
 

From 44209776e402d4357730ed16652aff3ba81defec Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Wed, 19 Jul 2023 09:09:27 -0700
Subject: [PATCH 191/703] Update auto-update config

---
 .github/workflows/autoupdate.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/autoupdate.yml b/.github/workflows/autoupdate.yml
index 4105ec6b902..352a34b4b7a 100644
--- a/.github/workflows/autoupdate.yml
+++ b/.github/workflows/autoupdate.yml
@@ -4,9 +4,9 @@ on:
   # This will trigger on all pushes to all branches.
   push: {}
   # Alternatively, you can only trigger if commits are pushed to certain branches, e.g.:
-  # push:
-  #   branches:
-  #     - current
+  push:
+    branches:
+      - current
   #     - unstable
 jobs:
   autoupdate:

From bac3b31d7a19f057f5510976b5392b36717c5086 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 17:12:42 +0100
Subject: [PATCH 192/703] adding cumulative metrics

---
 website/docs/docs/build/about-metricflow.md | 1 +
 website/docs/docs/build/metrics-overview.md | 5 +++--
 website/sidebars.js                         | 1 +
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 6ec7ecfe4b5..d96d477c1fb 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -60,6 +60,7 @@ Metrics, which is a key concept, are functions that combine measures, constraint
 
 MetricFlow supports different metric types:
 
+- [Cumulative](/docs/build/cumulative-metrics) &mdash;  Aggregates a measure over a given window.
 - [Derived](/docs/build/derived) &mdash; An expression of other metrics, which allows you to do calculations on top of metrics.
 - [Ratio](/docs/build/ratio) &mdash; Create a ratio out of two measures, like revenue per customer.
 - [Simple](/docs/build/simple) &mdash; Metrics that refer directly to one measure. 
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index e7271ecf417..103b7a242bc 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -25,7 +25,7 @@ This page explains the different supported metric types you can add to your dbt
 - [Ratio](#ratio-metrics) — Create a ratio out of two measures. 
 -->
 
-<!--not supported for this release
+
 ### Cumulative metrics 
 
 [Cumulative metrics](/docs/build/cumulative) aggregate a measure over a given window. Note that if no window is specified, the window would accumulate the measure over all time. 
@@ -43,7 +43,6 @@ metrics:
     #Omitting window will accumulate the measure over all time
     window: 7 days
 ```
--->
 ### Derived metrics
 
 [Derived metrics](/docs/build/derived) are defined as an expression of other metrics. Derived metrics allow you to do calculations on top of metrics. 
@@ -145,7 +144,9 @@ You can set more metadata for your metrics, which can be used by other tools lat
 ## Related docs
 
 - [Semantic models](/docs/build/semantic-models)
+- [Cumulative](/docs/build/cumulative-metrics)
 - [Derived](/docs/build/derived)
 
 
 
+
diff --git a/website/sidebars.js b/website/sidebars.js
index bf992619dbc..1a1b33ab1db 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -275,6 +275,7 @@ const sidebarSettings = {
               label: "Metrics",
               link: { type: "doc", id: "docs/build/metrics-overview"},
               items: [
+                "docs/build/cumulative",
                 "docs/build/derived",
                 "docs/build/ratio",
                 "docs/build/simple",

From 3f8a9adf536cd8469a7b540880a824ecbc90f9f7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 17:19:13 +0100
Subject: [PATCH 193/703] fix links

---
 website/docs/docs/build/about-metricflow.md | 2 +-
 website/docs/docs/build/metrics-overview.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index d96d477c1fb..5a42fcd7b3e 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -60,7 +60,7 @@ Metrics, which is a key concept, are functions that combine measures, constraint
 
 MetricFlow supports different metric types:
 
-- [Cumulative](/docs/build/cumulative-metrics) &mdash;  Aggregates a measure over a given window.
+- [Cumulative](/docs/build/cumulative) &mdash;  Aggregates a measure over a given window.
 - [Derived](/docs/build/derived) &mdash; An expression of other metrics, which allows you to do calculations on top of metrics.
 - [Ratio](/docs/build/ratio) &mdash; Create a ratio out of two measures, like revenue per customer.
 - [Simple](/docs/build/simple) &mdash; Metrics that refer directly to one measure. 
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 103b7a242bc..b2cadb9116f 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -144,7 +144,7 @@ You can set more metadata for your metrics, which can be used by other tools lat
 ## Related docs
 
 - [Semantic models](/docs/build/semantic-models)
-- [Cumulative](/docs/build/cumulative-metrics)
+- [Cumulative](/docs/build/cumulative)
 - [Derived](/docs/build/derived)
 
 

From 991ff053a24e97b0a6b5b3c5631c1c1d428953ab Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Wed, 19 Jul 2023 09:36:52 -0700
Subject: [PATCH 194/703] Update .github/workflows/autoupdate.yml

---
 .github/workflows/autoupdate.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/autoupdate.yml b/.github/workflows/autoupdate.yml
index 352a34b4b7a..f26abcb6802 100644
--- a/.github/workflows/autoupdate.yml
+++ b/.github/workflows/autoupdate.yml
@@ -2,7 +2,7 @@ name: Auto Update
 
 on:
   # This will trigger on all pushes to all branches.
-  push: {}
+#  push: {}
   # Alternatively, you can only trigger if commits are pushed to certain branches, e.g.:
   push:
     branches:

From 5bbfa6b81dea5b9fb594d36ce96c5021d3c011e7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 17:49:11 +0100
Subject: [PATCH 195/703] Update metrics-overview.md

adding timespine info
---
 website/docs/docs/build/metrics-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index b2cadb9116f..351c674ca8a 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -28,7 +28,7 @@ This page explains the different supported metric types you can add to your dbt
 
 ### Cumulative metrics 
 
-[Cumulative metrics](/docs/build/cumulative) aggregate a measure over a given window. Note that if no window is specified, the window would accumulate the measure over all time. 
+[Cumulative metrics](/docs/build/cumulative) aggregate a measure over a given window. If no window is specified, the window would accumulate the measure over all time. **Note**m, you will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
 ```yaml
 # Cumulative metrics aggregate a measure over a given window. The window is considered infinite if no window parameter is passed (accumulate the measure over all time)

From cc7ea5938a4290b5cf9e23a5e93f1c6c28242719 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 17:51:43 +0100
Subject: [PATCH 196/703] Update cumulative-metrics.md

---
 website/docs/docs/build/cumulative-metrics.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index 77d23d32dce..efdde600635 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -8,6 +8,12 @@ tags: [Metrics, Semantic Layer]
 
 Cumulative metrics aggregate a measure over a given window. If no window is specified, the window is considered infinite and accumulates values over all time.
 
+:::info MetricFlow time spine required
+
+You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
+
+:::
+
 ```yaml
 # Cumulative metrics aggregate a measure over a given window. The window is considered infinite if no window parameter is passed (accumulate the measure over all time)
 metrics:
@@ -24,7 +30,7 @@ metrics:
 
 ### Window options
 
-This section details examples for when you specify and don't specify window options.
+This section details examples of when you specify and don't specify window options.
 
 <Tabs>
 
@@ -56,7 +62,7 @@ metrics:
     window: 7 days
 ```
 
-From the sample yaml above, note the following: 
+From the sample YAML above, note the following: 
 
 * `type`: Specify cumulative to indicate the type of metric. 
 * `type_params`: Specify the measure you want to aggregate as a cumulative metric. You have the option of specifying a `window`, or a `grain to date`.  
@@ -142,7 +148,7 @@ metrics:
 ```yaml
 metrics: 
   name: revenue_monthly_grain_to_date #For this metric, we use a monthly grain to date 
-  description: Monthly revenue using a grain to date of 1 month (think of this as a monthly resetting point) 
+  description: Monthly revenue using grain to date of 1 month (think of this as a monthly resetting point) 
   type: cumulative 
   type_params: 
     measures: 

From 014175fabafc408ec19fba0b0f866ee46ea2fdf6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 18:07:31 +0100
Subject: [PATCH 197/703] Update website/docs/docs/build/metricflow-cli.md

---
 website/docs/docs/build/metricflow-cli.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 32ec5864b7a..23ac3c14079 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -41,6 +41,9 @@ For example, if you're trying to retrieve the `name` metadata value for a metric
 
 ## List metrics
 
+```bash
+mf list 
+
 This command lists the metrics with their available dimensions:
 
 ```bash

From 5a74d72318938843de735ad0937b3bdb27ecd090 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 19 Jul 2023 18:07:54 +0100
Subject: [PATCH 198/703] Update website/docs/docs/build/metricflow-cli.md

---
 website/docs/docs/build/metricflow-cli.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 23ac3c14079..9e934e9ccca 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -37,7 +37,6 @@ To execute the commands, use the `mf` prefix before the command name. For exampl
 
 This command retrieves metadata values related to [Metrics](/docs/build/metrics-overview), [Dimensions](/docs/build/dimensions), and [Entities](/docs/build/entities) values. 
 
-For example, if you're trying to retrieve the `name` metadata value for a metric, you can use the following command:
 
 ## List metrics
 

From ff192709adeedb2bc637ddb5bdd4928946552e48 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 19 Jul 2023 18:35:12 +0100
Subject: [PATCH 199/703] links

---
 website/docs/docs/dbt-versions/experimental-features.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/website/docs/docs/dbt-versions/experimental-features.md b/website/docs/docs/dbt-versions/experimental-features.md
index 35c64146149..3d7454215f7 100644
--- a/website/docs/docs/dbt-versions/experimental-features.md
+++ b/website/docs/docs/dbt-versions/experimental-features.md
@@ -21,3 +21,8 @@ To enable or disable experimental features:
 2. Find Experimental features at the bottom of Your Profile page.
 3. Click **Beta** to toggle the features on or off as shown in the following image.
    ![Experimental features](/img/docs/dbt-versions/experimental-feats.png)
+
+## Beta terms and conditions
+
+By using or enabling beta features/products, you agree to the Beta terms and conditions. Download the [Beta terms and conditions](/docs/cloud/about-cloud/assets/beta-t&c.docx) for more info.  
+

From 85ef0e8e359a1f5577277059d86fbf10ec2d9b41 Mon Sep 17 00:00:00 2001
From: Anders Swanson <anders.swanson@dbtlabs.com>
Date: Wed, 19 Jul 2023 12:59:52 -0400
Subject: [PATCH 200/703] snowflake dynamic tables: first draft

---
 .../resource-configs/snowflake-configs.md     | 67 ++++++++++++++++++-
 1 file changed, 66 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 42ee3635089..154f8795eea 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -77,7 +77,7 @@ select ...
 
 ```
   
-In this example, you can set up a query tag to be applied to every query with the model's name. 
+In this example, you can set up a query tag to be applied to every query with the model's name.
   
 ```sql 
 
@@ -341,3 +341,68 @@ In the configuration format for the model SQL file:
 </File>
 
 </VersionBlock>
+
+<VersionBlock firstVersion="1.6">
+
+## Dynamic Tables
+
+[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's flavor of Materialized Views. The `CREATE DYNAMIC TABLE` ([docs](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table)) statement requires the following parameters `TARGET_LAG` and `WAREHOUSE`, so a dbt-snowflake user must also provide these.
+
+You can create a dynamic table by editing _one_ of these files:
+
+- the SQL file for your model
+- the `dbt_project.yml` configuration file
+
+The following examples create a dynamic table:
+
+<File name='models/YOUR_MODEL_NAME.sql'>
+
+```sql
+{{
+  config(
+    materialized = 'dynamic_table',
+    warehouse = 'MY_WAREHOUSE',
+    target_lag = '10 min',
+  )
+}}
+```
+
+</File>
+
+<File name='dbt_project.yml'>
+
+```yaml
+models:
+  path:
+    materialized: dynamic_table
+    warehouse: MY_WAREHOUSE
+    target_lag: '10 min'
+```
+
+</File>
+
+### Limitations
+
+#### Changing materialization to and from "dynamic_table"
+
+Swapping an already materialized model to be a dynamic table and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again.
+
+To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="dynamic_table"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
+
+<File name='my_model.sql'>
+
+```yaml
+
+{{ config(
+    materialized="table" # or any model type eg view, incremental
+) }}
+
+```
+
+</File>
+
+#### Altering `target_lag` or `warehouse`
+
+Currently, changing either the `target_lag` or `warehouse` parameters for a pre-existing Dynamic Table (DT) will result in the Dynamic Table being dropped and replaced, rather than altered.
+
+</VersionBlock>

From e1354eb2c70de5ad144cefac4100d6a8ebdb13a8 Mon Sep 17 00:00:00 2001
From: Anders Swanson <anders.swanson@dbtlabs.com>
Date: Wed, 19 Jul 2023 13:47:39 -0400
Subject: [PATCH 201/703] updated limitations

add postgres limitation
---
 .../resource-configs/postgres-configs.md      | 20 +++++++++++++++++++
 .../resource-configs/redshift-configs.md      |  8 ++++++++
 2 files changed, 28 insertions(+)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index eb9108ad431..d4f6958ae27 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -148,4 +148,24 @@ models:
 ```
 </File>
 
+### Limitations
+
+#### Changing materialization to and from "materialized_view"
+
+Swapping an already materialized model to be a materialized view and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again.
+
+To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="materialized_view"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
+
+<File name='my_model.sql'>
+
+```yaml
+
+{{ config(
+    materialized="table" # or any model type eg view, incremental
+) }}
+
+```
+
+</File>
+
 </VersionBlock>
diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index a0ebf7e88df..00dfe517cea 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -136,4 +136,12 @@ models:
 ```
 </File>
 
+### Limitations
+
+#### Changing materialization from "materialized_view" to table or view
+
+Swapping a materialized view to be a table or view is not supported. The workaround is to manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
+
+To illustrate, assume for a materialized view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="view"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCASE` on the data warehouse before trying the model again.
+
 </VersionBlock>

From dc5ca3bfe0cd14f6756f012ed9b4b62cff2ecd17 Mon Sep 17 00:00:00 2001
From: Anders Swanson <anders.swanson@dbtlabs.com>
Date: Wed, 19 Jul 2023 14:00:48 -0400
Subject: [PATCH 202/703] BQ MVs are not supported

---
 .../resource-configs/bigquery-configs.md      | 45 -------------------
 1 file changed, 45 deletions(-)

diff --git a/website/docs/reference/resource-configs/bigquery-configs.md b/website/docs/reference/resource-configs/bigquery-configs.md
index c425fd5b94b..1df21af98e4 100644
--- a/website/docs/reference/resource-configs/bigquery-configs.md
+++ b/website/docs/reference/resource-configs/bigquery-configs.md
@@ -764,48 +764,3 @@ Views with this configuration will be able to select from objects in `project_1.
 
 The `grant_access_to` config is not thread-safe when multiple views need to be authorized for the same dataset. The initial `dbt run` operation after a new `grant_access_to` config is added should therefore be executed in a single thread. Subsequent runs using the same configuration will not attempt to re-apply existing access grants, and can make use of multiple threads.
 
-<VersionBlock firstVersion="1.6">
-
-
-## Materialized view
-
-The BigQuery adapter supports [materialized views](https://cloud.google.com/bigquery/docs/materialized-views-intro) and refreshes them for every subsequent `dbt run` you execute. For more information, see [Refresh Materialized Views](https://cloud.google.com/bigquery/docs/materialized-views-manage#refresh) in the Google docs.
-
-Materialized views support the optional configuration `on_configuration_change` with the following values: 
-- `apply` (default) &mdash; attempts to update the existing database object if possible, avoiding a complete rebuild. The following changes can be applied without the need to rebuild the materialized view:
-  - enable_refresh 
-  - refresh_interval_minutes 
-  - max_staleness
-- `skip` &mdash; allows runs to continue while also providing a warning that the model was skipped
-- `fail` &mdash; forces runs to fail if a change is detected in a materialized view 
-
-You can create a materialized view by editing _one_ of these files:
-- the SQL file for your model
-- the `dbt_project.yml` configuration file
-
-The following examples create a materialized view: 
-
-<File name='models/YOUR_MODEL_NAME.sql'>
-
-```sql
-{{
-  config(
-    materialized = 'materialized_view',
-    on_configuration_change = 'apply',
-  )
-}}
-```
-
-</File>
-
-
-<File name='dbt_project.yml'>
-
-```yaml 
-models:
-  path:
-    materialized: materialized_view
-```
-</File>
-
-</VersionBlock>

From 9ba9816c94876a8f104fd7ce6019fc78a1f64d2c Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Wed, 19 Jul 2023 14:07:34 -0400
Subject: [PATCH 203/703] More updates based on feedback

---
 .../manage-access/enterprise-permissions.md   | 199 +-----------------
 .../snippets/enterprise-permissions-table.md  |  83 ++++----
 2 files changed, 47 insertions(+), 235 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index de73ba02e9a..ed2ffb68cee 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -20,202 +20,9 @@ The following permission sets are available for assignment in dbt Cloud Enterpri
 can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
 can be associated with more than one permission sets.
 
-:::info Role permisisons
-
-The following table lists the standard roles and permissions associated with them in dbt Cloud. Some roles, such as _Stakeholder_ have more nuanced access to features. View the individual role descriptions for more information beyond the table definitions.
-
-::: 
-
-<Snippet src="enterprise-permissions-table" />
-
-### Account Admin
-
-- **Has permissions on:** Authorized projects, account-level settings
-- **License restrictions:** must have a developer license
-
-Account Admins have unrestricted access to dbt Cloud accounts. Users with Account Admin permissions can:
-
-- Create, delete, and modify all projects in an account
-- Create, delete, and modify Connections
-- Create, delete, and modify Environments
-- Create, delete, and modify Groups
-- Create, delete, and modify Group Memberships
-- Create, delete, and modify Jobs
-- Create, delete, and modify outbound webhook subscriptions
-- Create, delete, and modify Repositories
-- Manage Notification Settings
-- Manage account-level [artifacts](/docs/deploy/artifacts)
-- Run and cancel jobs
-- Use the IDE
-- View and modify Account Settings
-- Generate [service tokens](/docs/dbt-cloud-apis/service-tokens), such as for [API usage](/docs/dbt-cloud-apis/overview)
-
-### Security Admin
-
-- **Has permissions on:** Account-level settings
-- **License restrictions:** must have a Developer or an IT license
-
-Security Admins have access to modify certain account-level settings. Users with Security Admin permissions can:
-
-- View and modify Account Settings such as:
-    - View, invite, and modify account users
-    - Create, delete, and modify Groups
-    - Create, delete, and modify License Mappings
-    - Create and modify SSO Configurations
-    - View and export Audit Logs
-    - Create, delete, and modify IP Restrictions
-
-### Billing Admin
-
-- **Has permissions on:** Account-level settings
-- **License restrictions:** must have a Developer or an IT license
-
-Billing Admins have access to modify certain account-level settings related to billing. Users with Billing Admin permissions can:
-
-- View and modify **Account Settings** such as:
-    - View billing information
-    - Modify billing information (accounts on the Team plan)
-        - This includes modifying Developer Seat counts for the Account
-
-### Project Creator
-- **Has permissions on:** Authorized projects, account-level settings
-- **License restrictions:** must have a developer license
-
-Project Creators have write and read-only access to dbt Cloud accounts, but do not have the permissions required to modify SSO settings and account integrations. 
-
-Users with Project Creator permissions can:
-
-- View Account Settings
-- View and modify project users
-- Create, delete and modify all projects in an account
-- Create, delete, and modify Connections
-- Create, delete, and modify Environments
-- Create, delete, and modify Jobs
-- Create, delete, and modify Repositories
-- Run and cancel jobs
-- Use the IDE
-- View Groups
-- View Notification Settings
-
-### Account Viewer
-
-- **Has permissions on:** Authorized projects, account-level settings
-- **License restrictions:** must have a developer license
-
-Account Viewers have read-only access to dbt Cloud accounts. Users with Account Viewer permissions can:
-- View all projects in an account
-- View Account Settings
-- View account-level artifacts
-- View Connections
-- View Environments
-- View Groups
-- View Group Memberships
-- View Jobs
-- View Notification Settings
-- View Repositories
-
-### Admin
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Admins have unrestricted access to _projects_ in dbt Cloud accounts which they are members of.
-Admins can perform the following actions in projects they are assigned to:
-- Create, delete, and modify Repositories
-- Create, delete, and modify Connections
-- Create, delete, and modify Environments
-- Create, delete, and modify Group Memberships
-- Create, delete, and modify Jobs
-- Create, delete, and modify outbound webhook subscriptions
-- Run and cancel jobs
-- Use the IDE
-- View project details
-
-### Git Admin
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Git Admins can perform the following actions in projects they are assigned to:
-- Create, delete, and modify Repositories
-- View Connections
-- View Environments
-- View Jobs
-- View project details
-
-### Database Admin
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Database Admins can perform the following actions in projects they are assigned to:
-- Create, delete, and modify Connections
-- View Environments
-- View Jobs
-- View project details
-- View Repositories
-
-### Team Admin
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Team Admins can perform the following actions in projects they are assigned to:
-- View Groups
-- View Environments
-- View Jobs
-- View project details
-- View Repositories
-
-### Job Admin
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Job Admins can perform the following actions in projects they are assigned to:
-- Create, delete, and modify Jobs
-- Run and cancel jobs
-- View connections
-- View, edit, and create environments
-- View historical runs
-
-### Job Viewer
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Job Viewers can perform the following actions in projects they are assigned to:
-- View environments
-- View historical runs
-- View job definitions
-
-### Developer
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Developers can perform the following actions in projects they are assigned to:
-- Configure personal developer credentials
-- Create, delete, and modify Jobs
-- Create, delete, and modify outbound webhook subscriptions
-- Run and cancel jobs
-- Use the IDE
-
-### Analyst
-- **Has permissions on:** Authorized projects
-- **License restrictions:** must have a developer license
-
-Analysts can perform the following actions in projects they are assigned to:
-- Configure personal developer credentials
-- Configure environmental variables
-- View connections
-- View environments
-- View historical runs
-- View job definitions
-- Use the IDE
-
-
-### Stakeholder
-- **Has permissions on:** Authorized projects
-- **License restrictions:** Intended for use with Read-Only licenses, but may be used with Developer licenses.
-
-Stakeholders can perform the following actions in projects they are assigned to:
-- View generated documentation
-- View generated source freshness reports
-- View the Read-Only dashboard
+
+<Snippet path="enterprise-permissions-table" />
+
 
 ## Diagram of the Permission Sets
 
diff --git a/website/snippets/enterprise-permissions-table.md b/website/snippets/enterprise-permissions-table.md
index eda75a6e396..3fec61264ac 100644
--- a/website/snippets/enterprise-permissions-table.md
+++ b/website/snippets/enterprise-permissions-table.md
@@ -2,56 +2,61 @@
 Key:
 
 * (W)rite &mdash; Create new or modify existing. Includes `send`, `create`, `delete`, `allocate`, `modify`, and `read`.
-* (M)odify &mdash; Change existing fields but can not create new. Includes `delete` and `read`.
-* (R)ead &mdash; Can view but canm not create or change any fields.
-* ❌ = No access to the feature
-* ✅ = Can access feature that does not use C/M/D/V permissions
+* (R)ead &mdash; Can view but can not create or change any fields.
 
-## Account role permissions
+Permissions: 
+
+* Account-level permissions &mdash; Permissions related to management of the dbt Cloud account. For example, billing and account settings.
+* Project-level permissions &mdash; Permissions related to the projects in dbt Cloud. For example, repos and access to the IDE. 
+
+## Account roles
+Account roles are primarily designed for managing the dbt Cloud account. Permissions to alter the account settings (for example, generating service tokens, inviting users, configuring SSO). They may also have access to project-level permissions as well. The **Account Admin** role is the highest level of access that can be assigned.  
 
 ### Account permissions
 
-|                   | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
-|-------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
-| Account settings  |     W         |               |        R        |       R        |   R    |
-| Audit logs        |     R         |               |                 |       R        |        |
-| Auth provider     |     W         |               |                 |       W        |   R    |
-| Billing           |     W         |       W       |                 |                |   R    |
-| Invitations       |     W         |               |        W        |       W        |   R    |
-| IP restrictions   |     W         |               |                 |       W        |   R    |
-| Members           |     W         |               |        W        |       W        |   R    |
-| Project (create)  |     W         |               |        W        |                |        |
-| Public models     |     R         |       R       |        R        |       R        |   R    |
-| Service tokens    |     W         |               |                 |       R        |        |
-| Webhooks          |     W         |               |                 |                |        |
+| Account-level permission| Account Admin | Billing admin | Project creator | Security admin | Viewer | 
+|:-------------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
+| Account settings        |     W         |               |        R        |       R        |   R    |
+| Audit logs              |     R         |               |                 |       R        |        |
+| Auth provider           |     W         |               |                 |       W        |   R    |
+| Billing                 |     W         |       W       |                 |                |   R    |
+| Invitations             |     W         |               |        W        |       W        |   R    |
+| IP restrictions         |     W         |               |                 |       W        |   R    |
+| Members                 |     W         |               |        W        |       W        |   R    |
+| Project (create)        |     W         |               |        W        |                |        |
+| Public models           |     R         |       R       |        R        |       R        |   R    |
+| Service tokens          |     W         |               |                 |       R        |        |
+| Webhooks                |     W         |               |                 |                |        |
 
 ### Project permissions
 
-|                   | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
-|-------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
-| Connections       |       W       |               |       W         |                |   R    |
-| Credentials       |       W       |               |       W         |                |   R    |
-| Custom environment variables|  W  |               |       W         |                |   R    |
-| dbt adapters      |       W       |               |       W         |                |   R    |
-| Develop (IDE)     |       W       |               |       W         |                |        |
-| Environments      |       W       |               |       W         |                |   R    |
-| Groups            |       W       |               |       R         |       W        |   R    |
-| Jobs              |       W       |               |       W         |                |   R    |
-| Licenses          |       W       |               |       W         |       W        |   R    |
-| Metadata          |       R       |               |       R         |                |   R    |
-| Permissions       |       W       |               |       W         |       W        |   R    |
-| Profile           |       W       |               |       W         |                |   R    |
-| Projects          |       W       |               |       W         |       R        |   R    |
-| Repositories      |       W       |               |       W         |                |   R    |
-| Runs              |       W       |               |       W         |                |   R    |
-| Semantic Layer Config|    W       |               |       W         |                |   R    |
+|Project-level permission | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
+|:-------------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
+| Connections             |       W       |               |       W         |                |   R    |
+| Credentials             |       W       |               |       W         |                |   R    |
+| Custom env variables    |       W       |               |       W         |                |   R    |
+| dbt adapters            |       W       |               |       W         |                |   R    |
+| Develop (IDE)           |       W       |               |       W         |                |        |
+| Environments            |       W       |               |       W         |                |   R    |
+| Groups                  |       W       |               |       R         |       W        |   R    |
+| Jobs                    |       W       |               |       W         |                |   R    |
+| Licenses                |       W       |               |       W         |       W        |   R    |
+| Metadata                |       R       |               |       R         |                |   R    |
+| Permissions             |       W       |               |       W         |       W        |   R    |
+| Profile                 |       W       |               |       W         |                |   R    |
+| Projects                |       W       |               |       W         |       R        |   R    |
+| Repositories            |       W       |               |       W         |                |   R    |
+| Runs                    |       W       |               |       W         |                |   R    |
+| Semantic Layer Config   |    W          |               |       W         |                |   R    |
 
 
 ## Project role permissions
 
+The project roles are intended for users who will be working within the projects in various capacities. They'll primarily have access to project-level permissions such as repos and the IDE, but may also have some account-level permisions.
+
 ### Account permissions
 
-|                          | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+| Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
 | Account settings         |   R   |         |      R         |           |     R     |           |             |          |                |             |     R      |        |
 | Auth provider            |       |         |                |           |           |           |             |          |                |             |            |        |
@@ -65,11 +70,11 @@ Key:
 
 ### Project permissions
 
-|                          | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+|Project-level permission  | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
 | Connections              |   W   |    R    |       W        |     R     |     R     |     R     |             |          |                |     R       |     R      |        |
 | Credentials              |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
-| Custom environment variables|  W |    W    |       W        |     W     |     W     |     W     |      R      |          |                |     R       |     W      |        |
+| Custom env variables     |  W    |    W    |       W        |     W     |     W     |     W     |      R      |          |                |     R       |     W      |        |
 | dbt adapters             |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
 | Develop (IDE)            |   W   |    W    |                |     W     |           |           |             |          |                |             |            |        |
 | Environments             |   W   |    R    |       R        |     R     |     R     |     W     |      R      |          |                |     R       |     R      |        |

From dcc6c5c5277355dc4e3d8469332ace3ca84cfca7 Mon Sep 17 00:00:00 2001
From: Jessica <jessica.twitty-shuler@starburstdata.com>
Date: Wed, 19 Jul 2023 15:52:45 -0400
Subject: [PATCH 204/703] Apply small edits

---
 .../docs/quickstarts/starburst-galaxy-qs.md   | 28 +++++++++----------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/website/docs/quickstarts/starburst-galaxy-qs.md b/website/docs/quickstarts/starburst-galaxy-qs.md
index d9bd3b98a43..33228710509 100644
--- a/website/docs/quickstarts/starburst-galaxy-qs.md
+++ b/website/docs/quickstarts/starburst-galaxy-qs.md
@@ -68,7 +68,7 @@ Using Starburst Galaxy, you can create tables and also transform them with dbt.
     ```
 
 ## Connect Starburst Galaxy to the Amazon S3 bucket {#connect-to-s3-bucket}
-If your Starburst Galaxy instance is not already connected to your S3 bucket, you need to create a cluster, create a catalog that connects to the S3 bucket, associate the new catalog (your data source) to your new cluster, and configure privilege settings. 
+If your Starburst Galaxy instance is not already connected to your S3 bucket, you need to create a cluster, configure a catalog that allows Starburst Galaxy to connect to the S3 bucket, add the catalog to your new cluster, and configure privilege settings.
 
 In addition to Amazon S3, Starburst Galaxy supports many other data sources. To learn more about them, you can refer to the [Catalogs overview](https://docs.starburst.io/starburst-galaxy/catalogs/index.html) in the Starburst Galaxy docs.  
 
@@ -79,11 +79,11 @@ In addition to Amazon S3, Starburst Galaxy supports many other data sources. To
 
     When done, click **Create cluster**.
 
-1. Create a catalog. Click **Catalogs** on the left sidebar of the Starburst Galaxy UI, then click **Create catalog** in the main body of the page. 
-2. On the **Create a data source** page, select the Amazon S3 tile. 
-3. In the **Name and description** section of the **Amazon S3** page, fill out the fields. 
-4. In the **Authentication to S3** section of the **Amazon S3** page, select the [AWS (S3) authentication mechanism](#prerequisites) you chose to connect with.
-5. In the **Metastore configuration** section, set these options:
+3. Create a catalog. Click **Catalogs** on the left sidebar of the Starburst Galaxy UI, then click **Create catalog** in the main body of the page. 
+4. On the **Create a data source** page, select the Amazon S3 tile. 
+5. In the **Name and description** section of the **Amazon S3** page, fill out the fields. 
+6. In the **Authentication to S3** section of the **Amazon S3** page, select the [AWS (S3) authentication mechanism](#prerequisites) you chose to connect with.
+7. In the **Metastore configuration** section, set these options:
     - **Default S3 bucket name** &mdash; Enter the name of your S3 bucket you want to access.
     - **Default directory name** &mdash; Enter the folder name of where the Jaffle Shop data lives in the S3 bucket. This is the same folder name you used in [Load data to an Amazon S3 bucket](#load-data-to-s3).
     - **Allow creating external tables** &mdash; Enable this option.
@@ -93,19 +93,19 @@ In addition to Amazon S3, Starburst Galaxy supports many other data sources. To
 
     <Lightbox src="/img/quickstarts/dbt-cloud/starburst-galaxy-config-s3.png" title="Amazon S3 connection settings in Starburst Galaxy" />
 
-7. Click **Test connection**. This verifies that Starburst Galaxy can access your S3 bucket. 
-8. Click **Connect catalog** if the connection test passes.
+8. Click **Test connection**. This verifies that Starburst Galaxy can access your S3 bucket. 
+9. Click **Connect catalog** if the connection test passes.
     <Lightbox src="/img/quickstarts/dbt-cloud/test-connection-success.png" title="Successful connection test" />
 
-9. On the **Set permissions** page, click **Skip**. You can add permissions later if you want.
-10. On the **Add to cluster** page, choose the cluster you want to add the data source to from the dropdown and click **Add to cluster**.
-11. Add the location privilege for your S3 bucket to your role in Starburst Galaxy. Click **Access control > Roles and privileges** on the left sidebar of the Starburst Galaxy UI. Then, in the **Roles** table, click the role name **accountadmin**. 
+10. On the **Set permissions** page, click **Skip**. You can add permissions later if you want.
+11. On the **Add to cluster** page, choose the cluster you want to add the catalog to from the dropdown and click **Add to cluster**.
+12. Add the location privilege for your S3 bucket to your role in Starburst Galaxy. Click **Access control > Roles and privileges** on the left sidebar of the Starburst Galaxy UI. Then, in the **Roles** table, click the role name **accountadmin**. 
 
     If you're using an existing Starburst Galaxy cluster and don't have access to the accountadmin role, then select a role that you do have access to.
 
     To learn more about access control, refer to [Access control](https://docs.starburst.io/starburst-galaxy/security/access-control.html) in the Starburst Galaxy docs. 
-1. On the **Roles** page, click the **Privileges** tab and click **Add privilege**.
-2. On the **Add privilege** page, set these options:
+13. On the **Roles** page, click the **Privileges** tab and click **Add privilege**.
+14. On the **Add privilege** page, set these options:
     - **What would you like to modify privileges for?** &mdash; Choose **Location**.
     - **Enter a storage location provide** &mdash; Enter the storage location of _your S3 bucket_ and the folder of where the Jaffle Shop data lives. Make sure to include the `/*` at the end of the location. 
     - **Create SQL** &mdash; Enable the option. 
@@ -115,7 +115,7 @@ In addition to Amazon S3, Starburst Galaxy supports many other data sources. To
     <Lightbox src="/img/quickstarts/dbt-cloud/add-privilege.png" title="Add privilege to accountadmin role" />
 
 ## Create tables with Starburst Galaxy
-To query the Jaffle Shop data with Starburst Galaxy, you need to create tables using the Jaffle Shop data that you [loaded to your S3 bucket](#load-data-to-s3). You can do this (and run any SQL statement) from the [query editor](https://docs.starburst.io/starburst-galaxy/query/index.html). 
+To query the Jaffle Shop data with Starburst Galaxy, you need to create tables using the Jaffle Shop data that you [loaded to your S3 bucket](#load-data-to-s3). You can do this (and run any SQL statement) from the [query editor](https://docs.starburst.io/starburst-galaxy/query/query-editor.html). 
 
 1. Click **Query > Query editor** on the left sidebar of the Starburst Galaxy UI. The main body of the page is now the query editor. 
 2. Configure the query editor so it queries your S3 bucket. In the upper right corner of the query editor, select your cluster in the first gray box and select your catalog in the second gray box:

From 92fdc0f2f9883ef791a59591a6a657a942f9431b Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Wed, 19 Jul 2023 14:11:01 -0700
Subject: [PATCH 205/703] Create blog for ChatGPT (#3748)

resolves  https://github.com/dbt-labs/docs.getdbt.com/issues/3367

## What are you changing in this pull request and why?

Adding blog post by @pbritosa-sage along with @jasnonaz

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

---------

Co-authored-by: Jason Ganz <jason.ganz@dbtlabs.com>
Co-authored-by: gwen windflower <gwenwindflower@gmail.com>
---
 website/blog/2023-07-17-GPT-and-dbt-test.md   | 213 ++++++++++++++++++
 website/blog/authors.yml                      |  10 +
 ...-on-the-importance-of-naming.md => src.md} |   0
 .../2023-07-17-GPT-and-dbt-test/image1.png    | Bin 0 -> 159022 bytes
 .../2023-07-17-GPT-and-dbt-test/image2.png    | Bin 0 -> 124206 bytes
 .../2023-07-17-GPT-and-dbt-test/image3.png    | Bin 0 -> 324117 bytes
 .../static/img/blog/authors/pedro_brito.jpeg  | Bin 0 -> 123646 bytes
 7 files changed, 223 insertions(+)
 create mode 100644 website/blog/2023-07-17-GPT-and-dbt-test.md
 rename website/blog/{2021-11-23-on-the-importance-of-naming.md => src.md} (100%)
 create mode 100644 website/static/img/blog/2023-07-17-GPT-and-dbt-test/image1.png
 create mode 100644 website/static/img/blog/2023-07-17-GPT-and-dbt-test/image2.png
 create mode 100644 website/static/img/blog/2023-07-17-GPT-and-dbt-test/image3.png
 create mode 100644 website/static/img/blog/authors/pedro_brito.jpeg

diff --git a/website/blog/2023-07-17-GPT-and-dbt-test.md b/website/blog/2023-07-17-GPT-and-dbt-test.md
new file mode 100644
index 00000000000..84f756919a5
--- /dev/null
+++ b/website/blog/2023-07-17-GPT-and-dbt-test.md
@@ -0,0 +1,213 @@
+---
+title: "Create dbt Documentation and Tests 10x faster with ChatGPT"
+description: "You can use ChatGPT to infer the context of verbosely named fields from database table schemas."
+slug: create-dbt-documentation-10x-faster-with-ChatGPT
+
+authors: [pedro_brito_de_sa]
+
+tags: [analytics craft, data ecosystem]
+hide_table_of_contents: true
+
+date: 2023-07-18
+is_featured: true
+---
+
+Whether you are creating your pipelines into dbt for the first time or just adding a new model once in a while, **good documentation and testing should always be a priority** for you and your team. Why do we avoid it like the plague then? Because it’s a hassle having to write down each individual field, its description in layman terms and figure out what tests should be performed to ensure the data is fine and dandy. How can we make this process faster and less painful?
+
+By now, everyone knows the wonders of the GPT models for code generation and pair programming so this shouldn’t come as a surprise. But **ChatGPT really shines** at inferring the context of verbosely named fields from database table schemas. So in this post I am going to help you 10x your documentation and testing speed by using ChatGPT to do most of the leg work for you.
+
+<!--truncate-->
+
+As a one-person Analytics team at [Sage](http://www.hellosage.com/) I had to create our dbt pipelines from the ground up. This meant 30+ tables of internal facts and dimensions + external data into a Staging Layer, plus all of the following layers of augmented models and Mart tables. After the fact, we are talking about 3500+ lines of YAML that I was NOT excited to get started on. Fortunately for me, this was February 2023 and ChatGPT had just come out. And boy, was I glad to have it. After a good dose of “prompt engineering” I managed to get most of my documentation and tests written out, only needing a few extra tweaks.
+
+Writing this article as of July 2023, and now powered by GPT-4 and not GPT 3.5, it is already easier to get the same results I did, so here are my learnings that I hope everyone can replicate.
+
+## Use verbose tables with verbose fields
+
+ChatGPT can only infer so much, so tables with names and fields that resemble encryption keys are unlikely to be good for this approach. In this example we are going to use this table:
+
+```sql
+create or replace TRANSIENT TABLE STAGING.BASE.STG_STAFF_MEMBER (
+      ID NUMBER(38,0),
+      CREATEDATETIME TIMESTAMP_NTZ(9),
+      UPDATEDATETIME TIMESTAMP_NTZ(9),
+      VERSION NUMBER(38,0),
+      FIRSTNAME VARCHAR(16777216),
+      JOBTITLE VARCHAR(16777216),
+      LASTNAME VARCHAR(16777216),
+      MIDDLENAME VARCHAR(16777216),
+      ISCAREADMIN BOOLEAN,
+      ISARCHIVED BOOLEAN,
+      ADDRESSID VARCHAR(16777216),
+      ENTERPRISEID VARCHAR(16777216),
+      ISDELETED BOOLEAN
+);
+
+```
+
+---
+
+I copied this definition from Snowflake, and I would recommend that you always include both the name and type of the fields to get better results. This is especially helpful if you want to make sure ChatGPT correctly distinguishes between text, number, boolean or array fields, which may not be clear from the name alone.
+
+## Start out with simple prompts
+
+We all know how ChatGPT can digest very complex prompts, but as this is a tool for you to get the best results for your use case, I would recommend starting with simple, but specific, prompts.
+
+Opening ChatGPT with GPT4, my first prompt is usually along these lines:
+
+<Lightbox src="/img/blog/2023-07-17-GPT-and-dbt-test/image1.png" title="ChatGPT request for YAML file" />
+
+And the output of this simple prompt is nothing short of amazing:
+
+```yaml
+version: 2
+
+models:
+  - name: STAFF_MEMBER
+    description: This table contains information about the staff members.
+    columns:
+      - name: ID
+        description: The unique identifier for the staff member.
+        tests:
+          - unique
+          - not_null
+      - name: CREATEDATETIME
+        description: The timestamp when the record was created.
+        tests:
+          - not_null
+      - name: UPDATEDATETIME
+        description: The timestamp when the record was last updated.
+      - name: VERSION
+        description: Version number of the record.
+      - name: FIRSTNAME
+        description: The first name of the staff member.
+        tests:
+          - not_null
+      - name: JOBTITLE
+        description: The job title of the staff member.
+        tests:
+          - not_null
+      - name: LASTNAME
+        description: The last name of the staff member.
+        tests:
+          - not_null
+      - name: MIDDLENAME
+        description: The middle name of the staff member.
+      - name: ISCARADMIN
+        description: Boolean value indicating if the staff member is a care administrator.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+      - name: ISARCHIVED
+        description: Boolean value indicating if the staff member record is archived.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+      - name: COMMUNITYID
+        description: Identifier for the community of the staff member.
+      - name: ENTERPRISEID
+        description: Identifier for the enterprise of the staff member.
+      - name: ISDELETED
+        description: Boolean value indicating if the staff member record is deleted.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+```
+
+Back in my day (5 months ago), ChatGPT with GPT 3.5 didn’t have much context on what a dbt-style YAML file was, so the result was a funky, non-dbt YAML format. If you are using GPT 3.5 and the results you get from the first prompt don’t compile in dbt, I would recommend an extra step I took which is to pass your understanding of what a dbt-style YAML file is. You can do this simply by copy-pasting a piece of a publicly available YAML file like [this one from the Jaffle Shop example](https://github.com/dbt-labs/jaffle_shop/blob/main/models/schema.yml), or a piece of YAML from your own code base.
+
+A prompt for it would look something like:
+
+<Lightbox src="/img/blog/2023-07-17-GPT-and-dbt-test/image2.png" title="ChatGPT request to memorize dbt-style YAML" />
+
+## Specify details on generic tests in your prompts
+
+dbt has four built-in generic tests: unique, not_null, accepted_values and relationships. My approach to adding details on these to the prompts is one of ‘more is better’, as in it’s faster to just delete tests that I don’t need in certain fields than having to add them manually.
+
+ChatGPT will throw unique and not_null tests for mostly all columns that have ‘id’ in their name, and in my experience it adds them to a few more columns than it should, which is usually quick to fix.
+
+Accepted_values and relationships are slightly trickier but the model can be adjusted to account for these in fairly simple ways. My tips are:
+
+- accepted_values: add extra detail to the schema like an extra keyword like ‘CATEGORICAL’ after the data type
+- relationships: bind the relationships to fields with specific names. For better results, pass on a list of valid table names from your schema that it can refer to.
+
+One way of doing this is with a prompt like this:
+
+<Lightbox src="/img/blog/2023-07-17-GPT-and-dbt-test/image3.png" title="ChatGPT request to write dbt-style YAML with generic tests" />
+
+Which results in the following output:
+
+```yaml
+version: 2
+
+models:
+  - name: STAFF_MEMBER
+    description: This table contains information about the staff members.
+    columns:
+      - name: ID
+        description: The unique identifier for the staff member.
+        tests:
+          - unique
+          - not_null
+      - name: CREATEDATETIME
+        description: The timestamp when the record was created.
+        tests:
+          - not_null
+      - name: UPDATEDATETIME
+        description: The timestamp when the record was last updated.
+      - name: VERSION
+        description: Version number of the record.
+      - name: FIRSTNAME
+        description: The first name of the staff member.
+        tests:
+          - not_null
+      - name: JOBTITLE
+        description: The job title of the staff member. This is a categorical field.
+        tests:
+          - not_null
+          - accepted_values:
+              values: ['Job Title 1', 'Job Title 2', 'Job Title 3'] # replace these with actual job titles
+      - name: LASTNAME
+        description: The last name of the staff member.
+        tests:
+          - not_null
+      - name: MIDDLENAME
+        description: The middle name of the staff member.
+      - name: ISCARADMIN
+        description: Boolean value indicating if the staff member is a care administrator.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+      - name: ISARCHIVED
+        description: Boolean value indicating if the staff member record is archived.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+      - name: COMMUNITYID
+        description: Identifier for the community of the staff member.
+        tests:
+          - relationships:
+              to: STAGING.COMMUNITY.ID # replace with actual reference table
+              field: ID
+      - name: ENTERPRISEID
+        description: Identifier for the enterprise of the staff member.
+        tests:
+          - relationships:
+              to: STAGING.ENTERPRISE.ID # replace with actual reference table
+              field: ID
+      - name: ISDELETED
+        description: Boolean value indicating if the staff member record is deleted.
+        tests:
+          - accepted_values:
+              values: ['true', 'false']
+```
+
+Even though it doesn’t magically guess the categories of your variables or your relationships, it is definitely helpful to have some placeholders in the right places.
+
+As an add-on, giving the model a short description of the data models and the tables you are working with will help it fine tune your results.
+
+## Wrap-Up
+
+Creating documentation is still a very manual job, and this approach only works for one table at a time (maybe you can be the one leveraging the OpenAI API and creating a webapp that processes multiple tables at once?). However, ChatGPT can clearly cut a lot of time in these tasks.
+
+I hope that these simple tips help you be more motivated and efficient in creating documentation and tests for your data models. And remember: verbosity in - verbosity out!
diff --git a/website/blog/authors.yml b/website/blog/authors.yml
index 72e747cc577..6d222e8a543 100644
--- a/website/blog/authors.yml
+++ b/website/blog/authors.yml
@@ -373,6 +373,16 @@ pat_kearns:
   name: Pat Kearns
   organization: dbt Labs
 
+pedro_brito_de_sa:
+  image_url: /img/blog/authors/pedro_brito.jpeg
+  job_title: Product Analyst
+  links:
+    - icon: fa-linkedin
+      url: https://www.linkedin.com/in/pbritosa/
+  name: Pedro Brito de Sa
+  organization: Sage
+
+
 rastislav_zdechovan:
   image_url: /img/blog/authors/rastislav-zdechovan.png
   job_title: Analytics Engineer
diff --git a/website/blog/2021-11-23-on-the-importance-of-naming.md b/website/blog/src.md
similarity index 100%
rename from website/blog/2021-11-23-on-the-importance-of-naming.md
rename to website/blog/src.md
diff --git a/website/static/img/blog/2023-07-17-GPT-and-dbt-test/image1.png b/website/static/img/blog/2023-07-17-GPT-and-dbt-test/image1.png
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From 78e412b197a438a546f23990cb406a417bcca18a Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Wed, 19 Jul 2023 15:49:35 -0700
Subject: [PATCH 206/703] Fix link in debugging-schema-names.md (#3766)

## What are you changing in this pull request and why?

Found this bad link using the new link checker!

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 website/docs/guides/legacy/debugging-schema-names.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/legacy/debugging-schema-names.md b/website/docs/guides/legacy/debugging-schema-names.md
index 12daacb1f2d..6c869b5f8af 100644
--- a/website/docs/guides/legacy/debugging-schema-names.md
+++ b/website/docs/guides/legacy/debugging-schema-names.md
@@ -16,7 +16,7 @@ You can also follow along via this video:
 Do a file search to check if you have a macro named `generate_schema_name` in the `macros` directory of your project.
 
 #### I do not have a macro named `generate_schema_name` in my project
-This means that you are using dbt's default implementation of the macro, as defined [here](https://github.com/dbt-labs/dbt-core/blob/main/core/dbt/include/global_project/macros/get_custom_name/get_custom_schema.sql#L17-L30)
+This means that you are using dbt's default implementation of the macro, as defined [here](https://github.com/dbt-labs/dbt-core/blob/main/core/dbt/include/global_project/macros/get_custom_name/get_custom_schema.sql#L47C1-L60)
 
 ```sql
 {% macro generate_schema_name(custom_schema_name, node) -%}

From 66266fa290c1e62e79569e936656a372ac6452f4 Mon Sep 17 00:00:00 2001
From: Bart Schuijt <schuijt.bart@gmail.com>
Date: Thu, 20 Jul 2023 11:01:21 +0200
Subject: [PATCH 207/703] Update modify-owner-column.md

Small typo fix.
---
 website/docs/faqs/Docs/modify-owner-column.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/faqs/Docs/modify-owner-column.md b/website/docs/faqs/Docs/modify-owner-column.md
index db06e5af6cf..8395a182bb9 100644
--- a/website/docs/faqs/Docs/modify-owner-column.md
+++ b/website/docs/faqs/Docs/modify-owner-column.md
@@ -8,7 +8,7 @@ id: modify-owner-column
 
 Due to the nature of the field, you won't be able to change the owner column in your generated documentation. 
  
-The _owner_ field in `dbt-docs` is pulled from database metdata (`catalog.json`), meaning the owner of that table in the database. With the exception of exposures, it's not pulled from an `owner` field set within dbt.
+The _owner_ field in `dbt-docs` is pulled from database metadata (`catalog.json`), meaning the owner of that table in the database. With the exception of exposures, it's not pulled from an `owner` field set within dbt.
  
 Generally, dbt's database user owns the tables created in the database. Source tables are usually owned by the service responsible for ingesting/loading them. 
  

From 0cfbd7218c2ddb1d876b7969c51ca119149b9c9b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 10:52:59 +0100
Subject: [PATCH 208/703] adding tc pdf

---
 .../docs/dbt-versions/experimental-features.md  |   3 +--
 website/static/assets/beta-tc.pdf               | Bin 0 -> 31198 bytes
 2 files changed, 1 insertion(+), 2 deletions(-)
 create mode 100644 website/static/assets/beta-tc.pdf

diff --git a/website/docs/docs/dbt-versions/experimental-features.md b/website/docs/docs/dbt-versions/experimental-features.md
index 3d7454215f7..718974f7821 100644
--- a/website/docs/docs/dbt-versions/experimental-features.md
+++ b/website/docs/docs/dbt-versions/experimental-features.md
@@ -24,5 +24,4 @@ To enable or disable experimental features:
 
 ## Beta terms and conditions
 
-By using or enabling beta features/products, you agree to the Beta terms and conditions. Download the [Beta terms and conditions](/docs/cloud/about-cloud/assets/beta-t&c.docx) for more info.  
-
+By using or enabling features that aren't yet in general release ("Beta Features"), you agree to its terms and conditions. Download the [Beta terms and conditions](/assets/beta-tc.pdf) for more info.  
diff --git a/website/static/assets/beta-tc.pdf b/website/static/assets/beta-tc.pdf
new file mode 100644
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zqK%TZ*&o0!KEt0Q;2(14YHwr=1=K!9D4+!X&w|gw%nV@0H^%?V#sCE2{8{j=|6>D6
zFVNF}+vpkSfnxbzHUK><6OecFUp9Jr2B!b*kC_dqLjRqQiG>LmssG!?{(slY$PT0x
z|6lnS*;$x@Qt)480rUXof0T8!*8}Ri{T~*yf|)xoGC=i}x3K}9M*c(ycnbNWwEu~g
YgQK3k<G*4Bpl4%afFdCgk`adbKTqZ#Hvj+t

literal 0
HcmV?d00001


From 0b9073f4d84421bdc52cae7bb12d0ad2da030f58 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 11:20:58 +0100
Subject: [PATCH 209/703] Update product-lifecycles.md

---
 website/docs/docs/dbt-versions/product-lifecycles.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/product-lifecycles.md b/website/docs/docs/dbt-versions/product-lifecycles.md
index f676c6af2eb..7567af70a97 100644
--- a/website/docs/docs/dbt-versions/product-lifecycles.md
+++ b/website/docs/docs/dbt-versions/product-lifecycles.md
@@ -14,7 +14,7 @@ Any dbt feature will fall into one of the following lifecycle states:
 
 ### dbt Cloud
 
-- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These may not be feature-complete or fully stable. There may still be some planned additions and modifications to product behaviors while in Beta. Breaking changes may occur – although we will do our best to communicate them in advance, we may not always be able to do so. Beta features may not be fully documented, technical support may be limited, and service level objectives (SLOs) may not be provided.
+- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These may not be feature-complete or fully stable. There may still be some planned additions and modifications to product behaviors while in Beta. Breaking changes may occur – although we will do our best to communicate them in advance, we may not always be able to do so. Beta features may not be fully documented, technical support may be limited, and service level objectives (SLOs) may not be provided. Download the [Beta terms and conditions](/assets/beta-tc.pdf) for more info.
 
 - **Preview (Private or Public):**  Preview features are stable and can be considered for production deployments. There may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality to Preview features that is not backward compatible. Preview features include documentation, technical support, and include service level objectives (SLOs). Features in Preview are generally provided at no extra cost, although they may become paid features in their Generally Available state.
 

From de55ceb6596a8933d403c1be190393c7e66d2296 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 12:15:09 +0100
Subject: [PATCH 210/703] add partial

---
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md | 12 +++++++++++-
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md  | 11 ++++++++++-
 website/sidebars.js                                 |  2 +-
 website/snippets/_sl-plan-info.md                   |  1 +
 4 files changed, 23 insertions(+), 3 deletions(-)
 create mode 100644 website/snippets/_sl-plan-info.md

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index bc5472a2c01..9cc07518705 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -26,7 +26,17 @@ You can use the dbt Semantic Layer for a variety of tools and applications of da
 * Data discovery and cataloging
 * Machine learning and data science
 
-During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools. 
+<!-- this partial lives here: https://github.com/dbt-labs/docs.getdbt.com/website/snippets/_sl-plan-info. Use it on diff pages and to tailor the message depending which instance can access the SL and what product lifecycle we're in. -->
+
+import Features from '/snippets/_sl-plan-info.md'
+
+<Features
+cycle="public beta"
+product="dbt Semantic Layer"
+plan="dbt Cloud Team and Enterprise"
+instance="hosted in North America"
+/>
+<br /><br />
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index d79e4ddf6fb..e690078cd60 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -21,8 +21,17 @@ Moving metric definitions out of the BI layer and into the modeling layer allows
 To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
 
 ## Explore the dbt Semantic Layer
+<!-- this partial lives here: https://github.com/dbt-labs/docs.getdbt.com/website/snippets/_sl-plan-info. Use it on diff pages and to tailor the message depending which instance can access the SL and what product lifecycle we're in. -->
 
-During [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta), the dbt Semantic Layer is accessible to all dbt Cloud Team and Enterprise multi-tenant plans [hosted](/docs/cloud/about-cloud/regions-ip-addresses) in North America. It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.
+import Features from '/snippets/_sl-plan-info.md'
+
+<Features
+cycle="public beta"
+product="dbt Semantic Layer"
+plan="dbt Cloud Team and Enterprise"
+instance="hosted in North America"
+/>
+<br /><br />
 
 <div className="grid--3-col">
 
diff --git a/website/sidebars.js b/website/sidebars.js
index 5012c0c216f..17259b70867 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -1084,7 +1084,7 @@ const sidebarSettings = {
             "guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project",
           ],
         },
-        "guides/dbt-ecosystem/databricks-guides/sl-partner-integration-guide",
+        "guides/dbt-ecosystem/sl-partner-integration-guide",
       ],
     },
     {
diff --git a/website/snippets/_sl-plan-info.md b/website/snippets/_sl-plan-info.md
new file mode 100644
index 00000000000..23d259a346b
--- /dev/null
+++ b/website/snippets/_sl-plan-info.md
@@ -0,0 +1 @@
+<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, the {props.product} is accessible to <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span> 

From cbb016fbcd66d0b6ef220520ce13e1d27ece3164 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 12:51:51 +0100
Subject: [PATCH 211/703] changes

---
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md | 13 ++++++++-----
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md         |  4 ++--
 website/docs/docs/dbt-cloud-apis/sl-manifest.md     |  2 +-
 .../use-dbt-semantic-layer/avail-sl-integrations.md |  8 ++++++--
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md  |  6 +++---
 .../docs/use-dbt-semantic-layer/quickstart-sl.md    |  6 +++++-
 .../docs/use-dbt-semantic-layer/sl-architecture.md  |  4 +---
 website/docs/guides/migration/sl-migration.md       |  2 +-
 website/snippets/_legacy-sl-callout.md              |  2 +-
 website/snippets/_new-sl-changes.md                 |  2 +-
 website/snippets/_new-sl-setup.md                   |  2 +-
 website/snippets/_v2-sl-prerequisites.md            |  2 +-
 12 files changed, 31 insertions(+), 22 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 9cc07518705..89429c18d55 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -1,8 +1,8 @@
 ---
-title: "Semantic Layer APIs"
+title: "Semantic Layer API"
 id: sl-api-overview
 description: "Integrate and query using the Semantic Layer API."
-tags: [Semantic Layer, APIs]
+tags: [Semantic Layer, API]
 hide_table_of_contents: true
 ---
 
@@ -38,19 +38,22 @@ instance="hosted in North America"
 />
 <br /><br />
 
-<div className="grid--3-col">
+<div className="grid--2-col">
 
 <Card
     title="JDBC API"
-    body="Use a JDBC driver to query metrics in downstream tools."
+    body="Use a JDBC driver to query metrics and dimensions in downstream tools, while also providing standard metadata functionality."
     link="/docs/dbt-cloud-apis/sl-jdbc"
     icon="dbt-bit"/>
 
+<!-- 
+commenting out until it's available
 <Card
     title="GraphQL API"
     body="Use GraphQL to query metrics in downstream tools."
     link="/docs/dbt-cloud-apis/sl-graphql"
     icon="dbt-bit"/>
+-->
 
 <Card
     title="Semantic manifest"
@@ -62,4 +65,4 @@ instance="hosted in North America"
 
 ## Authentication
 
-dbt Cloud authorizes requests to the dbt Semantic Layer APIs. You need to provide an environment ID, host, and [service account tokens](/docs/dbt-cloud-apis/service-tokens).
+dbt Cloud authorizes requests to the dbt Semantic Layer API. You need to provide an environment ID, host, and [service account tokens](/docs/dbt-cloud-apis/service-tokens).
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 25163eb05d6..440e0bfbd8a 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -2,7 +2,7 @@
 title: "JDBC"
 id: sl-jdbc
 description: "Integrate and use the JDBC API to query your metrics."
-tags: [Semantic Layer, APIs]
+tags: [Semantic Layer, API]
 ---
 
 
@@ -24,7 +24,7 @@ A JDBC driver is a software component enabling a Java application to interact wi
 - You can embed the driver into your application stack as needed, and you can use dbt Labs' [example project](https://github.com/dbt-labs/example-semantic-layer-clients) for reference.
 - If you’re a partner or user building a homegrown application, you’ll need to install an AWS root CA to the Java Trust [documentation](https://www.amazontrust.com/repository/) (specific to Java and JDBC call).
 
-Partners of dbt Labs can use the JDBC API to build integrations in their tools with the dbt Semantic Layer
+dbt Labs partners can use the JDBC API to build integrations in their tools with the dbt Semantic Layer
 
 
 ## Using the JDBC API
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 031e634668b..cde72760f8c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -882,6 +882,6 @@ QUESTIONS:
 
 ## Related docs
 
-- [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview)
+- [dbt Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview)
 - [About dbt artifacts](/reference/artifacts/dbt-artifacts)
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 08fc1cacd6f..8ebf96278df 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -4,6 +4,8 @@ id: avail-sl-integrations
 description: "Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem."
 tags: [Semantic Layer]
 sidebar_label: "Available integrations"
+meta:
+  api_name: dbt Semantic Layer API
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -12,9 +14,11 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
+
+
 Discover the diverse range of data applications that seamlessly integrate with the powerful dbt Semantic Layer, powered by MetricFlow &mdash; from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-Query dbt metrics via external integrations using the sophisticated [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) (JDBC, GraphQL, and Discovery APIs). With these APIs, you can query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+<span>Query dbt metrics via external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
 
@@ -30,7 +34,7 @@ For information on the partner integrations, their documentation, and more &mdas
 
 ## Related docs
 
-- [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to learn how to integrate with the JDBC, GraphQL, and Discovery APIs to query your metrics in downstream tools.
+- <span><a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC to query your metrics in downstream tools.
 
 </VersionBlock>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index e690078cd60..e36622c1910 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -37,7 +37,7 @@ instance="hosted in North America"
 
 <Card
     title="Get started with the dbt Semantic Layer and MetricFlow"
-    body="Build and define metrics with MetricFlow, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
+    body="Build and define metrics with MetricFlow, set up the dbt Semantic Layer, and query them using the Semantic Layer API."
     link="/docs/use-dbt-semantic-layer/quickstart-sl"
     icon="dbt-bit"/>
 
@@ -60,8 +60,8 @@ instance="hosted in North America"
     icon="dbt-bit"/>
 
 <Card
-    title="Semantic Layer APIs"
-    body="Use the Semantic Layer APIs to query metrics in downstream tools for consistent, reliable data metrics."
+    title="Semantic Layer API"
+    body="Use the Semantic Layer API to query metrics in downstream tools for consistent, reliable data metrics."
     link="/docs/dbt-cloud-apis/sl-api-overview"
     icon="dbt-bit"/>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 70e6300df02..658b828cfc6 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -4,6 +4,8 @@ id: quickstart-sl
 description: "Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs."
 sidebar_label: "Get started with the dbt Semantic Layer"
 tags: [Semantic Layer]
+meta:
+  api_name: dbt Semantic Layer API
 ---
 
 <VersionBlock firstVersion="1.6">
@@ -150,7 +152,9 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query APIs
 
-This step requires you to connect to the [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview). Once you've connected to an API, you should then set up and query metrics in your downstream tool of choice. Refer to [dbt Semantic Layer Apis](/docs/dbt-cloud-apis/sl-api-overview) and [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+<span>This step requires you to connect to the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC to query your metrics in downstream tools. <br /><br />
+
+Once you've connected to an API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 8e76c8daca0..f350ab6996c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -16,9 +16,7 @@ At a high level, the dbt Semantic Layer allows you to define metrics and query t
 | **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with the YAML specifications. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
 | **MetricFlow Server**| A proprietary server that takes metric requests and generates optimized SQL for the specific data platform. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **Semantic Layer APIs** | The interfaces that allow users to submit metric queries include the MetricFlow CLI, JDBC API, and GraphQL. These APIs also serve as the foundation for building first-class integrations with various tools. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
-
-
+| **Semantic Layer API** | The interfaces that allow users to submit metric queries include the MetricFlow CLI and JDBC API. They also serve as the foundation for building first-class integrations with various tools. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 
 ## Related questions
 
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index fc4bcf76204..f9b34ebd35d 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -157,6 +157,6 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 - [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
 - [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)
-- [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview)
+- [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
 - Developer workflow demo
diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index 67517bb4342..622e8290181 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,6 +1,6 @@
 :::tip Upgrade to access the new dbt Semantic Layer
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing new APIs, and making it more efficient to define/query metrics. This means the legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing a new API, and making it more efficient to define/query metrics. This means the legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
 
 **Who does this affect?** Anyone who uses the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 
diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index a5f8c2210bb..6773dbdabf5 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -3,6 +3,6 @@
 
 The dbt Semantic Layer has been re-released with [significant improvements](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. 
 
-The new version is available in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and introduces [MetricFlow](/docs/build/about-metricflow), an essential component. It also includes new semantic elements, better governance, improved efficiency, easier data access, and new Semantic Layer APIs.
+The new version is available in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and introduces [MetricFlow](/docs/build/about-metricflow), an essential component. It also includes new semantic elements, better governance, improved efficiency, easier data access, and new Semantic Layer API.
 
 :::
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 3e8fdf5d867..65f38fd625d 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -12,7 +12,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
     * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
 5. Select the deployment environment you want for the Semantic Layer
 6. You should see connection information that allows you to connect to downstream tools. 
-    * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.
+    * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
 8. Save & copy your environment ID, service token, and host for inputting into a downstream tool
 9. Great job, you've configured the Semantic Layer 🎉! 
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index d602e5d0d3b..b8ec7f6aa02 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -8,7 +8,7 @@
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli)
   * Note &mdash; After installing the package, make sure you run at least one model.
-- Set up the [Semantic Layer API](/docs/use-dbt-semantic-layer/sl-api-overview) in the integrated tool to import metric definitions 
+- Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
   * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
 
 

From dbe0a7664c61016258a2975643d379e9280b9541 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 14:17:56 +0100
Subject: [PATCH 212/703] Update
 website/docs/docs/dbt-versions/experimental-features.md

---
 website/docs/docs/dbt-versions/experimental-features.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/experimental-features.md b/website/docs/docs/dbt-versions/experimental-features.md
index 718974f7821..02a8157d4cd 100644
--- a/website/docs/docs/dbt-versions/experimental-features.md
+++ b/website/docs/docs/dbt-versions/experimental-features.md
@@ -24,4 +24,4 @@ To enable or disable experimental features:
 
 ## Beta terms and conditions
 
-By using or enabling features that aren't yet in general release ("Beta Features"), you agree to its terms and conditions. Download the [Beta terms and conditions](/assets/beta-tc.pdf) for more info.  
+By using or enabling features that aren't yet in general release ("Beta Features"), you agree to the [Beta Features Terms and Conditions](/assets/beta-tc.pdf).

From 3588d0fd705746543366c398d6cdfcf0aae5d818 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 14:18:36 +0100
Subject: [PATCH 213/703] Update
 website/docs/docs/dbt-versions/product-lifecycles.md

---
 website/docs/docs/dbt-versions/product-lifecycles.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/product-lifecycles.md b/website/docs/docs/dbt-versions/product-lifecycles.md
index 7567af70a97..65aad0ad333 100644
--- a/website/docs/docs/dbt-versions/product-lifecycles.md
+++ b/website/docs/docs/dbt-versions/product-lifecycles.md
@@ -14,7 +14,7 @@ Any dbt feature will fall into one of the following lifecycle states:
 
 ### dbt Cloud
 
-- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These may not be feature-complete or fully stable. There may still be some planned additions and modifications to product behaviors while in Beta. Breaking changes may occur – although we will do our best to communicate them in advance, we may not always be able to do so. Beta features may not be fully documented, technical support may be limited, and service level objectives (SLOs) may not be provided. Download the [Beta terms and conditions](/assets/beta-tc.pdf) for more info.
+- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These may not be feature-complete or fully stable. There may still be some planned additions and modifications to product behaviors while in Beta. Breaking changes may occur – although we will do our best to communicate them in advance, we may not always be able to do so. Beta features may not be fully documented, technical support may be limited, and service level objectives (SLOs) may not be provided. Download the [Beta Features Terms and Conditions](/assets/beta-tc.pdf) for more info.
 
 - **Preview (Private or Public):**  Preview features are stable and can be considered for production deployments. There may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality to Preview features that is not backward compatible. Preview features include documentation, technical support, and include service level objectives (SLOs). Features in Preview are generally provided at no extra cost, although they may become paid features in their Generally Available state.
 

From fe0f51ff1283a1deef337c2755147457400a4e6e Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 14:29:13 +0100
Subject: [PATCH 214/703] gsg updates

---
 website/dbt-versions.js                       |  22 +---
 .../docs/docs/build/build-metrics-intro.md    |   2 +-
 website/docs/docs/build/sl-getting-started.md | 110 ++++++++++++------
 .../06-July-2023/sl-revamp-beta.md            |   2 +-
 .../use-dbt-semantic-layer/quickstart-sl.md   |  15 +--
 website/sidebars.js                           |   1 -
 website/snippets/_v2-sl-prerequisites.md      |   5 +-
 7 files changed, 90 insertions(+), 67 deletions(-)

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 324a98b80a0..227b620ae86 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -186,23 +186,7 @@ exports.versionedPages = [
   {
     "page": "docs/build/ratio",
     "firstVersion": "1.6",
-  },
-  {
-    "category": "docs/use-dbt-semantic-layer/sl-api-overview",
-    "firstVersion": "1.6",
-  },
-	{
-    "category": "docs/use-dbt-semantic-layer/graphql",
-    "firstVersion": "1.6",
-  },
-	{
-    "category": "docs/use-dbt-semantic-layer/jdbc",
-    "firstVersion": "1.6",
-  },
-  {
-    "category": "docs/use-dbt-semantic-layer/sl-manifest",
-    "firstVersion": "1.6",
-  },
+  }
 ]
 
 exports.versionedCategories = [
@@ -213,9 +197,5 @@ exports.versionedCategories = [
   {
     "category": "Build your metrics",
     "firstVersion": "1.6",
-  },
-  {
-    "category": "Semantic Layer APIs",
-    "firstVersion": "1.6",
   }
 ]
diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 99dcd8c52c8..19b31cab931 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -51,7 +51,7 @@ Before you start, keep the following considerations in mind:
 
  <Card
     title="Get started with the dbt Semantic Layer"
-    body="Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer APIs"
+    body="Use this guide to build and define metrics, set up the dbt Semantic Layer, and query them using the Semantic Layer API"
     link="/docs/use-dbt-semantic-layer/quickstart-sl"
     icon="dbt-bit"/>
 
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index d1275352a21..c46fa916f70 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -4,43 +4,52 @@ title: Get started with MetricFlow
 description: "Learn how to create your first semantic model and metric."
 sidebar_label: Get started with MetricFlow
 tags: [Metrics, Semantic Layer]
+meta:
+  api_name: dbt Semantic Layer API
 ---
 
 This getting started page recommends a workflow to help you get started creating your first metrics. Here are the following steps you'll take:
 
-- [Create a semantic model](#create-a-semantic-model)
-- [Create your metrics](#create-your-metrics)
-- [Test and query your metrics](#test-and-query-your-metrics)
+Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
+
+- [Create a semantic model](#create-a-semantic-model) with MetricFlow
+- [Define metrics](#define-metrics) with MetricFlow
+- [Test metrics](#test-metrics) with the MetricFlow
+- [Run a production job](#run-a-production-job) in dbt Cloud
+- [Set up dbt Semantic Layer](#set-up-dbt-semantic-layer) in dbt Cloud
+- [Connect and query API](#connect-and-query-api) in dbt Cloud
+- [Test and query with the CLI](#test-and-query-with-the-cli) 
+  * For Developer plans or dbt Core users only
 
 ## Prerequisites
 
-- Use the [command line (CLI)](/docs/core/about-the-cli) and have a dbt project and repository set up. 
-  * Note: Support for dbt Cloud and integrations coming soon.
-- Your dbt production environment must be on [dbt Core v1.6](/docs/dbt-versions/core) or higher. Support for the development environment coming soon.
-- Have a dbt project connected to Snowflake or Postgres. 
-  * Note: Support for BigQuery, Databricks, and Redshift coming soon.
 - Have an understanding of key concepts in [MetricFlow](/docs/build/about-metricflow), which powers the revamped dbt Semantic Layer.
-- Recommended &mdash; dbt Labs recommends you install the [MetricFlow CLI package](https://github.com/dbt-labs/metricflow) to test your metrics.
+- Have both your production and development environments running dbt version 1.6 or higher 
+- Use Snowflake, BigQuery, Databricks, and Redshift data platform
+- A successful run in the environment where your Semantic Layer is configured
+  * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
+- To query with dbt Cloud:
+  * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
+  * Set up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions.
+- To query locally:
+  * dbt Core or Developer accounts must manually install the [MetricFlow CLI package](/docs/build/metricflow-cli) to test or query their metrics. After installing the package, make sure you run at least one model. <br />
+
 
 :::tip 
 New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
 :::
 
-## Install MetricFlow
-
-Before you begin, make sure you install the `metricflow` and [dbt adapter](/docs/supported-data-platforms) via PyPI in the CLI. To install them, open the command line interface (CLI) and use the pip install command `pip install "dbt-metricflow[your_adapter_name]"`.
-
-Note that specifying `[your_adapter_name]` is required.  This is because you must install MetricFlow as an extension of a dbt adapter. For example, for a Snowflake adapter, run `pip install "dbt-metricflow[snowflake]"`.
- 
-Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon).
-
 ## Create a semantic model
 
-MetricFlow, which powers the dbt Semantic Layer, has two main objects: [semantic models](/docs/build/semantic-models) and [metrics](/docs/build/metrics-overview). You can think of semantic models as nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
+Before you begin, we recommend you learn about more about [MetricFlow](/docs/build/about-metricflow) and its key concepts. There are two main objects: 
+
+- [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
+- [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
 
-This step will guide you through setting up your semantic models, which consists of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
+This step will guide you through setting up your semantic models, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures),  in your editor of choice.
 
 1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
+
 ```yaml
 semantic_models:
   - name: transactions
@@ -87,11 +96,13 @@ group by metric_time_day, country  -- dimensions
   ```
 :::
 
-## Create your metrics
+## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). You can define metrics in the same YAML files as your semantic models, or create a new file.
+Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). 
 
-The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
+1. You can define metrics in the same YAML files as your semantic models, or create a new file.
+
+2. The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
 
 ```yaml
 ---
@@ -101,28 +112,61 @@ metrics:
     type_params:
     measure: transaction_amount_usd
 ```
+ 
+3. Save your code and make sure you test your metrics before committing them to your repository.
+
+To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
-Interact and test your metric using the CLI before committing it to your MetricFlow repository.
+## Test metrics 
 
-## Test and query your metrics
+The following steps explain how to test your metrics using the [MetricFlow CLI](/docs/build/metricflow-cli) (dbt Cloud IDE support coming soon).
 
-Follow these steps to test and query your metrics using MetricFlow:
+dbt Core or Developer plan user should refer to [Test and query with the CLI](#test-and-query-with-the-cli) for detailed steps. 
 
-1. If you haven't done so already, make sure you [install MetricFlow](#install-metricflow).
+1. To test your metrics, make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
+3. Run `mf validate-configs` to validate the changes before committing them
 
-2. Run `mf --help` to confirm you have MetricFlow installed, and to see the available commands. If you don't have the CLI installed, run `pip install --upgrade "dbt-metricflow[your_adapter_name]"`.  For example, if you have a Snowflake adapter, run `pip install --upgrade "dbt-metricflow[snowflake]"`.
+## Run a production job
 
-3. Save your files and run `mf validate-configs` to validate the changes before committing them
+Once you’ve defined metrics in your dbt project, you can perform a job run in your deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
+
+1. Go to **Deploy** in the navigation header
+2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.
+3. Your metric should appear as a red node in the dbt Cloud IDE and dbt directed acyclic graphs (DAG).
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/metrics_red_nodes.png" width="85%" title="DAG with metrics appearing as a red node" />
+
+## Set up dbt Semantic Layer
+
+import SlSetUp from '/snippets/_new-sl-setup.md';  
+
+<SlSetUp/>
+
+## Connect and query API
+
+To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
+
+1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
+2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+
+## Test and query with the CLI
+
+Before you begin, make sure you install the `metricflow` and [dbt adapter](/docs/supported-data-platforms) via PyPI in the CLI. To install them, open the command line interface (CLI) and use the pip install command `pip install "dbt-metricflow[your_adapter_name]"`.
 
-4. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions you want to see in the CLI.
+  * Note that specifying `[your_adapter_name]` is required.  This is because you must install MetricFlow as an extension of a dbt adapter. For example, for a Snowflake adapter, run `pip install "dbt-metricflow[snowflake]"`.
 
-5. Verify that the metric values are what you expect. You can view the generated SQL if you enter `--explain` in the CLI. 
+To test your metrics locally:
 
-6. Then commit your changes to push them to your git repo.
+1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
+3. Run `mf validate-configs` to validate the changes before committing them
 
-<!--## Troubleshooting
+To query your metrics locally:
 
-ANY COMMON TROUBLESHOOTING QUESTIONS?-->
+1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
+2. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
+3. Commit and merge the code changes that contain the metric definitions.
 
 ## Related docs
 
diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index ec2ebeca7f1..d8bb7c71940 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -12,7 +12,7 @@ dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/do
 - [MetricFlow](/docs/build/about-metricflow) is a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
 - New Semantic Layer infrastructure that enables support for more data platforms (Snowflake, Databricks, BigQuery, Redshift, and soon more), along with improved performance.
 - New and improved [developer workflows](/guides/migration/sl-migration), governance, and collaboration features.
-- New [Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) to query metrics and build integrations.
+- New [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) using JDBC to query metrics and build integrations.
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 658b828cfc6..01a3630e6d2 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -24,7 +24,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - [Create your metrics](#create-your-metrics) with MetricFlow
 - [Run your production job](#run-your-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#setup) in dbt Cloud
-- [Connect and Query APIs](#connect-and-query-apis)
+- [Connect and Query API](#connect-and-query-apis)
 
 ## Prerequisites
 
@@ -113,14 +113,15 @@ metrics:
  
 3. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
 
+## Test metrics
 
-### Test metrics
+The following steps explain how to test and manually query your metrics using the [MetricFlow CLI](/docs/build/metricflow-cli) only. dbt Cloud IDE support will be available soon.
 
-The following steps explain how to query and test your metrics, which currently is only available via the CLI. However, IDE support will be available soon:
-
-1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions in the IDE.
-3. Run `mf validate-configs` to validate the changes before committing them.
-3. Commit and merge the code changes that contain the metric definitions.
+1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+2. Run `mf validate-configs` to validate the changes before committing them.
+3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
+4. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
+5. Commit and merge the code changes that contain the metric definitions.
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
diff --git a/website/sidebars.js b/website/sidebars.js
index 6bd51847291..e0f617e8e0f 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -487,7 +487,6 @@ const sidebarSettings = {
           link: { type: "doc", id: "docs/dbt-cloud-apis/sl-api-overview" },
           items: [
             "docs/dbt-cloud-apis/sl-jdbc",
-            "docs/dbt-cloud-apis/sl-graphql",
             "docs/dbt-cloud-apis/sl-manifest",
           ],
         },
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index b8ec7f6aa02..bd10ad76c72 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -6,10 +6,9 @@
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
-- Install the [MetricFlow CLI](/docs/build/metricflow-cli)
-  * Note &mdash; After installing the package, make sure you run at least one model.
+- Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need access to the Semantic Layer APIs.<br />
+  * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need to have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
 
 
 </VersionBlock>

From 29779859f2f95fce715ae387881597b9e539bcf8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 14:35:43 +0100
Subject: [PATCH 215/703] gsg nit

---
 website/docs/docs/build/sl-getting-started.md    |  2 +-
 .../docs/use-dbt-semantic-layer/quickstart-sl.md | 16 +++++++++-------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index c46fa916f70..805174d74fc 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -17,7 +17,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - [Test metrics](#test-metrics) with the MetricFlow
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#set-up-dbt-semantic-layer) in dbt Cloud
-- [Connect and query API](#connect-and-query-api) in dbt Cloud
+- [Connect and query API](#connect-and-query-api) with dbt Cloud
 - [Test and query with the CLI](#test-and-query-with-the-cli) 
   * For Developer plans or dbt Core users only
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 01a3630e6d2..e24773b3513 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -21,10 +21,11 @@ MetricFlow is a powerful component within the dbt Semantic Layer that helps user
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
-- [Create your metrics](#create-your-metrics) with MetricFlow
-- [Run your production job](#run-your-production-job) in dbt Cloud
-- [Set up dbt Semantic Layer](#setup) in dbt Cloud
-- [Connect and Query API](#connect-and-query-apis)
+- [Define metrics](#define-metrics) with MetricFlow
+- [Test metrics](#test-metrics) with MetricFlow 
+- [Run a production job](#run-a-production-job) in dbt Cloud
+- [Set up dbt Semantic Layer](#setup) in dbt Cloud 
+- [Connect and query API](#connect-and-query-api) with dbt Cloud
 
 ## Prerequisites
 
@@ -151,11 +152,12 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 <SlSetUp/>
 
 
-## Connect and query APIs
+## Connect and query API
 
-<span>This step requires you to connect to the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC to query your metrics in downstream tools. <br /><br />
+To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
 
-Once you've connected to an API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
+2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 

From 38f943ac078bea4e83abcab8de862d5b6015bb37 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 14:43:59 +0100
Subject: [PATCH 216/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index 4aa1e273f33..628fcc95938 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -24,6 +24,6 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 ### Browser sessions
 
-A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. dbt Cloud will not automatically log you out after a set time. However, if you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
+A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. If you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
 
 If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which might vary depending on your identity provider (IdP).

From 219c42f7ca02d4ea83da6215ed59c255dea3d1a6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 14:44:41 +0100
Subject: [PATCH 217/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index 628fcc95938..88004a1f6ab 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -26,4 +26,4 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. If you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
 
-If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/manage-access/set-up-snowflake-oauth), you can customize your session time, which might vary depending on your identity provider (IdP).
+If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/git/connect-github#personally-authenticate-with-github), you can customize your session time, which might vary depending on your identity provider (IdP).

From d1edf3da64bc06502de4571446dd0537391ae361 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 20 Jul 2023 14:48:35 +0100
Subject: [PATCH 218/703] Update
 website/docs/docs/cloud/about-cloud/browsers.md

---
 website/docs/docs/cloud/about-cloud/browsers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/about-cloud/browsers.md b/website/docs/docs/cloud/about-cloud/browsers.md
index 88004a1f6ab..2fc5a8b4b4d 100644
--- a/website/docs/docs/cloud/about-cloud/browsers.md
+++ b/website/docs/docs/cloud/about-cloud/browsers.md
@@ -26,4 +26,4 @@ To improve your experience using dbt Cloud, we suggest that you turn off ad bloc
 
 A session is a period of time during which you’re signed in to a dbt Cloud account from a browser. If you close your browser, it will end your session and log you out. You'll need to log in again the next time you try to access dbt Cloud. 
 
-If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/git/connect-github#personally-authenticate-with-github), you can customize your session time, which might vary depending on your identity provider (IdP).
+If you've logged in using [SSO](/docs/cloud/manage-access/sso-overview) or [OAuth](/docs/cloud/git/connect-github#personally-authenticate-with-github), you can customize your maximum session duration, which might vary depending on your identity provider (IdP).

From 3deeb24886a80a743779c762c98a84baeb26896b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 14:56:22 +0100
Subject: [PATCH 219/703] FIX LINKS

---
 website/docs/docs/build/sl-getting-started.md | 10 +++++-----
 website/snippets/_test-tenancy.md             |  2 --
 website/snippets/_v2-sl-prerequisites.md      |  8 ++++----
 3 files changed, 9 insertions(+), 11 deletions(-)
 delete mode 100644 website/snippets/_test-tenancy.md

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 805174d74fc..8d1a471b6d0 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -32,7 +32,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
   * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
   * Set up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions.
 - To query locally:
-  * dbt Core or Developer accounts must manually install the [MetricFlow CLI package](/docs/build/metricflow-cli) to test or query their metrics. After installing the package, make sure you run at least one model. <br />
+  * dbt Core or Developer accounts must manually install the MetricFlow CLI (WILL ADD LINK WHEN CLI PR IS LIVE)  to test or query their metrics. After installing the package, make sure you run at least one model. <br />
 
 
 :::tip 
@@ -119,11 +119,11 @@ To continue building out your metrics based on your organization's needs, refer
 
 ## Test metrics 
 
-The following steps explain how to test your metrics using the [MetricFlow CLI](/docs/build/metricflow-cli) (dbt Cloud IDE support coming soon).
+The following steps explain how to test your metrics using the MetricFlow CLI (WILL ADD LINK WHEN CLI PR IS LIVE) ) (dbt Cloud IDE support coming soon).
 
 dbt Core or Developer plan user should refer to [Test and query with the CLI](#test-and-query-with-the-cli) for detailed steps. 
 
-1. To test your metrics, make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+1. To test your metrics, make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
 3. Run `mf validate-configs` to validate the changes before committing them
 
@@ -145,7 +145,7 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query API
 
-To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
+To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
 
 1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
 2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
@@ -158,7 +158,7 @@ Before you begin, make sure you install the `metricflow` and [dbt adapter](/docs
 
 To test your metrics locally:
 
-1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+1. Make sure you have the MetricFlow CLI (WILL ADD LINK WHEN CLI PR IS LIVE) installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
 3. Run `mf validate-configs` to validate the changes before committing them
 
diff --git a/website/snippets/_test-tenancy.md b/website/snippets/_test-tenancy.md
deleted file mode 100644
index 7fa5fac97f1..00000000000
--- a/website/snippets/_test-tenancy.md
+++ /dev/null
@@ -1,2 +0,0 @@
-
-dbt Cloud is available in both single (virtual private) and multi-tenant configurations.
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index bd10ad76c72..5006e1dc2a6 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -1,7 +1,7 @@
 
 <VersionBlock firstVersion="1.6">
 
-- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
+- Have a dbt Cloud Team or Enterprise [multi-tenant]/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
@@ -16,7 +16,7 @@
 
 <VersionBlock lastVersion="1.5">
 
-- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br />
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses">hosted</a> in North America <br />
 - Have both your production and development environments running dbt version 1.3 or higher <br />
 - Use Snowflake data platform <br />
 - Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=1.3.0, <1.4.0</code> in your dbt project <br />
@@ -29,7 +29,7 @@
 
 <VersionBlock firstVersion="1.3" lastVersion="1.3" >
 
-- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br />
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses">hosted</a> in North America <br />
 - Have both your production and development environments running dbt version 1.3 or higher <br />
 - Use Snowflake data platform <br />
 - Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=1.3.0, <1.4.0</code> in your dbt project <br />
@@ -42,7 +42,7 @@
 
 <VersionBlock lastVersion="1.2">
 
-- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/deploy/regions">hosted</a> in North America <br /> 
+- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses">hosted</a> in North America <br /> 
 - Have both your production and development environments running dbt version 1.2 <br />
 - Use Snowflake data platform <br />
 - Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=0.3.0, <0.4.0</code> in your dbt project <br />

From 68e994e398a425a9a44640067760d0f6f3709ca9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 15:59:09 +0100
Subject: [PATCH 220/703] space

---
 website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index e24773b3513..b239ddc83c7 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -84,6 +84,7 @@ measures:
 :::tip
 
 If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
+
 ```sql
 select
   metric_time_day,  -- time

From 0c9ef6cb000e77cf070d7ebd084efbc52ceaa78b Mon Sep 17 00:00:00 2001
From: dave-connors-3 <73915542+dave-connors-3@users.noreply.github.com>
Date: Thu, 20 Jul 2023 10:16:01 -0500
Subject: [PATCH 221/703] Update metricflow-time-spine.md

---
 website/docs/docs/build/metricflow-time-spine.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 607df692bc9..650500b9aec 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -11,6 +11,12 @@ MetricFlow uses a timespine table to construct cumulative metrics. By default, M
 To create this table, you need to create a model in your dbt project called `metricflow_time_spine` and add the following code:
 
 ```sql
+{{
+    config(
+        materialized = 'table',
+    )
+}}
+
 -- metricflow_time_spine.sql
 with days as (
     {{dbt_utils.date_spine('day'

From 7c4083a3cc74355b5b08192a77c85948e6d9ad98 Mon Sep 17 00:00:00 2001
From: dave-connors-3 <73915542+dave-connors-3@users.noreply.github.com>
Date: Thu, 20 Jul 2023 10:18:26 -0500
Subject: [PATCH 222/703] move comment

---
 website/docs/docs/build/metricflow-time-spine.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 650500b9aec..6199346124c 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -11,13 +11,13 @@ MetricFlow uses a timespine table to construct cumulative metrics. By default, M
 To create this table, you need to create a model in your dbt project called `metricflow_time_spine` and add the following code:
 
 ```sql
+-- metricflow_time_spine.sql
 {{
     config(
         materialized = 'table',
     )
 }}
 
--- metricflow_time_spine.sql
 with days as (
     {{dbt_utils.date_spine('day'
     , "to_date('01/01/2000','mm/dd/yyyy')"

From 46a5775a81d8501f70c21f4be50150e65fd39570 Mon Sep 17 00:00:00 2001
From: dave-connors-3 <73915542+dave-connors-3@users.noreply.github.com>
Date: Thu, 20 Jul 2023 10:19:30 -0500
Subject: [PATCH 223/703] formatting

---
 website/docs/docs/build/metricflow-time-spine.md | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 6199346124c..15415297ccc 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -19,11 +19,15 @@ To create this table, you need to create a model in your dbt project called `met
 }}
 
 with days as (
-    {{dbt_utils.date_spine('day'
-    , "to_date('01/01/2000','mm/dd/yyyy')"
-    , "to_date('01/01/2027','mm/dd/yyyy')"
-    )
+
+    {{
+        dbt_utils.date_spine(
+            'day',
+            "to_date('01/01/2000','mm/dd/yyyy')",
+            "to_date('01/01/2027','mm/dd/yyyy')"
+        )
     }}
+
 ),
 
 final as (
@@ -31,8 +35,7 @@ final as (
     from days
 )
 
-select *
-from final
+select * from final
 ```
 
 You only need to include the `date_day` column in the table. MetricFlow can handle broader levels of detail, but it doesn't currently support finer grains.

From f1a2e7e1b5eded7f7485364e80f6f6d3d6443156 Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Thu, 20 Jul 2023 11:06:58 -0500
Subject: [PATCH 224/703] Update semantic-models.md

Fix capitalization miss and add a link for clarity
---
 website/docs/docs/build/semantic-models.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index 28fccaddb72..a304944a440 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -140,11 +140,11 @@ You can refer to entities (join keys) in a semantic model using the `name` param
 
 MetricFlow simplifies this by allowing you to query all metric groups and construct the join during the query. To specify dimensions parameters, include the `name` (either a column or SQL expression) and `type` (`categorical` or `time`). Categorical groups represent qualitative values, while time groups represent dates of varying granularity.
 
-dimensions are identified using the name parameter, just like identifiers. The naming of groups must be unique within a semantic model, but not across semantic models since MetricFlow, uses entities to determine the appropriate groups.
+Dimensions are identified using the name parameter, just like identifiers. The naming of groups must be unique within a semantic model, but not across semantic models since MetricFlow, uses entities to determine the appropriate groups.
 
 :::info For time groups
 
-For semantic models with a measure, you must have a primary time group.
+For semantic models with a measure, you must have a [primary time group](/docs/build/dimensions#time).
 
 :::
 

From e3e1b303d7cdcb7f1b288d6e734193ef7a25f98c Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 17:31:22 +0100
Subject: [PATCH 225/703] adding file

---
 website/snippets/_test-tenancy.md | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 website/snippets/_test-tenancy.md

diff --git a/website/snippets/_test-tenancy.md b/website/snippets/_test-tenancy.md
new file mode 100644
index 00000000000..7fa5fac97f1
--- /dev/null
+++ b/website/snippets/_test-tenancy.md
@@ -0,0 +1,2 @@
+
+dbt Cloud is available in both single (virtual private) and multi-tenant configurations.

From d972fb3dc70aeabce3d171f51a46e51f008f01a2 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 20 Jul 2023 17:43:29 +0100
Subject: [PATCH 226/703] fix link

---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 7ea30e5e737..eec19374fe4 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -29,7 +29,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - A successful run in the environment where your Semantic Layer is configured
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - To query with dbt Cloud:
-  * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America
+  * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
   * Set up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions.
 - To query locally:
   * dbt Core or Developer accounts must manually install the [MetricFlow CLI](/docs/build/metricflow-cli) to test or query their metrics. After installing the package, make sure you run at least one model. <br />

From aebdcc543da40335028600f5d2a18f4ebfaff464 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Thu, 20 Jul 2023 11:19:56 -0700
Subject: [PATCH 227/703] Update avail-sl-integrations.md

adding more details on the boundaries of our JDBC driver
---
 .../docs/use-dbt-semantic-layer/avail-sl-integrations.md    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 8ebf96278df..30c2337b5dc 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -22,10 +22,10 @@ Discover the diverse range of data applications that seamlessly integrate with t
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
 
-- Google Sheets  &mdash; coming soon
 - Hex &mdash;
-- Mode &mdash; 
-- Any SQL developer tool that supports a generic driver, such as DataGrip
+- Mode &mdash;
+- Google Sheets - Coming Soon!
+- Any tool that allows you to write SQL and meets one of the two criteria -- (a) supports a generic JDBC driver option (e.g., DataGrip) or (b) supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or greater.
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 

From cfb3cc58683f0ed3b44019367a9c9d7dd78b7112 Mon Sep 17 00:00:00 2001
From: Brian Jan <brian.j.jan@gmail.com>
Date: Thu, 20 Jul 2023 13:42:42 -0500
Subject: [PATCH 228/703] Update set-up-sso-azure-active-directory.md

Fix redirect URI in Azure AD SSO doc
---
 .../cloud/manage-access/set-up-sso-azure-active-directory.md    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md b/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
index fcc9a79e860..f58bceff816 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-azure-active-directory.md
@@ -45,7 +45,7 @@ need to select the appropriate directory and then register a new application.
 
 | Application Type | Redirect URI |
 | ----- | ----- |
-| Single-Tenant _(recommended)_ | `https://YOUR_AUTH0_URI/login/callback?connection=<login slug>` |
+| Single-Tenant _(recommended)_ | `https://YOUR_AUTH0_URI/login/callback` |
 | Multi-Tenant | `https://YOUR_AUTH0_URI/login/callback` |
 
 

From bd6dd60f4b0b2d82eb05327979be43f22a4ecfcb Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Thu, 20 Jul 2023 13:10:45 -0700
Subject: [PATCH 229/703] Update sl-jdbc.md

Adding new JDBC metadata commands!
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 30 ++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 440e0bfbd8a..f050c7bd635 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -81,7 +81,6 @@ select * from {{
 	semantic_layer.dimensions(metrics=['food_order_amount'])}}
 ```
 
-
 </TabItem>
 
 <TabItem value="dimensionvalueformetrics" label="Fetch dimension values metrics">
@@ -97,6 +96,35 @@ semantic_layer.dimension_values(metrics=["food_order_amount"], group_by="custome
 
 </TabItem>
 
+<TabItem value="queryablegranularitiesformetrics" label="Fetch queryable time granularities for metrics">
+
+Use this query to fetch queryable granularities for a list of metrics. This argument allows you to only show the time granularities that make sense for the source model that the metrics are built off of.
+
+Note, `metrics` is a required argument that lists with one or multiple metrics in it.
+
+```bash
+select * from {{
+    semantic_layer.queryable_granularities(metrics=['food_order_amount', 'order_gross_profit'])}}
+```
+
+</TabItem>
+
+
+<TabItem value="metricsfordimensions" label="Fetch available metrics given dimensions">
+
+Use this query to fetch available metrics given dimensions. This command is essentially the opposite of getting dimensions given a list of metrics.
+
+Note, `group_by` is a required argument that lists one or multiple dimensions in it.
+
+```bash
+select * from {{
+    semantic_layer.metrics_for_dimensions(group_by=['customer__customer_type'])
+
+}}
+```
+
+</TabItem>
+
 </Tabs>
 
 ## Querying the API for metric values

From 9a64f9017ea9a1dcf1d320e4a453b9ae036f1110 Mon Sep 17 00:00:00 2001
From: Anders <swanson.anders@gmail.com>
Date: Thu, 20 Jul 2023 16:38:13 -0400
Subject: [PATCH 230/703] Apply suggestions from code review

---
 website/docs/reference/resource-configs/postgres-configs.md  | 3 ++-
 website/docs/reference/resource-configs/redshift-configs.md  | 1 +
 website/docs/reference/resource-configs/snowflake-configs.md | 4 ++--
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index d4f6958ae27..2e603ba8111 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -150,9 +150,10 @@ models:
 
 ### Limitations
 
+Below are current limitations that we hope to address in a future release.
 #### Changing materialization to and from "materialized_view"
 
-Swapping an already materialized model to be a materialized view and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again.
+Swapping an already materialized model to be a materialized view and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again. Normally, re-running with the `--full-refresh` flag would resolve this, but not in this case.
 
 To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="materialized_view"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
 
diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 00dfe517cea..0991afc21db 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -138,6 +138,7 @@ models:
 
 ### Limitations
 
+Below are current limitations that we hope to address in a future release.
 #### Changing materialization from "materialized_view" to table or view
 
 Swapping a materialized view to be a table or view is not supported. The workaround is to manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 154f8795eea..da110fd50df 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -362,7 +362,7 @@ The following examples create a dynamic table:
   config(
     materialized = 'dynamic_table',
     warehouse = 'MY_WAREHOUSE',
-    target_lag = '10 min',
+    target_lag = '10 minutes',
   )
 }}
 ```
@@ -376,7 +376,7 @@ models:
   path:
     materialized: dynamic_table
     warehouse: MY_WAREHOUSE
-    target_lag: '10 min'
+    target_lag: '10 minutes'
 ```
 
 </File>

From 3cabbd220b864a17837e4957bdf51e5bdf505b82 Mon Sep 17 00:00:00 2001
From: Anders Swanson <anders.swanson@dbtlabs.com>
Date: Thu, 20 Jul 2023 16:43:18 -0400
Subject: [PATCH 231/703] explicitly callout parameters

---
 .../resource-configs/snowflake-configs.md          | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index da110fd50df..9644a1539f5 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -346,7 +346,19 @@ In the configuration format for the model SQL file:
 
 ## Dynamic Tables
 
-[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's flavor of Materialized Views. The `CREATE DYNAMIC TABLE` ([docs](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table)) statement requires the following parameters `TARGET_LAG` and `WAREHOUSE`, so a dbt-snowflake user must also provide these.
+[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's flavor of Materialized Views.
+
+### Parameters
+
+dbt-snowflake requires the following parameters:
+
+- `TARGET_LAG`
+- `WAREHOUSE`
+
+To learn more about each parameter and what values it can take, see 
+the Snowflake docs page: [`CREATE DYNAMIC TABLE: Parameters`](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table)
+
+### Usage
 
 You can create a dynamic table by editing _one_ of these files:
 

From dd64a6e9e8f06c556263582ebeb0455b6a7a68a7 Mon Sep 17 00:00:00 2001
From: "Alison (she/her)" <alison985@users.noreply.github.com>
Date: Thu, 20 Jul 2023 17:43:35 -0400
Subject: [PATCH 232/703] Update database.md to fix a link and clarify the
 Redshift specific consideration

---
 website/docs/reference/resource-configs/database.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/resource-configs/database.md b/website/docs/reference/resource-configs/database.md
index b4759d8b6f3..4e7ce3be039 100644
--- a/website/docs/reference/resource-configs/database.md
+++ b/website/docs/reference/resource-configs/database.md
@@ -12,7 +12,7 @@ This is a work in progress document. While this configuration applies to multipl
 
 ## Definition
 
-Optionally specify a custom database for a [model](docs/build/models) or [seed](/docs/build/seeds). (To specify a database for a [snapshot](/docs/build/snapshots), use the [`target_database` config](/reference/resource-configs/target_database)).
+Optionally specify a custom database for a [model](/docs/build/sql-models) or [seed](/docs/build/seeds). (To specify a database for a [snapshot](/docs/build/snapshots), use the [`target_database` config](/reference/resource-configs/target_database)).
 
 When dbt creates a relation (<Term id="table" />/<Term id="view" />) in a database, it creates it as: `{{ database }}.{{ schema }}.{{ identifier }}`, e.g. `analytics.finance.payments`
 
@@ -43,4 +43,4 @@ seeds:
 
 ## Warehouse specific information
 * BigQuery: `project` and `database` are interchangeable
-* Redshift: Cross-database queries are not possible in Redshift. As such, dbt will return a Database Error if you use this configuration.
+* Redshift: Cross-database queries are not possible in Redshift. As such, dbt will return a Database Error if you use 'Database A' for a seed file and try to `{{ ref() }}` that seed file(or its database object) in a model using 'Database B'. This error would only be found at runtime.

From d5cd737a9764e7a881abda4bc3dc0cea88c559aa Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 21 Jul 2023 09:59:14 +0100
Subject: [PATCH 233/703] Update
 website/docs/docs/dbt-versions/experimental-features.md

---
 website/docs/docs/dbt-versions/experimental-features.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/experimental-features.md b/website/docs/docs/dbt-versions/experimental-features.md
index 02a8157d4cd..5ed0cf037ca 100644
--- a/website/docs/docs/dbt-versions/experimental-features.md
+++ b/website/docs/docs/dbt-versions/experimental-features.md
@@ -24,4 +24,4 @@ To enable or disable experimental features:
 
 ## Beta terms and conditions
 
-By using or enabling features that aren't yet in general release ("Beta Features"), you agree to the [Beta Features Terms and Conditions](/assets/beta-tc.pdf).
+By using or enabling features that are not yet in general release ("Beta Features"), you agree to the [Beta Features Terms and Conditions](/assets/beta-tc.pdf).

From 87f34aac03abc465a3fb26858733d08ea4836691 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 21 Jul 2023 13:26:11 +0100
Subject: [PATCH 234/703] add screenshots, edit best practices

---
 website/docs/docs/build/metricflow-cli.md     |   2 +
 website/docs/docs/build/sl-getting-started.md |  28 +--
 .../avail-sl-integrations.md                  |   4 +-
 .../use-dbt-semantic-layer/quickstart-sl.md   |   8 +-
 .../sl-partner-integration-guide.md           | 175 ++++++++++++++++--
 website/snippets/_new-sl-setup.md             |  22 ++-
 website/snippets/_v2-sl-prerequisites.md      |   3 +-
 .../semantic-layer/sl-configure-sl.jpg        | Bin 0 -> 48952 bytes
 .../semantic-layer/sl-initial-page.jpg        | Bin 0 -> 29742 bytes
 9 files changed, 197 insertions(+), 45 deletions(-)
 create mode 100644 website/static/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg
 create mode 100644 website/static/img/docs/dbt-cloud/semantic-layer/sl-initial-page.jpg

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 9e934e9ccca..8687d4e31aa 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -15,6 +15,8 @@ You can install the [MetricFlow CLI](https://github.com/dbt-labs/metricflow#gett
 1. Create or activate your virtual environment.`python -m venv venv`
 2. Run `pip install dbt-metricflow`
 
+  * You can install MetricFlow using PyPI as an extension of your dbt adapter in the CLI. To install the adapter, run `pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `pip install "dbt-metricflow[snowflake]"`
+
 The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
 
 # CLI commands
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index eec19374fe4..71145beed8f 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -119,9 +119,10 @@ To continue building out your metrics based on your organization's needs, refer
 
 ## Test metrics 
 
-The following steps explain how to test your metrics using the [MetricFlow CLI](/docs/build/metricflow-cli) (dbt Cloud IDE support coming soon).
-
-dbt Core or Developer plan user should refer to [Test and query with the CLI](#test-and-query-with-the-cli) for detailed steps. 
+:::note
+For public beta, you can't yet test metrics using the dbt Cloud IDE (support coming soon). To test metrics, you must install the [MetricFlow CLI](/docs/build/metricflow-cli).
+  * dbt Core or Developer plan users can skip to [Test and query with the CLI](#test-and-query-with-the-cli) for detailed steps. 
+:::
 
 1. To test your metrics, make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
@@ -145,24 +146,26 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query API
 
-To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
+Before you begin, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
+
+To connect and query your metrics using the dbt Semantic Layer and its API:
 
 1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
-2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice.
 
-## Test and query with the CLI
+Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
-Before you begin, make sure you install the `metricflow` and [dbt adapter](/docs/supported-data-platforms) via PyPI in the CLI. To install them, open the command line interface (CLI) and use the pip install command `pip install "dbt-metricflow[your_adapter_name]"`.
+## Test and query with the CLI
 
-  * Note that specifying `[your_adapter_name]` is required.  This is because you must install MetricFlow as an extension of a dbt adapter. For example, for a Snowflake adapter, run `pip install "dbt-metricflow[snowflake]"`.
+Before you begin, dbt Core or Developer plan users must first install the [MetricFlow CLI](/docs/build/metricflow-cli). 
 
-To test your metrics locally:
+**Test using the CLI:**
 
-1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
+1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
 3. Run `mf validate-configs` to validate the changes before committing them
 
-To query your metrics locally:
+**Query using the CLI:**
 
 1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
 2. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
@@ -173,6 +176,3 @@ To query your metrics locally:
 - [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [The dbt Semantic Layer: what’s next](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) blog post
 - [About MetricFlow](/docs/build/about-metricflow)
-- [Semantic models](/docs/build/semantic-models)
-- [Metrics](/docs/build/metrics-overview)
-- [MetricFlow CLI](/docs/build/metricflow-cli)
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 30c2337b5dc..ac6317a9c9d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -25,7 +25,9 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 - Hex &mdash;
 - Mode &mdash;
 - Google Sheets - Coming Soon!
-- Any tool that allows you to write SQL and meets one of the two criteria -- (a) supports a generic JDBC driver option (e.g., DataGrip) or (b) supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or greater.
+- Any tool that allows you to write SQL and meets one of the two criteria: 
+  * Supports a generic JDBC driver option (such as DataGrip) or 
+  * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
 
 For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index b239ddc83c7..5e85f87e792 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -117,7 +117,7 @@ metrics:
 
 ## Test metrics
 
-The following steps explain how to test and manually query your metrics using the [MetricFlow CLI](/docs/build/metricflow-cli) only. dbt Cloud IDE support will be available soon.
+The following steps explain how to test and manually query your metrics.
 
 1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
 2. Run `mf validate-configs` to validate the changes before committing them.
@@ -158,7 +158,9 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
 
 1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
-2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. 
+
+Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 
@@ -181,7 +183,7 @@ The dbt Semantic Layer is proprietary, however, some components of the dbt Seman
    
 ## Next steps
 
-Are you ready to define your own metrics and bring consistency to data consumers? Review the following documents to understand how to structure, define, and query metrics, and set up the dbt Semantic Layer.
+Ready to define your own metrics and bring consistency to data consumers?  Review the following documents to get started:
 
 - [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
 - [Build your metrics](/docs/build/build-metrics-intro)
diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 6f7ae1df2e7..b5f848d9832 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -1,15 +1,163 @@
 ---
-title: "dbt Semantic Layer integration" 
+title: "dbt Semantic Layer integration best practices" 
 id: "sl-partner-integration-guide"
 description: Learn about partner integration guidelines, roadmap, and connectivity. 
 ---
 
-# dbt Semantic Layer partner integration
+<VersionBlock firstVersion="1.6">
 
-:::info Coming soon
-The dbt Semantic Layer is undergoing some sophisticated changes, enabling more complex metric definitions and efficient querying. As part of these changes, the dbt_metrics package will be deprecated and replaced with MetricFlow. For more info, check out the [The dbt Semantic Layer: what's next?](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) and [dbt_metrics deprecation](https://docs.getdbt.com/blog/deprecating-dbt-metrics) blog.
+import NewChanges from '/snippets/_new-sl-changes.md';
+
+<NewChanges/>
+
+To fit your tool within the world of the Semantic Layer, dbt Labs offers some best practice recommendations for how to expose metrics and allow users to interact with them seamlessly. 
+
+:::note
+This is an evolving guide that is meant to provide recommendations based on our experience. If you have any feedback, we'd love to hear it (email?)!
 :::
 
+
+## Requirements
+
+To build a dbt Semantic Layer integration: 
+
+-  Initially, we recommend building an integration with the [JDBC](/docs/dbt-cloud-apis/sl-jdbc) followed by enhancements of additional features. Refer to the dedicated [dbt Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) for more technical integration details.
+
+- Familiarize yourself with the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and [MetricFlow](/docs/build/about-metricflow)'s key concepts. There are two main objects: 
+
+  - [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
+  - [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
+
+### Connection parameters
+
+The dbt Semantic Layer authenticates with:
+- `environmentId`, 
+- `SERVICE_TOKEN`, 
+- `host`
+
+This applies to the dbt Semantic Layer APIs, which all currently use different host names.
+
+We recommend you provide users with separate input fields with these components (which dbt Cloud provides). 
+
+For [JDBC](/docs/dbt-cloud-apis/sl-jdbc), you can construct the JDBC URL from these inputs. Or, you could request the full URL string.
+
+If you use both Semantic Layer APIs, users will need to provide different host information for each one. In the future, we want to unify these host names.
+
+## Best practices on exposing metrics:
+
+Best practices for exposing metrics is summarized into five themes:
+
+- [Governance](#governance-and-traceability) &mdash;
+- [Discoverability](#discoverability) &mdash;
+- [Organization](#organization) &mdash;
+- [Context and interpretation](#context-and-interpretation) &mdash;
+- [Query flexibility](#query-flexibility) &mdash;
+
+### Governance and traceability
+
+When working with more governed data, it's essential to establish clear guardrails. Here are some recommendations:
+
+**Aggregations control** <br />
+Users shouldn't generally be allowed to modify aggregations unless they are performing post-processing calculations on data from the Semantic Layer (such as year over year analysis).
+
+**Traceability of metric and dimension changes** <br />
+When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
+
+**Time series alignment**<br />
+Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes. 
+  
+Implementing guardrails in the application might help achieve this.
+
+**Units consistency**<br />
+If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
+
+### Discoverability 
+
+1. Consider treating [metrics](/docs/build/metrics-overview) as first-class objects rather than measures. Metrics offer a higher-level and more contextual way to interact with data, reducing the burden on end-users to manually aggregate data.
+
+2. Easy metric interactions: Provide users with an intuitive approach to:
+
+    * Search for Metrics &mdash; Users should be able to easily search and find relevant metrics. Metrics can serve as the starting point to lead users into exploring dimensions.
+    * Search for Dimensions &mdash; Users should be able to query metrics with associated dimensions, allowing them to gain deeper insights into the data.
+    * Filter by Dimension Values &mdash; Expose and enable users to filter metrics based on dimension values, encouraging data analysis and exploration.
+    * Filter additional metadata &mdash; Allow users to filter metrics based on other available metadata, such as metric type and default time granularity.
+
+3. Suggested Metrics: Ideally, the system should intelligently suggest relevant metrics to users based on their team's activities. This approach encourages user exposure, facilitates learning, and supports collaboration among team members.
+
+By implementing these recommendations, the data interaction process becomes more user-friendly, empowering users to gain valuable insights without the need for extensive data manipulation.
+
+### Organization 
+
+We recommend organizing metrics and dimensions in ways that a non-technical user can understand the data model, without needing much context:
+
+**Organizing Dimensions** 
+
+To help non-technical users understand the data model better, we recommend organizing dimensions based on the entity they originated from. For example, consider dimensions like `user__country` and `product__category`.  
+
+You can create groups by extracting `user` and `product` and then nest the respective dimensions under each group. This way, dimensions align with the entity or semantic model they belong to and makes them them more user-friendly and accessible.
+
+**Organizing Metrics** 
+
+The goal is to organize metrics into a hierarchy in our configurations, instead of presenting them in a long list. 
+
+This hierarchy helps you organize metrics based on a specific criteria, such as business unit or team. By providing this structured organization, users can find and navigate metrics more efficiently, enhancing their overall data analysis experience.
+
+### Context and interpretation
+
+For better analysis, it's best to have the context of the metrics close to where the analysis is happening. We recommend the following:
+
+- Expose business definitions of the metrics as well as logical definitions
+- Expose additional metadata from the Semantic layer (measures, type parameters)
+- Use the [Discovery API](/docs/dbt-cloud-apis/discovery-api) to enhance the metric and build confidence in its accuracy:
+  * Check if the metric is fresh and when it was last updated.
+  * Include lineage information to understand the metric's origin.
+- Allow for creating other metadata that’s useful for the metric. We can provide some of this information in our configuration (Display name, Default Granularity for View, Default Time range), but there may be other metadata that your tool wants to provide to make the metric richer.
+
+### Query flexibility
+
+Allow users to query either one metric without dimensions or one or more metrics with dimensions
+
+- Allow toggling between metrics / dimensions seamlessly
+- Be clear on exposing what dimensions are queryable with what metrics and hide things that don’t apply, and vice versa.
+- Include Presets for filtering date ranges and calendar for filtering time dimensions
+  * For example, last 30 days, last week etc.
+- Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
+  * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity" isn't available to the user.  
+
+### Example stages of an integration
+
+**Stage 1 - The basic**
+* Supporting and using the new [JDBC](/docs/dbt-cloud-apis/sl-jdbc) is the first step. Refer to the [dbt Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) for more technical details. 
+
+**Stage 2 - More discoverability and basic querying**
+* Support listing metrics defined in project
+* Listing available dimensions based on one or many metrics
+* Querying defined metric values on their own or grouping by available dimensions
+* Display metadata from [Discovery API](/docs/dbt-cloud-apis/discovery-api) and other context
+
+**Stage 3 - More querying flexibility and better user experience (UX)**
+* More advanced filtering
+  * Time filters with good presets/calendar UX
+  * Filtering metrics on pre-populated set of dimensions values
+* Make dimension values more user-friendly by organizing them effectively
+* Intelligent filtering of metrics based on available dimensions and vice versa
+
+**Stage 4 - More custom user interface (UI) / Collaboration**
+* A place where users can see all the relevant information about a given metric
+* Organize metrics by hierarchy and more advanced search features (such as filter on the type of metric or other metadata)
+* Use and expose more metadata 
+* Querying dimensions without metrics and other more advanced querying functionality
+* Suggest metrics to users based on teams/identity, and so on.
+
+</VersionBlock>
+
+<VersionBlock lastVersion="1.5">
+
+import LegacyInfo from '/snippets/_legacy-sl-callout.md';
+
+<LegacyInfo/>
+
+
 This guide is for dbt Semantic Layer integration partners and explains integration guidelines, and connectivity.<br />
 
 To become a formal partner, integrate with the API, or have questions/feedback &mdash; **[contact us](mailto:semantic-layer@dbtlabs.com)** for more info.
@@ -107,17 +255,6 @@ To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-s
     </details>
     <br></br>
 
-<!-- the following content was used for the <details> portion above. leaving this here for legacy purposes 
-**Metadata API authorization**
-
-To learn how to authorize requests to the Metadata API, review the [documentation](/docs/dbt-cloud-apis/discovery-querying#authorization) for more details. Metrics-specific queries work identically to existing Metadata API queries, so existing integrations used to query model metadata will work perfectly in the context of metrics. 
-
-**Query the Metadata API**
-
-Test out the Metadata API by using the [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) and use this [Python client](https://github.com/transform-data/dbt-metadata-client) as a starting point to develop. 
-
--->
-
 ### Query models for a project
 
 You can query model definitions or details about a specific model for a project from a given job.
@@ -412,7 +549,6 @@ from {{ ref('orders') }}
 </Tabs>
 
 ### Entities 
-<!-- rewrite a little and include a summary for each tab entry -->
 
 dbt Labs will introduce a new node type, **[entity](https://github.com/dbt-labs/dbt-core/issues/6379)**, when dbt Core version 1.5 launches. It introduces a new and efficient way to define metrics by reusing logic (for example, `time_grains`).  
 
@@ -650,11 +786,12 @@ If you'd like to become a formal partner, have product feedback/questions, or ar
 
 <!-- rewrite a little and would like to see if i can include a button -->
 
+</VersionBlock>
+
 ## Related docs
 
-- [dbt Semantic Layer docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
-- [dbt Metrics docs](https://docs.getdbt.com/docs/building-a-dbt-project/metrics) for more information about its components.
-- [dbt Semantic Layer intro blog](https://www.getdbt.com/blog/dbt-semantic-layer/) and [launch blog](https://www.getdbt.com/blog/frontiers-of-the-dbt-semantic-layer/) to learn more about the product vision and purpose. 
+- [Use the dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
+- [Build your metrics](/docs/build/build-metrics-intros) for more info about MetricFlow and its components. 
 - [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.
 
 
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 65f38fd625d..f4b7f7763a3 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -6,18 +6,26 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
     * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
+
 2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
-3. In the **Project Details** page, navigate to **Configure Semantic Layer**.
-4. Enter the credentials you want the Semantic Layer to use specific to your data platform. 
+
+3. In the **Project Details** page, navigate to the **Semantic Layer** section, and select **Configure Semantic Layer**.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-initial-page.jpg" width="60%" title="Semantic Layer section in the Project Details page"/>
+
+4. In the **Set Up Semantic Layer Configuration** page, enter the credentials you want the Semantic Layer to use specific to your data platform. 
     * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
-5. Select the deployment environment you want for the Semantic Layer
+
+5. Select the deployment environment you want for the Semantic Layer and click **Save**.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="60%" title="Set Up your Semantic Layer configuration."/>
+
 6. You should see connection information that allows you to connect to downstream tools. 
     * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
-7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
-8. Save & copy your environment ID, service token, and host for inputting into a downstream tool
-9. Great job, you've configured the Semantic Layer 🎉! 
-
 
+7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
 
+8. Save and copy your environment ID, service token, and host for inputting into a downstream tool
 
+9. Great job, you've configured the Semantic Layer 🎉! 
 
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 5006e1dc2a6..7dde6f32572 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -1,7 +1,7 @@
 
 <VersionBlock firstVersion="1.6">
 
-- Have a dbt Cloud Team or Enterprise [multi-tenant]/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
+- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
@@ -9,6 +9,7 @@
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
   * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need to have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
+- Have an understanding of [MetricFlow's](/docs/build/about-metricflow) key concepts, which powers the revamped dbt Semantic Layer.
 
 
 </VersionBlock>
diff --git a/website/static/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg b/website/static/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg
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IgmK`10pNd$m;e9(

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HcmV?d00001


From ad87637f988863f5127c52bc876f4b1201851d96 Mon Sep 17 00:00:00 2001
From: Bart Schuijt <schuijt.bart@gmail.com>
Date: Fri, 21 Jul 2023 14:26:41 +0200
Subject: [PATCH 235/703] Update compile.md

Updates `compile.md` with a link to the Introspective queries documentation.
---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 97d989a140b..b5ef4cb2987 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -68,7 +68,7 @@ select * from renamed
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
 - `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query.
-- `--no-introspect` to disable introspective queries. dbt will raise an error if a model's definition requires running one.
+- `--no-introspect` to disable [introspective queries](https://docs.getdbt.com/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one.
 
 
 ### FAQs

From 4faf734cb1ade852e2977ceb0a8c1292022bdd63 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 21 Jul 2023 13:28:12 +0100
Subject: [PATCH 236/703] Update website/docs/reference/commands/compile.md

---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index b5ef4cb2987..a9821c0ff12 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -68,7 +68,7 @@ select * from renamed
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
 - `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query.
-- `--no-introspect` to disable [introspective queries](https://docs.getdbt.com/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one.
+- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one.
 
 
 ### FAQs

From 0e04ae4ce3c80c6e3fe36aad88265890cb3c44d4 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 21 Jul 2023 13:31:45 +0100
Subject: [PATCH 237/703] fix link

---
 .../docs/guides/dbt-ecosystem/sl-partner-integration-guide.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index b5f848d9832..ccf1c0d8d09 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -791,7 +791,7 @@ If you'd like to become a formal partner, have product feedback/questions, or ar
 ## Related docs
 
 - [Use the dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
-- [Build your metrics](/docs/build/build-metrics-intros) for more info about MetricFlow and its components. 
+- [Build your metrics](/docs/build/build-metrics-intro) for more info about MetricFlow and its components. 
 - [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.
 
 

From 7cbe8277a81ab10aee9799b315c306818de3d193 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 21 Jul 2023 13:39:07 +0100
Subject: [PATCH 238/703] Update
 website/docs/reference/resource-configs/database.md

---
 website/docs/reference/resource-configs/database.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/database.md b/website/docs/reference/resource-configs/database.md
index 4e7ce3be039..9b7cd1b39d3 100644
--- a/website/docs/reference/resource-configs/database.md
+++ b/website/docs/reference/resource-configs/database.md
@@ -43,4 +43,4 @@ seeds:
 
 ## Warehouse specific information
 * BigQuery: `project` and `database` are interchangeable
-* Redshift: Cross-database queries are not possible in Redshift. As such, dbt will return a Database Error if you use 'Database A' for a seed file and try to `{{ ref() }}` that seed file(or its database object) in a model using 'Database B'. This error would only be found at runtime.
+* Redshift: Cross-database queries are not possible in Redshift. As such, dbt will return a Database Error if you use 'Database A' for a seed file and try to `{{ ref() }}` that seed file (or its database object) in a model using 'Database B'. This error would only be found at runtime.

From 0398e75343a8bd5d0e8780f3556df8cec70dbb49 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Fri, 21 Jul 2023 15:13:54 +0100
Subject: [PATCH 239/703] fix spacing

---
 .../docs/guides/dbt-ecosystem/sl-partner-integration-guide.md   | 2 --
 1 file changed, 2 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index ccf1c0d8d09..7e6dc81bac0 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -793,5 +793,3 @@ If you'd like to become a formal partner, have product feedback/questions, or ar
 - [Use the dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.
 - [Build your metrics](/docs/build/build-metrics-intro) for more info about MetricFlow and its components. 
 - [dbt Semantic Layer integrations page](https://www.getdbt.com/product/semantic-layer-integrations) for information about the available partner integrations.
-
-

From 2fd2e752f9db85414afd1258791f0b1f2d8f4a45 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 21 Jul 2023 16:16:32 +0100
Subject: [PATCH 240/703] Update
 website/docs/docs/dbt-versions/product-lifecycles.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-versions/product-lifecycles.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/product-lifecycles.md b/website/docs/docs/dbt-versions/product-lifecycles.md
index 65aad0ad333..71f33110eb2 100644
--- a/website/docs/docs/dbt-versions/product-lifecycles.md
+++ b/website/docs/docs/dbt-versions/product-lifecycles.md
@@ -14,7 +14,7 @@ Any dbt feature will fall into one of the following lifecycle states:
 
 ### dbt Cloud
 
-- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These may not be feature-complete or fully stable. There may still be some planned additions and modifications to product behaviors while in Beta. Breaking changes may occur – although we will do our best to communicate them in advance, we may not always be able to do so. Beta features may not be fully documented, technical support may be limited, and service level objectives (SLOs) may not be provided. Download the [Beta Features Terms and Conditions](/assets/beta-tc.pdf) for more info.
+- **Beta:** Beta features may be made available for the purpose of customer testing and evaluation. These might not be feature-complete or fully stable. There might still be some planned additions and modifications to product behaviors while in beta. Breaking changes could occur &mdash; although we will do our best to communicate them in advance, we might not always be able to do so. Beta features might not be fully documented, technical support might be limited, and service level objectives (SLOs) might not be provided. Download the [Beta Features Terms and Conditions](/assets/beta-tc.pdf) for more details.
 
 - **Preview (Private or Public):**  Preview features are stable and can be considered for production deployments. There may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality to Preview features that is not backward compatible. Preview features include documentation, technical support, and include service level objectives (SLOs). Features in Preview are generally provided at no extra cost, although they may become paid features in their Generally Available state.
 

From 5c68543ed8a8e7a1e337d924e305903f1195eedb Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 21 Jul 2023 10:23:08 -0700
Subject: [PATCH 241/703] Update
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 3f8a910461c..bb793edf4c7 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -59,7 +59,7 @@ models:
 Some options that could previously be specified before a sub-command can now only be specified afterward. For example, `dbt --profiles-dir . run` isn't valid anymore, and instead, you need to use `dbt run --profiles-dir .`
 
 
-The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object, otherwise, some of your dbt commands will never finish. For example:
+The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object; otherwise, some of your dbt commands will never finish. For example:
 
 ```sql
 {{ return(load_result('collect_freshness')) }}

From 71e4be2e4eddf5763f52590e902099475201e6b0 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Fri, 21 Jul 2023 12:11:45 -0700
Subject: [PATCH 242/703] add specs + timespine

---
 website/docs/docs/build/dimensions.md           | 11 ++++++++++-
 website/docs/docs/build/entities.md             |  9 +++++++++
 .../docs/docs/build/metricflow-time-spine.md    | 17 +++++++++++++++++
 website/docs/docs/build/metrics-overview.md     | 14 +++++++-------
 website/docs/docs/build/semantic-models.md      | 15 +++++++++++++++
 5 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index abe769e54a1..6338646f31f 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -14,7 +14,16 @@ In a data platform, dimensions is part of a larger structure called a semantic m
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-Refer to the following semantic model example:
+The complete spec for dimensions is below:
+```yaml
+dimensions:
+    - name: The name [Required]
+      type: Categorical or Time [Required]
+      type_params: specific type params such as if the time is primary or used as a partition [Required]
+      description: same as always [Optional]
+      expr: the column name or expression. If not provided the defult is the dimension name [Optional]
+```
+Refer to the following example to see how dimensions are used in a semantic model:
 
 ```yaml
 semantic_models:
diff --git a/website/docs/docs/build/entities.md b/website/docs/docs/build/entities.md
index 1e7f2ff878d..b3af052ca50 100644
--- a/website/docs/docs/build/entities.md
+++ b/website/docs/docs/build/entities.md
@@ -28,6 +28,15 @@ MetricFlow's join logic depends on the entity `type` you use, and it also determ
 * **Foreign &mdash;** A foreign key can include zero, one, or multiple instances of the same record. Null values may also be present.
 * **Natural &mdash;** Natural keys are column or combination of columns in a table that uniquely identify a record based on real-world data. For instance, in a sales_person_department dimension table, the sales_person_id can serve as a natural key.
 
+The complete spec for entities is below:
+```yaml
+entities:
+  - name: the name # think transaction [Required]
+    type: primary # or natural or foreign or unique [Required]
+    description: a description of the field or role the entity takes in this table [Optional]
+    expr: the field that denotes that entity (transaction_id). If not specified will default to name [Optional]
+```
+
 Here's an example of how to define entities in a semantic model:
 
 ``` yaml
diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 607df692bc9..73520ae513f 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -28,5 +28,22 @@ final as (
 select *
 from final
 ```
+```sql
+--- BigQuery supports DATE() instead of TO_DATE(). Use this model if you're using BigQuery
+with days as (
+    {{dbt_utils.date_spine('day'
+    , "DATE(2000,01,01)"
+    , "DATE(2030,01,01)"
+    )
+    }}
+),
+
+final as (
+    select cast(date_day as date) as date_day
+    from days
+)
 
+select *
+from final
+```
 You only need to include the `date_day` column in the table. MetricFlow can handle broader levels of detail, but it doesn't currently support finer grains.
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 6375054ed5c..ec0861b93e3 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -20,14 +20,14 @@ Here's a complete example of the metrics spec configuration:
 
 ```
 metrics:
-  - name: metric name
-    description: same as always
-    type: the type of the metric
-    type_params:
+  - name: metric name [Required]
+    description: same as always [Optional]
+    type: the type of the metric [Required]
+    type_params: [Required]
         - specific properties for the metric type
-    configs: here for `enabled`
-    label: The display name for your metric. This value will be shown in downstream tools.
-    filter: |
+    configs: here for `enabled`[Optional]
+    label: The display name for your metric. This value will be shown in downstream tools. [Required]
+    filter: | [Optional]
       {{  dimension('name') }} > 0 and {{ dimension(' another name') }} is not null
 
 ```
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index a304944a440..16c78a17c2e 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -23,6 +23,21 @@ You can configure semantic models in your dbt project directory in a `YAML` file
 
 
 ## Semantic models components
+The complete spec for semantic models is below:
+```yaml
+semantic_models:
+  - name: the_name_of_the_semantic_model [Required]
+    description: same as always [Optional]
+    model: ref('some_model') [Required]
+    default: [Required]
+        agg_time_dimension: dimension_name [Required if the model contains dimensions]
+    entities: [Required]
+       - see more information in entities
+    measures:[Optional]
+       - see more information in measures section
+    dimensions:[Required]
+       - see more information in dimensions section
+```
 
 The following example displays a complete configuration and detailed descriptions of each field:
 

From c46f9d0baaebe912942ea371611c1e21c9227ba8 Mon Sep 17 00:00:00 2001
From: Gleb Mezhanskiy <glebmezh@users.noreply.github.com>
Date: Fri, 21 Jul 2023 15:39:26 -0400
Subject: [PATCH 243/703] Add Datafold to 3rd-party column-level lineage
 providers

---
 website/docs/terms/data-lineage.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/terms/data-lineage.md b/website/docs/terms/data-lineage.md
index 41779e77702..22dc03d4d61 100644
--- a/website/docs/terms/data-lineage.md
+++ b/website/docs/terms/data-lineage.md
@@ -79,7 +79,7 @@ DAGs shouldn’t be dependent on manual updates. Instead, your DAG should be aut
 
 ### Third-party tooling
 
-Data teams may also choose to use third-party tools  with  lineage capabilities such as [Atlan](https://ask.atlan.com/hc/en-us/articles/4433673207313-How-to-set-up-dbt-Cloud), Alation, [Collibra](https://marketplace.collibra.com/listings/dbt-lineage-to-collibra-integration/), Metaphor, [Monte Carlo](https://docs.getmontecarlo.com/docs/dbt-cloud), [Select Star](https://docs.selectstar.com/integrations/dbt/dbt-cloud), or [Stemma](https://docs.stemma.ai/docs/stemma/getting-started/what-we-need-from-you/dbt-integration/). These tools often integrate directly with your data pipelines and dbt workflows and offer zoomed-in data lineage capabilities such as column-level or business logic-level lineage.
+Data teams may also choose to use third-party tools  with  lineage capabilities such as [Atlan](https://ask.atlan.com/hc/en-us/articles/4433673207313-How-to-set-up-dbt-Cloud), Alation, [Collibra](https://marketplace.collibra.com/listings/dbt-lineage-to-collibra-integration/), [Datafold](https://www.datafold.com/column-level-lineage), Metaphor, [Monte Carlo](https://docs.getmontecarlo.com/docs/dbt-cloud), [Select Star](https://docs.selectstar.com/integrations/dbt/dbt-cloud), or [Stemma](https://docs.stemma.ai/docs/stemma/getting-started/what-we-need-from-you/dbt-integration/). These tools often integrate directly with your data pipelines and dbt workflows and offer zoomed-in data lineage capabilities such as column-level or business logic-level lineage.
 
 ## Data lineage challenges
 

From c4cf6b4d41c5d1670f0238c4c39af8ec5c6aed93 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Fri, 21 Jul 2023 14:23:48 -0700
Subject: [PATCH 244/703] add config

---
 website/docs/docs/build/metricflow-time-spine.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 73520ae513f..71ab0e77c16 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -12,6 +12,7 @@ To create this table, you need to create a model in your dbt project called `met
 
 ```sql
 -- metricflow_time_spine.sql
+{{config(materialized='table')}}
 with days as (
     {{dbt_utils.date_spine('day'
     , "to_date('01/01/2000','mm/dd/yyyy')"
@@ -30,6 +31,7 @@ from final
 ```
 ```sql
 --- BigQuery supports DATE() instead of TO_DATE(). Use this model if you're using BigQuery
+{{config(materialized='table')}}
 with days as (
     {{dbt_utils.date_spine('day'
     , "DATE(2000,01,01)"

From 822fa1d173e5be8de4c982f04e630db889162ca0 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Fri, 21 Jul 2023 16:10:37 -0700
Subject: [PATCH 245/703] Update quickstart-sl.md

made just a few tweaks to the query section
---
 website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 5e85f87e792..ef598b3da96 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -157,10 +157,11 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
 
-1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
-2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice. 
+You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer.
 
-Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to use the JDBC API and what tools you can query it with.
+
+2. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 

From 4c6116f839537bc4c4804f0561560ea63d3c9848 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Sun, 23 Jul 2023 15:35:33 -0700
Subject: [PATCH 246/703] Update contract with better description (#3797)

## What are you changing in this pull request and why?

Thanks to @joellabes for a better one!

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page
---
 website/docs/reference/resource-configs/contract.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/contract.md b/website/docs/reference/resource-configs/contract.md
index 91d87fd2716..66072fc8b89 100644
--- a/website/docs/reference/resource-configs/contract.md
+++ b/website/docs/reference/resource-configs/contract.md
@@ -1,6 +1,6 @@
 ---
 resource_types: [models]
-description: "Read this guide to understand the contract configuration in dbt."
+description: "When the contract configuration is enforced, dbt will ensure that your model's returned dataset exactly matches the attributes you have defined in yaml, such as name and data_type, as well as any additional constraints supported by the data platform."
 datatype: "{<dictionary>}"
 default_value: {contract: false}
 id: "contract"

From 286cb69bedc584d24d32d8b8a9e6a84a8d308eba Mon Sep 17 00:00:00 2001
From: Mike Derksen <85023255+mike-6383@users.noreply.github.com>
Date: Mon, 24 Jul 2023 09:50:43 +0100
Subject: [PATCH 247/703] Update incremental-models.md

Incremental models do not necessarily fail is the unique_key is not unique. Changes docs to better reflect this.
---
 website/docs/docs/build/incremental-models.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index 29c7c8c585f..dd20ca36a53 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -108,7 +108,7 @@ When you define a `unique_key`, you'll see this behavior for each row of "new" d
 * If the same `unique_key` is present in the "new" and "old" model data, dbt will update/replace the old row with the new row of data. The exact mechanics of how that update/replace takes place will vary depending on your database, [incremental strategy](#about-incremental_strategy), and [strategy specific configs](#strategy-specific-configs).
 * If the `unique_key` is _not_ present in the "old" data, dbt will insert the entire row into the table.
 
-Please note that if there's a `unique_key` with more than one row in either the existing target table or the new incremental rows, the incremental model run will fail. Your database and [incremental strategy](#about-incremental_strategy) will determine the specific error that you see, so if you're having issues running an incremental model, it's a good idea to double check that the unique key is truly unique in both your existing database table and your new incremental rows. You can [learn more about surrogate keys here](/terms/surrogate-key).
+Please note that if there's a unique_key with more than one row in either the existing target table or the new incremental rows, the incremental model may fail depending on your database and [incremental strategy](#about-incremental_strategy). If you're having issues running an incremental model, it's a good idea to double check that the unique key is truly unique in both your existing database table and your new incremental rows. You can [learn more about surrogate keys here](/terms/surrogate-key).
 
 :::info
 While common incremental strategies, such as`delete+insert` + `merge`, might use `unique_key`, others don't. For example, the `insert_overwrite` strategy does not use `unique_key`, because it operates on partitions of data rather than individual rows. For more information, see [About incremental_strategy](#about-incremental_strategy).

From 83331701b48398db67e2766fcd35e4e9fa90167b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 24 Jul 2023 10:49:18 +0100
Subject: [PATCH 248/703] fix yaml and turn jordan's edits to table

---
 website/docs/docs/build/dimensions.md         |  32 +++---
 website/docs/docs/build/entities.md           |  10 +-
 website/docs/docs/build/metrics-overview.md   | 102 +++++++++---------
 website/docs/docs/build/semantic-models.md    |  34 +++---
 website/docs/docs/build/sl-getting-started.md |  41 +++----
 5 files changed, 116 insertions(+), 103 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index 6338646f31f..c0614a82df1 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -14,15 +14,27 @@ In a data platform, dimensions is part of a larger structure called a semantic m
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-The complete spec for dimensions is below:
+All dimensions require a `name`, `type` and in most cases, an `expr` parameter. 
+
+| Name | Parameter | Field type |
+| --- | --- | --- |
+| `name` |  Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the `expr` parameter. <br /><br /> Dimension names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses [joins](/docs/build/join-logic) to identify the right dimension. | Required |
+| `type` | Specifies the type of group created in the semantic model. There are three types:<br /><br />&mdash; Categorical: Group rows in a table by categories like geography, product type, color, and so on. <br />&mdash; Time: Point to a date field in the data platform, and must be of type TIMESTAMP or equivalent in the data platform engine. <br />&mdash; Slowly-changing dimensions: Analyze metrics over time and slice them by groups that change over time, like sales trends by a customer's country. | Required |
+| `type_params` | Specific type params such as if the time is primary or used as a partition | Required |
+| `description` | Description of the dimension | Optional |
+| `expr` | Defines the underlying column or SQL query for a dimension. If no `expr` is specified, MetricFlow will use the column with the same name as the group. You can use column name itself to input a SQL expression. | Optional |
+
+Refer to the following for the complete specification for dimensions:
+
 ```yaml
 dimensions:
-    - name: The name [Required]
-      type: Categorical or Time [Required]
-      type_params: specific type params such as if the time is primary or used as a partition [Required]
-      description: same as always [Optional]
-      expr: the column name or expression. If not provided the defult is the dimension name [Optional]
+  - name: name of the group that will be visible to the user in downstream tools
+    type: Categorical or Time
+    type_params: specific type params such as if the time is primary or used as a partition
+    description: same as always
+    expr: the column name or expression. If not provided the default is the dimension name
 ```
+
 Refer to the following example to see how dimensions are used in a semantic model:
 
 ```yaml
@@ -49,14 +61,6 @@ semantic_models:
       expr: case when quantity > 10 then true else false end
 ```
 
-All dimensions require a `name`, `type` and in most cases, an `expr` parameter. 
-
-| Name | Parameter | Field type |
-| --- | --- | --- |
-| `name` |  Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the `expr` parameter. <br /><br /> &mdash; dimensions names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses [joins](/docs/build/join-logic) to identify the right dimension. | Required |
-| `type` | Specifies the type of group created in the semantic model. There are three types:<br /><br />&mdash; Categorical: Group rows in a table by categories like geography, product type, color, and so on. <br />&mdash; Time: Point to a date field in the data platform, and must be of type TIMESTAMP or equivalent in the data platform engine. <br />&mdash; Slowly-changing dimensions: Analyze metrics over time and slice them by groups that change over time, like sales trends by a customer's country. | Required |
-| `expr` | Defines the underlying column or SQL query for a dimension. If no `expr` is specified, MetricFlow will use the column with the same name as the group. You can use column name itself to input a SQL expression. | Optional |
-
 ## Dimensions types
 
 Dimensions has three types. This section further explains the definitions and provides examples.
diff --git a/website/docs/docs/build/entities.md b/website/docs/docs/build/entities.md
index b3af052ca50..464fa2c3b8c 100644
--- a/website/docs/docs/build/entities.md
+++ b/website/docs/docs/build/entities.md
@@ -31,10 +31,12 @@ MetricFlow's join logic depends on the entity `type` you use, and it also determ
 The complete spec for entities is below:
 ```yaml
 entities:
-  - name: the name # think transaction [Required]
-    type: primary # or natural or foreign or unique [Required]
-    description: a description of the field or role the entity takes in this table [Optional]
-    expr: the field that denotes that entity (transaction_id). If not specified will default to name [Optional]
+  - name: transaction     ## Required
+    type: primary or natural or foreign or unique ## Required
+    description: a description of the field or role the entity takes in this table ## Optional
+    expr: the field that denotes that entity (transaction_id).  ## Optional
+          If not specified will default to name 
+
 ```
 
 Here's an example of how to define entities in a semantic model:
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index ec0861b93e3..a7f0d1d3d21 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -10,27 +10,31 @@ Once you've created your semantic models, it's time to start adding metrics! Met
 
 The keys for metrics definitions are: 
 
-* `name`: Provide the reference name for the metric. This name must be unique amongst all metrics.  
-* `type`: Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)). 
-* `type_params`: Additional parameters used to configure metrics. `type_params` are different for each metric type. 
-* `constraint`: For any type of metric, you may optionally include a constraint string, which applies a dimensional filter when computing the metric. You may think of this as your WHERE clause.  
-* `meta`: Additional metadata you want to add to your metric. 
+| Component | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
+| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Optional |
+| `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
+| `constraint` | For any type of metric, you may optionally include a constraint string, which applies a dimensional filter when computing the metric. You may think of this as your WHERE clause.   | Required |
+|  `meta` | Additional metadata you want to add to your metric. |
+
 
 Here's a complete example of the metrics spec configuration:
 
-```
+```yaml
 metrics:
-  - name: metric name [Required]
-    description: same as always [Optional]
-    type: the type of the metric [Required]
-    type_params: [Required]
-        - specific properties for the metric type
-    configs: here for `enabled`[Optional]
-    label: The display name for your metric. This value will be shown in downstream tools. [Required]
-    filter: | [Optional]
-      {{  dimension('name') }} > 0 and {{ dimension(' another name') }} is not null
-
+  - name: metric name                     ## Required
+    description: same as always           ## Optional
+    type: the type of the metric          ## Required
+    type_params:                          ## Required
+      - specific properties for the metric type
+    configs: here for `enabled`           ## Optional
+    label: The display name for your metric. This value will be shown in downstream tools. ## Required
+    filter: |                             ## Optional            
+      {{  dimension('name') }} > 0 and {{ dimension(' another name') }} is not
+      null
 ```
+
 This page explains the different supported metric types you can add to your dbt project. 
 <!--
 - [Cumulative](#cumulative-metrics) — Cumulative metrics aggregate a measure over a given window.
@@ -40,7 +44,6 @@ This page explains the different supported metric types you can add to your dbt
 - [Ratio](#ratio-metrics) — Create a ratio out of two measures. 
 -->
 
-
 ### Cumulative metrics 
 
 [Cumulative metrics](/docs/build/cumulative) aggregate a measure over a given window. If no window is specified, the window would accumulate the measure over all time. **Note**m, you will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
@@ -48,15 +51,16 @@ This page explains the different supported metric types you can add to your dbt
 ```yaml
 # Cumulative metrics aggregate a measure over a given window. The window is considered infinite if no window parameter is passed (accumulate the measure over all time)
 metrics:
-- name: wau_rolling_7
-  owners:
-    - support@getdbt.com
-  type: cumulative
-  type_params:
-    measures:
-      - distinct_users
+  - name: wau_rolling_7
+    owners:
+      - support@getdbt.com
+    type: cumulative
+    type_params:
+      measures:
+        - distinct_users
     #Omitting window will accumulate the measure over all time
-    window: 7 days
+      window: 7 days
+      
 ```
 ### Derived metrics
 
@@ -66,13 +70,13 @@ metrics:
 metrics:
   - name: net_sales_per_user
     type: derived
-    type_params: 
+    type_params: null
     metrics:
       - name: gross_sales # these are all metrics (can be a derived metric, meaning building a derived metric with derived metrics)
       - name: cogs
       - name: users
-      filter: is_active # Optional additional constraint
-      alias: active_users # Optional alias to use in the expr
+        filter: is_active # Optional additional constraint
+        alias: active_users # Optional alias to use in the expr
 ```
 <!-- not supported
 ### Expression metrics
@@ -104,47 +108,43 @@ metrics:
       - support@getdbt.com
 # Ratio metrics create a ratio out of two measures.
 # Define the measures from the semantic model as numerator or denominator
-    type: ratio  
+    type: ratio
     type_params:
       numerator: cancellations_usd
       denominator: transaction_amount_usd
-      filter: | # add optional constraint string. This applies to both the numerator and denominator
-      {{ dimension('country', entity_path=['customer']) }} = 'MX'
-
+      filter: |     # add optional constraint string. This applies to both the numerator and denominator
+        {{ dimension('country', entity_path=['customer']) }} = 'MX'
   - name: enterprise_cancellation_rate
     owners:
       - support@getdbt.com
- # Ratio metrics create a ratio out of two measures. 
- # Define the measures from the semantic model as numerator or denominator
-    type: ratio 
+      # Ratio metrics create a ratio out of two measures. 
+      # Define the measures from the semantic model as numerator or denominator
+    type: ratio
     type_params:
-      numerator: 
+      numerator:
         name: cancellations_usd
-        filter: tier = 'enterprise' #constraint only applies to the numerator
-      denominator: transaction_amount_usd 
-        filter: | # add optional constraint string. This applies to both the numerator and denominator
-        {{ dimension('country', entity_path=['customer']) }} = 'MX'
-  
+        filter: tier = 'enterprise'  # constraint only applies to the numerator
+      denominator: transaction_amount_usd
+      filter: |   #  add optional constraint string. This applies to both the numerator and denominator
+        {{ dimension('country', entity_path=['customer']) }} = 'MX'  
 ```
 ### Simple metrics
 
 [Simple metrics](/docs/build/simple) point directly to a measure. You may think of it as a function that takes only one measure as the input.
 
+- `name`&mdash; Use this parameter to define the reference name of the metric. The name must be unique amongst metrics and can include lowercase letters, numbers, and underscores. You can use this name to call the metric from the dbt Semantic Layer API.
+
 <!--create_metric not supported yet
 **Note:** If you've already defined the measure using the `create_metric: True` parameter, you don't need to create simple metrics.  However, if you would like to include a constraint on top of the measure, you will need to create a simple type metric. 
 -->
 ```yaml
-metrics: 
-# Define the reference name of the metric.  
-# This name must be unique amongst metrics and can include lowercase letters, numbers, and underscores. 
-# This name is used to call the metric from the dbt Semantic Layer API.
-  - name: cancellations 
-    type: simple 
+metrics:
+  - name: cancellations
+    type: simple
     type_params:
-  # Specify the measure you are creating a proxy for. 
-      measure: cancellations_usd 
-      filter: | 
-      {{dimension('value')}} > 100 and {{dimension('acquisition', entity_path=['user'])}}
+      measure: cancellations_usd  # Specify the measure you are creating a proxy for. 
+      filter: |
+        {{dimension('value')}} > 100 and {{dimension('acquisition', entity_path=['user'])}}
 ```
 
 ### Further configuration 
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index 16c78a17c2e..9b74b5b4813 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -12,30 +12,34 @@ Semantic models serve as the foundation for defining data in MetricFlow, which p
 
 Each semantic model corresponds to a dbt model in your DAG. Therefore you will have one YAML config for each semantic model in your dbt project. You can create multiple semantic models out of a single dbt model, as long as you give each semantic model a unique name. 
 
-You can configure semantic models in your dbt project directory in a `YAML` file. Depending on your project structure, you can nest semantic models under a `metrics:` folder or organize them under project sources. Semantic models have 6 components and this page explains the definitions with some examples:
+You can configure semantic models in your dbt project directory in a `YAML` file. Depending on your project structure, you can nest semantic models under a `metrics:` folder or organize them under project sources. 
 
-1. [Name](#name) &mdash; Unique name for the semantic model. 
-1. [Description](#description) &mdash; Includes important details in the description.
-1. [Model](#model) &mdash;  Specifies the dbt model for the semantic model using the `ref` function.
-1. [Entities](#entities) &mdash;  Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter.
-1. [Dimensions](#dimensions) &mdash; Different ways to group or slice data for a metric, they can be `time-based` or `categorical`.
-1. [Measures](#measures) &mdash; Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics.
+Semantic models have 6 components and this page explains the definitions with some examples:
 
+| Component | Description | Type |
+| --------- | ----------- | ---- |
+| [Name](#name) | Unique name for the semantic model | Required |
+| [Description](#description) | Includes important details in the description | Optional |
+| [Model](#model) | Specifies the dbt model for the semantic model using the `ref` function | Required |
+| [Entities](#entities) | Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter | Required |
+| [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time-based` or `categorical` | Required |
+| [Measures](#measures) | Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics | Optional |
 
 ## Semantic models components
+
 The complete spec for semantic models is below:
 ```yaml
 semantic_models:
-  - name: the_name_of_the_semantic_model [Required]
-    description: same as always [Optional]
-    model: ref('some_model') [Required]
-    default: [Required]
-        agg_time_dimension: dimension_name [Required if the model contains dimensions]
-    entities: [Required]
+  - name: the_name_of_the_semantic_model      ## Required
+    description: same as always               ## Optional
+    model: ref('some_model')                  ## Required
+    default:                                  ## Required
+        agg_time_dimension: dimension_name    ## Required if the model contains dimensions
+    entities:                                 ## Required
        - see more information in entities
-    measures:[Optional]
+    measures:                                 ## Optional
        - see more information in measures section
-    dimensions:[Required]
+    dimensions:                               ## Required
        - see more information in dimensions section
 ```
 
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 5b8a40904f5..29134b3cf59 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -28,11 +28,15 @@ New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/
 
 ## Install MetricFlow
 
-Before you begin, make sure you install the `metricflow` and [dbt adapter](/docs/supported-data-platforms) via PyPI in the CLI. To install them, open the command line interface (CLI) and use the pip install command `pip install "dbt-metricflow[your_adapter_name]"`.
+Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
 
-Note that specifying `[your_adapter_name]` is required.  This is because you must install MetricFlow as an extension of a dbt adapter. For example, for a Snowflake adapter, run `pip install "dbt-metricflow[snowflake]"`.
+Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
+
+- Create or activate your virtual environment. `python -m venv venv`
+- `pip install "dbt-metricflow[your_adapter_name]"`
+  * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
-Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon).
+Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). 
 
 ## Create a semantic model
 
@@ -52,26 +56,26 @@ semantic_models:
 2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `transaction_id`, and so on.
 
 ```yaml
-  entities:
-    - name: transaction
-      type: primary
-      expr: id_transaction
-    - name: customer
-      type: foreign
-      expr: id_customer
+entities:
+  - name: transaction
+    type: primary
+    expr: id_transaction
+  - name: customer
+    type: foreign
+    expr: id_customer
   ```
 
 3. Define your dimensions and measures. dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
 
 ```yaml
 measures:
-    - name: transaction_amount_usd
-      description: The total USD value of the transaction.
-      agg: sum
-  dimensions:
-    - name: is_large
-      type: categorical
-      expr: case when transaction_amount_usd >= 30 then true else false end
+  - name: transaction_amount_usd
+    description: The total USD value of the transaction.
+    agg: sum
+dimensions:
+  - name: is_large
+    type: categorical
+    expr: case when transaction_amount_usd >= 30 then true else false end
 ```
 
 :::tip
@@ -94,11 +98,10 @@ Now that you've created your first semantic model, it's time to define your firs
 The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
 
 ```yaml
----
 metrics:
   - name: transaction_amount_usd
     type: simple
-    type_params:
+    type_params: null
     measure: transaction_amount_usd
 ```
 

From 61ff5490c3951764de18e61804b5aed3f6de53d4 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 24 Jul 2023 10:56:18 +0100
Subject: [PATCH 249/703] Update dimensions.md

fix lines and typos
---
 website/docs/docs/build/dimensions.md | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index c0614a82df1..d154141791f 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -47,7 +47,6 @@ semantic_models:
 # --- entities ---
   entities: 
       ...
-
 # --- measures --- 
   measures: 
       ... 
@@ -63,7 +62,7 @@ semantic_models:
 
 ## Dimensions types
 
-Dimensions has three types. This section further explains the definitions and provides examples.
+Dimensions have three types. This section further explains the definitions and provides examples.
 
 1. [Categorical](#categorical)
 1. [Time](#time)
@@ -92,9 +91,9 @@ To use BigQuery as your data platform, time dimensions columns need to be in the
 
 <TabItem value="is_primary" label="is_primary">
 
-To specify the default time dimensions for a measure or metric in MetricFlow, set the `is_primary` parameter to True. If you have multiple time dimensions in your semantic model, the non-primary ones should have `is_primary` set to False. To assign a non-primary time dimensions to a measure, use the `agg_time_dimension` parameter and refer to the time dimensions defined in the section. 
+To specify the default time dimensions for a measure or metric in MetricFlow, set the `is_primary` parameter to True. If your semantic model has multiple time dimensions, the non-primary ones should have `is_primary` set to False. To assign non-primary time dimensions to a measure, use the `agg_time_dimension` parameter and refer to the time dimensions defined in the section. 
 
-In the provided example, the semantic model has two time groups, `created_at` and `deleted_at`, with `created_at` being the primary time dimensions through `is_primary: True`. The `users_created` measure defaults to the primary time dimensions, while the `users_deleted` measure uses `deleted_at` as its time group. 
+In the provided example, the semantic model has two-time groups, `created_at` and `deleted_at`, with `created_at` being the primary time dimension through `is_primary: True`. The `users_created` measure defaults to the primary time dimensions, while the `users_deleted` measure uses `deleted_at` as its time group. 
 
 ```yaml
 dimensions: 
@@ -123,7 +122,7 @@ measures:
     agg: sum
 ```
 
-When querying one or more metrics in MetricFlow using the CLI, the default time dimensions for a single metric is the primary time dimension, which can be referred to as metric_time or the dimensions's name. Multiple time groups can be used in separate metrics, such as users_created which uses created_at, and users_deleted which uses deleted_at.
+When querying one or more metrics in MetricFlow using the CLI, the default time dimension for a single metric is the primary time dimension, which can be referred to as metric_time or the dimensions' name. Multiple time groups can be used in separate metrics, such as users_created which uses created_at, and users_deleted which uses deleted_at.
 
       ```
        mf query --metrics users_created,users_deleted --dimensions metric_time --order metric_time 
@@ -217,7 +216,7 @@ measures:
 Currently, there are limitations in supporting SCD's. 
 :::
 
-MetricFlow, supports joins against dimensions values in a semantic model built on top of an SCD Type II table (slowly changing dimension) Type II table. This is useful when you need a particular metric sliced by a group that changes over time, such as the historical trends of sales by a customer's country. 
+MetricFlow supports joins against dimensions values in a semantic model built on top of an SCD Type II table (slowly changing dimension) Type II table. This is useful when you need a particular metric sliced by a group that changes over time, such as the historical trends of sales by a customer's country. 
 
 As their name suggests SCD Type II are groups that change values at a coarser time granularity. This results in a range of valid rows with different dimensions values for a given metric or measure. MetricFlow associates the metric with the first (minimum) available dimensions value within a coarser time window, such as month. By default, MetricFlow uses the group that is valid at the beginning of the time granularity.
 
@@ -265,7 +264,7 @@ Take note of the extra arguments under `validity_params`: `is_start` and `is_end
 ```yaml 
 semantic_models:
   - name: sales_person_tiers
-    description: SCD Type II table of tiers for sales people 
+    description: SCD Type II table of tiers for salespeople 
     model: {{ref(sales_person_tiers)}}
     defaults:
       agg_time_dimension: tier_start
@@ -350,7 +349,7 @@ In the sales tier example,  For instance, if a salesperson was Tier 1 from 2022-
 
 <TabItem value="example2" label="SCD table example 2">
 
-This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about sales persons' tier and the time length of that tier. Suppose you have the underlying SCD table:
+This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about salespersons' tier and the time length of that tier. Suppose you have the underlying SCD table:
 
 | sales_person_id | tier | start_date | end_date | 
 |-----------------|------|------------|----------|

From 6bef3470f8c38d543794311ba3b44590ee72bf9b Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 24 Jul 2023 11:01:13 +0100
Subject: [PATCH 250/703] Update measures.md

---
 website/docs/docs/build/measures.md | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index 3483e4d823d..13758fa094a 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -15,26 +15,25 @@ Measures are aggregations performed on columns in your model. They can be used a
 | [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `count_distinct`, and `sum_boolean`. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
-| [`agg_params`] | specific aggregation properties such as a percentile. | [Optional]|
-| [`agg_time_dimension`] | The time field. Defaults to the default agg time dimension for the semantic model.  | [Optional] |
-| [`non_additive_dimension`] | Use these configs when you need non-additive dimensions. | [Optional]|
-| [`label`] | How the metric appears in project docs and downstream integrations. | [Required]|
+| `agg_params` | specific aggregation properties such as a percentile. | Optional |
+| `agg_time_dimension` | The time field. Defaults to the default agg time dimension for the semantic model.  | Optional |
+| `label` | How the metric appears in project docs and downstream integrations. | Required |
 
 
 ## Measure spec
 
 An example of the complete YAML measures spec is below. The actual configuration of your measures will depend on the aggregation you're using.
 
-```bash
+```yaml
 measures:
-      - name: The name of the measure # think transaction_total. If `expr` isn't present then this is the expected name of the column [Required]
-        description: same as always [Optional]
-        agg: the aggregation type.  #think average, sum, max, min, etc.[Required]
-        expr: the field # think transaction_total or some other name you might want to alias [Optional]
-        agg_params: specific aggregation properties such as a percentile [Optional]
-        agg_time_dimension: The time field. Defaults to the default agg time dimension for the semantic model. [Optional]
-        non_additive_dimension: Use these configs when you need non-additive dimensions. [Optional]
-        label: How the metric appears in project docs and downstream integrations. [Required]
+  - name: The name of the measure
+    description: 'same as always' ## Optional
+    agg: the aggregation type.
+    expr: the field
+    agg_params: 'specific aggregation properties such as a percentile'  ## Optional
+    agg_time_dimension: The time field. Defaults to the default agg time dimension for the semantic model. ##  Optional
+    non_additive_dimension: 'Use these configs when you need non-additive dimensions.' ## Optional
+    label: How the metric appears in project docs and downstream integrations. ## Required
 ```
 
 ### Name 
@@ -226,7 +225,7 @@ semantic_models:
           window_choice: max
           window_groupings: 
             - user_id 
----
+
 metrics:
   - name: mrr_end_of_month
     type: simple

From 1f939a8d5145dec7523f092257761dc8d5bc9776 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 24 Jul 2023 11:31:02 +0100
Subject: [PATCH 251/703] update gsg

---
 website/docs/docs/build/sl-getting-started.md | 50 ++++++++-----------
 .../use-dbt-semantic-layer/quickstart-sl.md   | 21 +++++---
 website/snippets/_new-sl-setup.md             |  4 +-
 3 files changed, 36 insertions(+), 39 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 71145beed8f..de5c77fb5f8 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -14,12 +14,10 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
 - [Define metrics](#define-metrics) with MetricFlow
-- [Test metrics](#test-metrics) with the MetricFlow
+- [Test and query metrics](#test-and-query-metrics) with MetricFlow
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#set-up-dbt-semantic-layer) in dbt Cloud
 - [Connect and query API](#connect-and-query-api) with dbt Cloud
-- [Test and query with the CLI](#test-and-query-with-the-cli) 
-  * For Developer plans or dbt Core users only
 
 ## Prerequisites
 
@@ -41,12 +39,7 @@ New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/
 
 ## Create a semantic model
 
-Before you begin, we recommend you learn about more about [MetricFlow](/docs/build/about-metricflow) and its key concepts. There are two main objects: 
-
-- [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
-- [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
-
-This step will guide you through setting up your semantic models, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures),  in your editor of choice.
+This step will guide you through setting up your semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
 
 1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
 
@@ -117,20 +110,32 @@ metrics:
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
-## Test metrics 
+## Test and query metrics
 
 :::note
-For public beta, you can't yet test metrics using the dbt Cloud IDE (support coming soon). To test metrics, you must install the [MetricFlow CLI](/docs/build/metricflow-cli).
-  * dbt Core or Developer plan users can skip to [Test and query with the CLI](#test-and-query-with-the-cli) for detailed steps. 
+For public beta, testing and querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). However, install the [MetricFlow CLI](/docs/build/metricflow-cli) to test and query metrics locally.
+  * dbt Core or Developer plan users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
 :::
 
-1. To test your metrics, make sure you have the MetricFlow CLI installed and up to date.
+Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for more detailed MetricFlow commands.
+
+**Test using the CLI:**
+
+1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
 3. Run `mf validate-configs` to validate the changes before committing them
 
+**Query using the CLI:**
+
+1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
+2. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
+3. Commit and merge the code changes that contain the metric definitions.
+
 ## Run a production job
 
-Once you’ve defined metrics in your dbt project, you can perform a job run in your deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
+Before you begin, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
+
+Once you’ve defined metrics in your dbt project, you can perform a job run in your dbt Cloud deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
 
 1. Go to **Deploy** in the navigation header
 2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.
@@ -146,7 +151,7 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query API
 
-Before you begin, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
+You must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America. You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer.
 
 To connect and query your metrics using the dbt Semantic Layer and its API:
 
@@ -155,21 +160,6 @@ To connect and query your metrics using the dbt Semantic Layer and its API:
 
 Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
-## Test and query with the CLI
-
-Before you begin, dbt Core or Developer plan users must first install the [MetricFlow CLI](/docs/build/metricflow-cli). 
-
-**Test using the CLI:**
-
-1. Make sure you have the MetricFlow CLI installed and up to date.
-2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
-3. Run `mf validate-configs` to validate the changes before committing them
-
-**Query using the CLI:**
-
-1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
-2. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
-3. Commit and merge the code changes that contain the metric definitions.
 
 ## Related docs
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index ef598b3da96..d780cbb44fe 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -37,14 +37,21 @@ import SetUp from '/snippets/_v2-sl-prerequisites.md';
 New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) to help you get started!
 :::
 
-## Create a semantic model
+## Install MetricFlow
+
+Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
 
-Before you begin, we recommend you learn about more about [MetricFlow](/docs/build/about-metricflow) and its key concepts. There are two main objects: 
+Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
 
-- [Semantic models](/docs/build/semantic-models) &mdash; Nodes in your semantic graph, connected via entities as edges. MetricFlow takes semantic models defined in YAML configuration files as inputs and creates a semantic graph that you can use to query metrics. 
-- [Metrics](/docs/build/metrics-overview) &mdash; Can be defined in the same YAML files as your semantic models, or split into separate YAML files into any other subdirectories (provided that these subdirectories are also within the same dbt project repo).
+- Create or activate your virtual environment. `python -m venv venv`
+- `pip install "dbt-metricflow[your_adapter_name]"`
+  * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
+ 
+Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). 
+
+## Create a semantic model
 
-This step will guide you through setting up your semantic models, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
+This step will guide you through setting up your semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
 
 1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
 
@@ -117,10 +124,10 @@ metrics:
 
 ## Test metrics
 
-The following steps explain how to test and manually query your metrics.
+The following steps explain how to test and manually query your metrics. Currently, you can only manually test your metrics using the CLI (dbt Cloud IDE support coming soon)
 
 1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
-2. Run `mf validate-configs` to validate the changes before committing them.
+2. In the CLI, run `mf validate-configs` to validate the changes before committing them.
 3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
 4. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
 5. Commit and merge the code changes that contain the metric definitions.
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index f4b7f7763a3..71488507fa1 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -4,7 +4,7 @@ You can set up the dbt Semantic Layer in dbt Cloud at the environment and projec
 If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 :::
 
-1. Create new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
+1. in dbt Cloud, create a new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
     * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 
 2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
@@ -18,7 +18,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 5. Select the deployment environment you want for the Semantic Layer and click **Save**.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="60%" title="Set Up your Semantic Layer configuration."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="Set Up your Semantic Layer configuration."/>
 
 6. You should see connection information that allows you to connect to downstream tools. 
     * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->

From dbeeb7cfd7371d4e2123e5758e094d874d121003 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 24 Jul 2023 12:44:07 +0100
Subject: [PATCH 252/703] Update website/docs/terms/data-lineage.md

---
 website/docs/terms/data-lineage.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/terms/data-lineage.md b/website/docs/terms/data-lineage.md
index 22dc03d4d61..bb3751df3da 100644
--- a/website/docs/terms/data-lineage.md
+++ b/website/docs/terms/data-lineage.md
@@ -79,7 +79,7 @@ DAGs shouldn’t be dependent on manual updates. Instead, your DAG should be aut
 
 ### Third-party tooling
 
-Data teams may also choose to use third-party tools  with  lineage capabilities such as [Atlan](https://ask.atlan.com/hc/en-us/articles/4433673207313-How-to-set-up-dbt-Cloud), Alation, [Collibra](https://marketplace.collibra.com/listings/dbt-lineage-to-collibra-integration/), [Datafold](https://www.datafold.com/column-level-lineage), Metaphor, [Monte Carlo](https://docs.getmontecarlo.com/docs/dbt-cloud), [Select Star](https://docs.selectstar.com/integrations/dbt/dbt-cloud), or [Stemma](https://docs.stemma.ai/docs/stemma/getting-started/what-we-need-from-you/dbt-integration/). These tools often integrate directly with your data pipelines and dbt workflows and offer zoomed-in data lineage capabilities such as column-level or business logic-level lineage.
+Data teams may also choose to use third-party tools with lineage capabilities such as [Atlan](https://ask.atlan.com/hc/en-us/articles/4433673207313-How-to-set-up-dbt-Cloud), Alation, [Collibra](https://marketplace.collibra.com/listings/dbt-lineage-to-collibra-integration/), [Datafold](https://www.datafold.com/column-level-lineage), Metaphor, [Monte Carlo](https://docs.getmontecarlo.com/docs/dbt-cloud), [Select Star](https://docs.selectstar.com/integrations/dbt/dbt-cloud), or [Stemma](https://docs.stemma.ai/docs/stemma/getting-started/what-we-need-from-you/dbt-integration/). These tools often integrate directly with your data pipelines and dbt workflows and offer zoomed-in data lineage capabilities such as column-level or business logic-level lineage.
 
 ## Data lineage challenges
 

From 5be04ac6b4485dc161ac0986b5e227c955c85416 Mon Sep 17 00:00:00 2001
From: David Uforo <79027366+uforodavid@users.noreply.github.com>
Date: Mon, 24 Jul 2023 13:53:03 +0100
Subject: [PATCH 253/703] Update 2-lint-on-push.md

Line 41 has a wrong spelling 'coved' instead of 'covered' and this commit includes the correction.
---
 .../orchestration/custom-cicd-pipelines/2-lint-on-push.md       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md b/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md
index dea3b5b5de3..465994e4442 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md
+++ b/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md
@@ -38,7 +38,7 @@ Below I touch on the important pieces for running a dbt Cloud job, but if you wa
 
 - `on:` - this is used to filter when the pipeline is run. In this example we’re running it on every push except for pushes to branches named `main`. For more filters, checkout [GitHub’s docs](https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows).
 - `runs-on: ubuntu-latest` - this defines the operating system we’re using to run the job
-- `uses:` - remember the virtual servers we coved in the background section? They’re just empty operating systems, so there are two pieces of setup that are needed in order to access the code in your repo, and setup Python correctly on the virtual server. These two actions are called from other repos in GitHub to provide those services. For more information on them, checkout their repos: [actions/checkout](https://github.com/actions/checkout#checkout-v3) and [actions/setup-python](https://github.com/actions/setup-python#setup-python-v3).
+- `uses:` - remember the virtual servers we covered in the background section? They’re just empty operating systems, so there are two pieces of setup that are needed in order to access the code in your repo, and setup Python correctly on the virtual server. These two actions are called from other repos in GitHub to provide those services. For more information on them, checkout their repos: [actions/checkout](https://github.com/actions/checkout#checkout-v3) and [actions/setup-python](https://github.com/actions/setup-python#setup-python-v3).
 - `run:` - this is how we’re telling the GitHub runner to execute the Python script we defined above.
 
 ```yaml

From a17d2ee307b61115612be11d0479379acc8303dc Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 24 Jul 2023 15:34:55 +0100
Subject: [PATCH 254/703] updating commands

---
 .../dbt-cloud-ide/develop-in-the-cloud.md     |  4 +--
 website/docs/reference/dbt-commands.md        | 25 ++++++++++++++++++-
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index a128cbb9bd1..3c33114ac26 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -6,9 +6,7 @@ sidebar_label: Develop in the IDE
 tags: [IDE]
 ---
 
-The dbt Cloud integrated development environment (IDE) is a single interface for building, testing, running, and version-controlling dbt projects from your browser. With the Cloud IDE, you can compile dbt code into SQL and run it against your database directly. The IDE leverages the open-source [dbt-rpc](/reference/commands/rpc) plugin to recompile only the changes made in your project.
-
-
+The dbt Cloud integrated development environment (IDE) is a single interface for building, testing, running, and version-controlling dbt projects from your browser. With the Cloud IDE, you can compile dbt code into SQL and run it against your database directly. The IDE leverages the dbt-server, making the IDE faster and more reliable.
 ## Prerequisites
 
 To develop in the Cloud IDE, make sure you have the following:
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 5b37f13a3fb..b0adf00e212 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -12,6 +12,29 @@ For information about selecting models on the command line, consult the docs on
 
 ### Available commands
 
+Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [CLI](/docs/core/about-the-cli). Use the `dbt` prefix. For example, to run the `test` command, type `dbt test`.
+
+- [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
+- [clean](/reference/commands/clean): deletes artifacts present in the dbt project
+- [clone](/reference/commands/clone): clone selected models from specified state (requires dbt 1.6 or higher)
+- [compile](/reference/commands/compile): compiles (but does not run) the models in a project
+- [debug](/reference/commands/debug): debugs dbt connections and projects
+- [deps](/reference/commands/deps): downloads dependencies for a project
+- [docs](/reference/commands/cmd-docs) : generates documentation for a project
+- [list](/reference/commands/list): lists resources defined in a dbt project
+- [parse](/reference/commands/parse): parses a project and writes detailed timing info
+- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
+- [run](/reference/commands/run): runs the models in a project
+- [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
+- [seed](/reference/commands/seed): loads CSV files into the database
+- [show](/reference/commands/show): preview table rows post-transformation
+- [snapshot](/reference/commands/snapshot): executes "snapshot" jobs defined in a project
+- [source](/reference/commands/source): provides tools for working with source data (including validating that sources are "fresh")
+- [test](/reference/commands/test): executes tests defined in a project
+- [init](/reference/commands/init): initializes a new dbt project (CLI only)
+- [rpc](/reference/commands/rpc): runs an RPC server that clients can submit queries to (CLI only)
+
+<!-- commenting out the 2nd iteration July 2023
 Select the tabs that are relevant to the your development workflow. For example, if you develop in the dbt Cloud IDE, select **dbt Cloud**. 
 
 <Tabs>
@@ -62,7 +85,7 @@ Use the following dbt commands in the [CLI](/docs/core/about-the-cli) and use th
 </TabItem>
 
 </Tabs>
-
+-->
 <!-- leaving original list here below for reference (as of April 2023)
 
 - [build](build): build and test all selected resources (models, seeds, snapshots, tests)

From c52d5896ad6bf2c21e248a55d23c52f4d1e9e50f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 24 Jul 2023 15:45:31 +0100
Subject: [PATCH 255/703] remove old partner content

---
 .../sl-partner-integration-guide.md           | 637 ------------------
 1 file changed, 637 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 7e6dc81bac0..4068c005d2b 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -151,643 +151,6 @@ Allow users to query either one metric without dimensions or one or more metrics
 
 </VersionBlock>
 
-<VersionBlock lastVersion="1.5">
-
-import LegacyInfo from '/snippets/_legacy-sl-callout.md';
-
-<LegacyInfo/>
-
-
-This guide is for dbt Semantic Layer integration partners and explains integration guidelines, and connectivity.<br />
-
-To become a formal partner, integrate with the API, or have questions/feedback &mdash; **[contact us](mailto:semantic-layer@dbtlabs.com)** for more info.
-
-The dbt Semantic Layer allows users to dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics, models, and entities. It helps organizations manage complexities such as data, tools, and teams to make more efficient and trustworthy decisions.
-
-The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads. 
-
-To solve this, the dbt Semantic Layer provides a platform where users can confidently leverage their data from within their tools. dbt Cloud's change management capabilities ensure that any user modifications made to core business constructs, like metrics or entities, are distributed into all the tools connected to the data platform.
-
-The dbt Semantic Layer can be used for a variety of tools and applications of data. Here are some common use cases
-
-* Business intelligence (BI), reporting, and analytics,
-* Data quality and monitoring,
-* Governance and privacy,
-* Data discovery and cataloging,
-* Machine learning and data science.
-
-:::info Share your use case
-
-If you'd like to share other use cases for the dbt Semantic Layer, contact the [dbt Labs team](mailto:semantic-layer@dbtlabs.com).
-
-:::
-
-
-## Product overview
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[dbt Project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source in dbt Core |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt Core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja-like query models and metrics, use macros), compiles the query into pure SQL, executes the query in the data platform, and returns the data. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary in dbt Cloud |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, and metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary in dbt Cloud |
-    
-Review the following current architecture to understand how the components work together:
-  
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="Current product architecture" />
-
-
-## Integration guidelines
-
-In collaboration with dbt Labs, partners and users can build dbt Semantic Layer integrations that can import model metadata and metric definitions, query metrics, use macros, and more. 
-
-For more details, refer to the [Integration roadmap](#integration) and [Integration best practices](#best-practices) guidance.
-
-**Integration roadmap <a id="integration"></a>** 
-
-Integration partners generally build and approach their roadmap in the following stages:
-
-| Feature  |  Info | Availability |
-|----------|-------|:------------:|
-| **Model metadata**  |  Import/sync model metadata (descriptions, dimensions, test, freshness, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
-| **Metric definitions**  | Import/sync metric definitions (metric calculation, dimensions, description, and more) via the [dbt Cloud Discovery API](/docs/dbt-cloud-apis/discovery-api). | ✅ |
-| **dbt Semantic Layer as a data source**  | Connect to the dbt Semantic Layer as a data source (for example, the Snowflake Proxy Server). Users can execute dbt-SQL to query metrics or models and use macros.* | ✅ |
-| **Query metrics**  | Query the imported metrics via a metric-centric UI (for example, a user can select a metric, time grain, and dimensions of interest). | ✅ |
-| **Entity definitions**   | Import/sync entity definitions (descriptions, dimensions, data types, relationships, metrics, and more) and query entities via the dbt Semantic Layer. | _*Coming soon |
-| **dbt Semantic Layer Connector**   | A dedicated connector with the ability to query any data platform supported in dbt Cloud. (Will replace (3).) | _*Coming soon |
-
-_*The coming soon features are expected to launch in 2023. 
-
-**Integration best practices <a id="best-practices"></a>**
-
-To build a successful and seamless dbt Semantic Layer integration, it should express the following:
-
-- **Consistent**: Have a consistent user experience (UX) incorporated into existing core user workflows.
-- **Trustworthy**: Treat dbt assets (metrics, models, and entities) as first-class objects and indicate that their definitions and resulting datasets come from dbt Cloud.
-- **Efficient**: Provide a clear advantage over the current approach to setting up metrics and analyses, and finding dimensions/datasets in the tool.
-- **Accessible**: Include a self-serve component so a data consumer can ask questions via the user interface (UI), if applicable.
-
-
-## Use the Discovery API
-
-This section will explain how to connect to and query the [Discovery API](/docs/dbt-cloud-apis/discovery-api) for model and metric definitions. 
-
-To use the dbt Semantic Layer, you must meet the [prerequisites](/docs/use-dbt-semantic-layer/dbt-sl#prerequisites). 
-
-<details>
-  <summary><b>Discovery API authorization</b></summary>
-  <div>
-    <div>Refer to our <a href="/docs/dbt-cloud-apis/discovery-querying#authorization">Authorization documentation</a> to learn how to authorize requests to the Discovery API.<br></br><br></br>
-    
-    Metrics-specific queries work identical to existing Discovery API queries. This means existing integrations that query model metadata will work perfectly in the context of metrics.
-    </div>
-    </div>
-    </details>
-
-  <details>
-  <summary><b>Query the Discovery API</b></summary>
-  <div>
-    <div>Test out the Discovery API by using the <a href="https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql">GraphQL sandbox</a> and use this <a href="https://github.com/transform-data/dbt-metadata-client">Python client</a> as a starting point to develop.
-    </div>
-    </div>
-    </details>
-    <br></br>
-
-### Query models for a project
-
-You can query model definitions or details about a specific model for a project from a given job.
-
- 
-<!--- tabs for listing models and fetching details about specific model --->
-<Tabs>
-
-<TabItem value="list" label="Listing models for a job">
-
-This is an example of querying all models that utilize the schema`analytics` from a given job.
-
-```
-{  
- models(jobId: 181329, schema: "analytics") {
-   name
-   status
-   compileCompletedAt
-   database
-   dbtVersion
-   runGeneratedAt
-   }
-}
-```
-</TabItem>
-
-<TabItem value="exampleresponse" label="Example response with models">
-
-```
-{
-   "data": {
-       "models": [
-           {
-               "name": "customers",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:24.186Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           },
-           {
-               "name": "stg_customers",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           },
-           {
-               "name": "stg_orders",
-               "status": "success",
-               "compileCompletedAt": "2022-12-15T06:37:22.509Z",
-               "database": "analytics",
-               "dbtVersion": "1.3.1",
-               "runGeneratedAt": "2022-12-15T06:37:25.187Z"
-           }
-       ]
-   }
-}
-```
-</TabItem>
-
-<TabItem value="details" label="Query details about a specific model">
-
-This is an example of querying details about a specific model, `model.jaffle_shop.customers`, from a given job.
-
-```
-{
- model(jobId: 181329, uniqueId: "model.jaffle_shop.customers") {
-   parentsModels {
-     runId
-     uniqueId
-     executionTime
-   }
- }
-}
-{
-   "data": {
-       "model": {
-           "parentsModels": [
-               {
-                   "runId": 105297555,
-                   "uniqueId": "model.jaffle_shop.stg_customers",
-                   "executionTime": 1.676571846008301
-               },
-               {
-                   "runId": 105297555,
-                   "uniqueId": "model.jaffle_shop.stg_orders",
-                   "executionTime": 1.631831407546997
-               }
-           ]
-       }
-   }
-}
-```
-
-</TabItem>
-</Tabs>
-
-<!--- End tabs for listing models and fetching details about specific model --->
-
-### Query metrics for a project
-
-Query metrics definitions or details for a project from a given job and refer to the following resources: 
-
-- [Metrics query](/docs/dbt-cloud-apis/discovery-schema-metrics) &mdash; Information on how to query the full list of metrics defined in a user’s project with the dbt Cloud Discovery API. 
-- [dbt Metrics docs](https://docs.getdbt.com/docs/build/metrics#available-properties) &mdash; Information on the available metric properties. 
-- [GraphQL sandbox](https://studio.apollographql.com/sandbox/explorer?endpoint=https%3A%2F%2Fmetadata.cloud.getdbt.com%2Fgraphql) &mdash; Access to test the dbt Cloud Discovery API testing environment.
-
-<!--- tabs for listing, fetching, example, and querying metrics --->
-<Tabs>
-
-<TabItem value="list" label="Listing metrics">
-
-This is an example listing metrics from a given job:
-
-```
-{
-  metrics(jobId: 123) {
-    name
-    label
-    description
-    model
-    dependsOn
-    calculation_method
-    expression
-    timestamp
-    timeGrains
-    dimensions
-    window
-    filters
-    tags
-    meta
-  }
-}
-```
-</TabItem>
-
-<TabItem value="query" label="Query details about a specific metric">
-
-The `metric` query supports all metric properties listed in **Listing metrics**. 
-The following abbreviated example is querying details about the metric `new_customers` from job `123`:
-
-This is an example of querying details about a specific metric `new_customers` from a given job `123`.
-
-```
-{
-  metric(jobId: 123) {
-    label
-    calculation_method
-    timestamp
-    timeGrains
-    dimensions
-  }
-}
-```
-
-</TabItem>
-
-<TabItem value="exampleapi" label="Example Discovery API response with metrics">
-
-```
-{
-  "data": {
-    "metrics": [
-      {
-        "uniqueId": "metric.claim_to_fame.total_claim_charges",
-        "name": "total_claim_charges",
-        "tags": [],
-        "label": "Total Claim Charges",
-        "calculation_method": "sum",
-        "expression": "total_charge_amount",
-        "timestamp": "created_at",
-        "timeGrains":[
-          "day",
-          "week",
-          "month"
-        ],
-        "meta": {},  
-        "resourceType": "metric",
-        "model": {
-          "name": "fct_billed_patient_claims"
-        }
-       },
-      {
-        "uniqueId": "metric.claim_to_fame.total_billed_diagnoses",
-        "name": "total_billed_diagnoses",
-        "tags": [],
-        "label": "Total Billed Diagnoses",
-        "calculation_method": "count_distinct",
-        "expression": "diagnosis_id",
-        "timestamp": "created_at",
-        "timeGrains":[
-          "week",
-          "month",
-          "year"
-        ],
-        "meta": {},  
-        "resourceType": "metric",
-        "model": {
-          "name": "fct_billed_patient_claims"
-        },
-      }
-     ]
-  }
-}
-```
-
-</TabItem>
-
-<TabItem value="example" label="Example metric definition">
-
-```
-metrics:
-  - name: total_claim_charges
-    label: Total Claim Charges
-    model: ref('fct_billed_patient_claims')
-    calculation_method: sum
-    expression: total_charge_amount
-    timestamp: created_at
-    time_grains: [day, week, month, all_time]
-
-
-  - name: total_billed_diagnoses
-    label: Total Billed Diagnoses
-    model: ref('fct_billed_patient_claims')
-    calculation_method: count_distinct
-    expression: diagnosis_id
-    timestamp: created_at
-    time_grains: [day, week, month]
-```
-
-</TabItem>
-
-</Tabs>
-
-<!--- End tabs for listing, fetching, example, and querying metrics --->
-
-
-## Query the dbt Semantic Layer
-
-This section explains how to connect to or query the dbt Semantic Layer Proxy Server to return model data, metric data, and so on. 
-
-When you configure the dbt Semantic Layer, dbt Cloud provides a Proxy Server endpoint that users can connect to as though it's a Snowflake-hosted endpoint. Once the queries are submitted, dbt Cloud will:
-
-1. Compile dbt-sql queries into valid Snowflake SQL,
-2. Execute the compiled SQL against the Snowflake data platform,
-3. Return the results to the client.
-
-Replace the hostname in your existing data platform connection with the relevant dbt Cloud Proxy Server URL (for example, `abc123.proxy.cloud.getdbt.com`). All queries you submit through the endpoint will be compiled en route to the data platform.* 
-
-*_Note: This approach will change with the new Semantic Layer connection in mid-2023, which will be able to query all data platforms supported in dbt Cloud through dedicated JDBC/ODBC drivers, and eventually an API._
-
-
-<!--- tabs for running models and running metrics --->
-<Tabs>
-
-<TabItem value="metrics" label="Metric queries">
-
-Users can compile and execute metric queries using macros defined in the [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics).  This package:
-
-- Generates the SQL required to accurately calculate the metric definition,
-- Supplies helper macros for derived calculations (like month over month, year to date, and so on) time series operations
-
-
-```
-select * 
-from {{ metrics.calculate(
-    metric_list=[metric('customers'), metric(‘revenue’)],
-    grain='week',
-    dimensions=['plan', 'country'],
-    secondary_calculations=[
-        metrics.period_to_date(aggregate="sum", period="year"),
-        metrics.rolling(aggregate="average", interval=4, alias="avg_past_4wks")
-    ],
-    start_date='2020-01-01',
-    end_date="date_trunc('day', getdate())"
-) }}
-```
-
-</TabItem>
-
-<TabItem value="models" label="Model queries">
-
-Model queries allow users to query models and use macros from their dbt project.
-
-```
-select cents_to_dollars('amount_cents') as amount_dollars 
-from {{ ref('orders') }}
-```
-</TabItem>
-</Tabs>
-
-### Entities 
-
-dbt Labs will introduce a new node type, **[entity](https://github.com/dbt-labs/dbt-core/issues/6379)**, when dbt Core version 1.5 launches. It introduces a new and efficient way to define metrics by reusing logic (for example, `time_grains`).  
-
-Entities are semantic objects made up of curated dimensions from models with more metadata defined. Over time, users can standardize metric and entity definitions with packages to speed up development. 
-
-For integrations, entities will provide information like:
-
-- a way to organize metrics based on the entity they reference, and 
-- a new consumable and dynamically generated dataset (versus finding a table in the data platform). 
-
-This information will be available alongside the Discovery API, and entities can be directly queried through the dbt Semantic Layer. 
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/entity-lineage.jpg" title="Entity lineage graph" />
-
-:::caution 🚧
-
-Entities are a work in progress &mdash; expect continuous changes and improvements. To stay up-to-date, refer to the [entity discussions](https://github.com/dbt-labs/dbt-core/issues/6379) page. 
-
-:::
-
-<!--- tabs for running entity def, entity metadata response, metric def, metric metadata response, entity queries --->
-
-<Tabs>
-
-<TabItem value="entitydef" label="Entity definition">
-
-Define entities in your dbt project.
-
-```
-entities: ## The top-level path of the new node
-  - name: [Required] ## The name of the entity
-    model: [Required] ## The name of the model that the entity is dependent on
-    description: [Optional] ## The description of the entity
-    
-    dimensions: [Optional] ## The list of dimensions & properties associated with the entity. 
-      - include: [Optional] *
-      - exclude: [Optional]
-      - name: [Required] ## The name of the dimension 
-		column_name: [Optional] ## The name of the column in the model if not 1:1. Serves as mapping
-		data_type: [Optional] ## The data type of the dimension
-		description: [Optional] ## Description of the dimension
-		default_timestamp: [Optional] ## Setting datetime dimension as default for metrics
-		time_grains: [Optional] ## Acceptable time grains for the datetime dimension
-		primary_key: [Optional] ## Whether this dimension is part of the primary key
-```
-</TabItem>
-
-<TabItem value="entityapi" label="Entity Discovery API response">
-
-Query entities via the Discovery API.
-
-```
-"entity.project_name.entity_name": {
-      "unique_id": "entity.project_name.entity_name",
-      "package_name": "project_name",
-      "original_file_path": "models/metric_definitions/ratio_metric.yml",
-      "name": "entity_name",
-      "model": "ref('model_name')",
-      "description": "some description",
-      "dimensions": {
-				"dimension_name": {
-            "name": "dimension_name",
-						"column_name": "column_name",
-						"default_timestamp": "true",
-						"time_grains": "[day, week, month, year]"
-            "primary_key": true,
-            "data_type": null,
-            "description": "TBD",
-            "meta": {},
-						}
-      },
-      "resource_type": "entity",
-      "meta": {},
-      "tags": [],
-      "config": {
-          "enabled": true,
-      },
-      "depends_on": {
-          "macros": [],
-          "nodes": [
-              "model.project_name.model_name",
-          ]
-      },
-			"docs": {
-        "show": true,
-        "node_color": null
-      },
-      "refs": [
-					[
-						"model_name",
-					]
-			],
-      "created_at": 1669653016.522599
-  },
-  ```
-</TabItem>
-
-<TabItem value="newmetricdef" label="New metric definition">
-
-How to define new [metrics](/docs/build/metrics) in your dbt project. The metric definition and metadata response will change accordingly once entities are introduced, notably with metrics referencing entities instead of models and inheriting entity dimensions. 
-
-    ```
-    metrics: 
-		## Always required
-	     - name: [Required] ## The name of the metric
-		label: [Required] ## The human-readable name of the metric
-		calculation_method: [Required] ## The calculation/aggregation used for the metric
-		expression: [Required] ## The SQL expression being aggregated/calculated
-		entity: [Required] ## The entity being used as the source of the metric
-
-		## Always optional
-		description: [Optional] ## Any description about the metric
-		timestamp: [Optional] ## The name of the timestamp field to use
-		time_grains: [Optional] ## The list of time grains that are permitted
-		filters: [Optional] ## The filters of the metric
-		window: [Optional] ## The ability to make a metric cumulative over a time period
-		config: [Optional] ## Additional information for configuring the output
-		
-		## Either or dimensions:
-			include: [Optional] ## The list of dimensions to be included. Either * or list
-			exclude: [Optional] ## The list of dimensions to be excluded from the inherited list
-    ```
-    
-</TabItem>      
-
-<TabItem value="newmetricapi" label="New metric Discovery API response">
-
-```
-"metric.project_name.metric_name": {
-      "fqn": [
-          "project_name",
-          "folder_name",
-          "metric_name"
-      ],
-      "unique_id": "metric.project_name.metric_name",
-      "package_name": "project_name",
-      "root_path": "file_path",
-      "path": "file_path",
-      "original_file_path": "file_path",
-      "name": "metric_name",
-      "description": "description",
-			"entity": "entity_name",
-      "label": "Human readable version",
-      "calculation_method": "the calc method",
-      "timestamp": "the timestamp field",
-      "time_grains": [
-          "day",
-          "week"
-      ],
-      "expression": "a field name or sql expression",
-      "dimensions": [
-				{
-				"entity_name": [
-          "had_discount",
-          "order_country"
-					]
-				}
-      ],
-      "window": null,
-      "resource_type": "metric",
-      "filters": [],
-      "meta": {},
-      "tags": [],
-      "config": {
-          "enabled": true
-      },
-      "unrendered_config": {},
-      "sources": [],
-      "depends_on": {
-          "macros": [],
-          "nodes": [
-              "entity.projet_name.entity_name",
-          ]
-      },
-      "entities": [
-				[
-					"entity_name"
-				]
-			],
-      "metrics": ["used for derived metrics"],
-      "created_at": 1669653027.290001
-  },
-  ```
-</TabItem>
-
-<TabItem value="entityquery" label="Entity queries">
-
-Query an entity using dbt-SQL. Eventually, users will be able to query entities and dynamically generate datasets using a macro (like with metrics), without having to find specific tables or columns. 
-
-```
-select *
-from {{ entities.calculate(
-	entity_list=[...], [Required, one to start]
-       dimensions: [...], [Optional, default is all]
-       metrics: [...], [Optional, default is all at finest grain]
-       filters: ...
-	)}}
-  ```
-</TabItem>
-</Tabs>
-
-### dbt Semantic Layer Connector 
-
-In order to support more data platforms and enhance the user experience, users will be able to connect to a [dbt Cloud-supported data platform](/docs/cloud/connect-data-platform/about-connections) with the dbt Semantic Layer.
-
-Integration partners need to install the [Arrow FlightSQL](https://arrow.apache.org/docs/format/FlightSql.html) JDBC/ODBC driver, which will authenticate with dbt Cloud and the data platform that it queries. 
-
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/connection-architecture.jpg" title="Envisioned connection architecture" />
-
-
-### dbt Semantic Layer API 
-
-dbt Cloud will provide a web API that supports:
-
-- Compiling dbt-SQL queries to return their compiled SQL.
-- Executing dbt-SQL queries and returning the queried results from the data platform.
-
-The API will be a viable integration point with the dbt Semantic Layer. It will be authorized by a [dbt Cloud service token](/docs/dbt-cloud-apis/service-tokens) and eventually support the invocation of dbt commands (e.g., `dbt run`, `dbt test`, etc.) in the future.
-
-
-## Contact us
-
-### For dbt Semantic Layer support
-
-For partner and customer support, please email the [Support team](mailto:support@getdbt.com). Please ensure the message includes:
-
-- "Semantic Layer"
-- The name of the partner software
-- The dbt Cloud account ID of the customer, if you are a partner making the inquiry
-
-### For product and partnerships
-
-If you'd like to become a formal partner, have product feedback/questions, or are interested in integrating, email the [Product and Partnership team](mailto:semantic-layer@dbtlabs.com). 
-
-<!-- rewrite a little and would like to see if i can include a button -->
-
-</VersionBlock>
-
 ## Related docs
 
 - [Use the dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn about the product.

From 0a9864fab23701e4ef543820371521610dcc60be Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Mon, 24 Jul 2023 14:55:40 -0500
Subject: [PATCH 256/703] Add Redshift Serverless, remove public preview, add
 field to Snowflake config

---
 .../docs/cloud/secure/about-privatelink.md    |   3 ---
 .../docs/cloud/secure/redshift-privatelink.md |  18 +++++++++++++++---
 .../cloud/secure/snowflake-privatelink.md     |   2 ++
 .../img/docs/dbt-cloud/redshiftserverless.png | Bin 0 -> 139544 bytes
 4 files changed, 17 insertions(+), 6 deletions(-)
 create mode 100644 website/static/img/docs/dbt-cloud/redshiftserverless.png

diff --git a/website/docs/docs/cloud/secure/about-privatelink.md b/website/docs/docs/cloud/secure/about-privatelink.md
index 7bd18f306b6..77ee8a6af7a 100644
--- a/website/docs/docs/cloud/secure/about-privatelink.md
+++ b/website/docs/docs/cloud/secure/about-privatelink.md
@@ -4,9 +4,6 @@ id: about-privatelink
 description: "Configuring PrivateLink for AWS"
 sidebar_label: "About PrivateLink"
 ---
-:::info 
-This feature is currently in Private Preview, and these instructions are specific to dbt Cloud multi-tenant Enterprise tier environments hosted on AWS. 
-:::
 
 PrivateLink enables a private connection from any dbt Cloud Multi-Tenant environment to your data platform hosted on AWS using [AWS PrivateLink](https://aws.amazon.com/privatelink/) technology. PrivateLink allows dbt Cloud customers to meet security and compliance controls as it allows connectivity between dbt Cloud and your data platform without traversing the public internet. This feature is supported in most regions across NA, Europe, and Asia, but [contact us](https://www.getdbt.com/contact/) if you have questions about availability. 
 
diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index b8c357825f8..35cea7af06e 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -11,6 +11,10 @@ AWS provides two different ways to create a PrivateLink VPC endpoint for a Redsh
 
 dbt Cloud supports both types of endpoints, but there are a number of [considerations](https://docs.aws.amazon.com/redshift/latest/mgmt/managing-cluster-cross-vpc.html#managing-cluster-cross-vpc-considerations) to take into account when deciding which endpoint type to use. Redshift-managed provides a far simpler setup with no additional cost, which might make it the preferred option for many, but may not be an option in all environments. Based on these criteria, you will need to determine which is the right type for your system. Follow the instructions from the section below that corresponds to your chosen endpoint type.
 
+:::info Redshift Serverless
+While Redshift Serverless does support Redshift-managed type VPC endpoints, this functionality is not currently available across AWS accounts. Due to this limitation, an Interface-type VPC endpoint service must be used for Redshift Serverless cluster PrivateLink connectivity from dbt Cloud. 
+:::
+
 ## Configuring Redshift-managed PrivateLink
 
 1. On the running Redshift cluster, select the **Properties** tab.
@@ -51,11 +55,19 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
     
     - Target Type: **IP**
 
-        Notes:
-        - Use IP addresses from the Redshift cluster’s **Network Interfaces**, _not_ IPs listed in the **Node IP addresses** section as those can change.
+        **Notes:**
+
+        _Standard Redshift_
+        - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
         <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
 
-        - There is likely only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will be created for that AZ as well. The NI IP from the original AZ should still work, but the new NI IP can also be added to the Target Group if desired. If adding additional IPs, note that the NLB will need to add the corresponding AZ as well. Once created, the NI(s) shouldn't change (NOTE: this is our observation from testing, but is not officially documented by AWS).
+        - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will be created for that AZ as well. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group if desired. If adding additional IPs, note that the NLB will need to add the corresponding AZ as well. Once created, the NI(s) shouldn't change (NOTE: this is our observation from testing, but is not officially documented by AWS).
+
+        _Redshift Serverless_
+        - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
+        <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>
+
+        - From a command line run the command `nslookup <endpoint>` using the endpoint found in the previous step and use the associated IP(s) for the Target Group
 
     - Target Group protocol: **TCP** 
 
diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md
index 16138e7e86d..bd82d38ff12 100644
--- a/website/docs/docs/cloud/secure/snowflake-privatelink.md
+++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md
@@ -25,8 +25,10 @@ The following steps will walk you through the setup of a Snowflake AWS PrivateLi
 Subject: New Multi-Tenant PrivateLink Request
 - Type: Snowflake
 - SYSTEM$GET_PRIVATELINK_CONFIG output:
+- *Use privatelink-account-url or regionless-privatelink-account-url?: 
 - dbt Cloud multi-tenant environment (US, EMEA, AU):
 ```
+_*By default dbt Cloud will be configured to use `privatelink-account-url` from the provided `SYSTEM$GET_PRIVATELINK_CONFIG` as the PrivateLink endpoint. At the request of the customer, `regionless-privatelink-account-url` can be used instead._
 
 dbt Labs will work on your behalf to complete the PrivateLink setup. Please allow 1-2 business days for this process to complete. Support will contact you when the endpoint is available. 
 
diff --git a/website/static/img/docs/dbt-cloud/redshiftserverless.png b/website/static/img/docs/dbt-cloud/redshiftserverless.png
new file mode 100644
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From cb3bf8713edf64a56f053a742fd6b728b4f3f43e Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Mon, 24 Jul 2023 15:18:02 -0500
Subject: [PATCH 257/703] Small wording and format updates for PL changes

---
 website/docs/docs/cloud/secure/redshift-privatelink.md  | 2 +-
 website/docs/docs/cloud/secure/snowflake-privatelink.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 35cea7af06e..4151cb6f4dd 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -11,7 +11,7 @@ AWS provides two different ways to create a PrivateLink VPC endpoint for a Redsh
 
 dbt Cloud supports both types of endpoints, but there are a number of [considerations](https://docs.aws.amazon.com/redshift/latest/mgmt/managing-cluster-cross-vpc.html#managing-cluster-cross-vpc-considerations) to take into account when deciding which endpoint type to use. Redshift-managed provides a far simpler setup with no additional cost, which might make it the preferred option for many, but may not be an option in all environments. Based on these criteria, you will need to determine which is the right type for your system. Follow the instructions from the section below that corresponds to your chosen endpoint type.
 
-:::info Redshift Serverless
+:::note Redshift Serverless
 While Redshift Serverless does support Redshift-managed type VPC endpoints, this functionality is not currently available across AWS accounts. Due to this limitation, an Interface-type VPC endpoint service must be used for Redshift Serverless cluster PrivateLink connectivity from dbt Cloud. 
 :::
 
diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md
index bd82d38ff12..0cbd6e96aad 100644
--- a/website/docs/docs/cloud/secure/snowflake-privatelink.md
+++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md
@@ -28,7 +28,7 @@ Subject: New Multi-Tenant PrivateLink Request
 - *Use privatelink-account-url or regionless-privatelink-account-url?: 
 - dbt Cloud multi-tenant environment (US, EMEA, AU):
 ```
-_*By default dbt Cloud will be configured to use `privatelink-account-url` from the provided `SYSTEM$GET_PRIVATELINK_CONFIG` as the PrivateLink endpoint. At the request of the customer, `regionless-privatelink-account-url` can be used instead._
+_*By default dbt Cloud will be configured to use `privatelink-account-url` from the provided [SYSTEM$GET_PRIVATELINK_CONFIG](https://docs.snowflake.com/en/sql-reference/functions/system_get_privatelink_config.html) as the PrivateLink endpoint. Upon request, `regionless-privatelink-account-url` can be used instead._
 
 dbt Labs will work on your behalf to complete the PrivateLink setup. Please allow 1-2 business days for this process to complete. Support will contact you when the endpoint is available. 
 

From 36d83954f544482a13e588b7978b4add67e1439f Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Mon, 24 Jul 2023 15:34:39 -0500
Subject: [PATCH 258/703] fix indenting

---
 website/docs/docs/cloud/secure/redshift-privatelink.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 4151cb6f4dd..10d26865409 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -63,6 +63,7 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
 
         - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will be created for that AZ as well. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group if desired. If adding additional IPs, note that the NLB will need to add the corresponding AZ as well. Once created, the NI(s) shouldn't change (NOTE: this is our observation from testing, but is not officially documented by AWS).
 
+
         _Redshift Serverless_
         - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
         <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>

From 0adb669b3705390be59f42437e348975bdbada18 Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Mon, 24 Jul 2023 15:42:25 -0500
Subject: [PATCH 259/703] another indenting fix

---
 website/docs/docs/cloud/secure/redshift-privatelink.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 10d26865409..62fc893156a 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -58,13 +58,14 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
         **Notes:**
 
         _Standard Redshift_
+        
         - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
         <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
 
         - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will be created for that AZ as well. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group if desired. If adding additional IPs, note that the NLB will need to add the corresponding AZ as well. Once created, the NI(s) shouldn't change (NOTE: this is our observation from testing, but is not officially documented by AWS).
 
-
         _Redshift Serverless_
+
         - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
         <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>
 

From 435d3e520c1bc8ba40fc374cede2293771e72b9a Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Mon, 24 Jul 2023 16:03:52 -0500
Subject: [PATCH 260/703] remove unnecessary line

---
 website/docs/docs/cloud/secure/redshift-privatelink.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 62fc893156a..f1c526a7963 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -55,10 +55,8 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
     
     - Target Type: **IP**
 
-        **Notes:**
-
         _Standard Redshift_
-        
+
         - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
         <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
 

From 70eb48598a5febb1d5876033153b2abdd4bfe49e Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 24 Jul 2023 18:36:56 -0400
Subject: [PATCH 261/703] Update
 website/docs/docs/cloud/secure/redshift-privatelink.md

---
 website/docs/docs/cloud/secure/redshift-privatelink.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index f1c526a7963..6fd5e77f21f 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -60,7 +60,7 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
         - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
         <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
 
-        - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will be created for that AZ as well. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group if desired. If adding additional IPs, note that the NLB will need to add the corresponding AZ as well. Once created, the NI(s) shouldn't change (NOTE: this is our observation from testing, but is not officially documented by AWS).
+        - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will also be created for that AZ. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group. If adding additional IPs, note that the NLB will also need to add the corresponding AZ. Once created, the NI(s) should stay the same (This is our observation from testing, but AWS does not officially document it).
 
         _Redshift Serverless_
 

From 3f53ab57fe37f10b81af216a0c44dc25e5b2eb40 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 24 Jul 2023 18:37:02 -0400
Subject: [PATCH 262/703] Update
 website/docs/docs/cloud/secure/redshift-privatelink.md

---
 website/docs/docs/cloud/secure/redshift-privatelink.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 6fd5e77f21f..56d5dd13a36 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -67,7 +67,7 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
         - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
         <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>
 
-        - From a command line run the command `nslookup <endpoint>` using the endpoint found in the previous step and use the associated IP(s) for the Target Group
+        - From a command line run the command `nslookup <endpoint>` using the endpoint found in the previous step and use the associated IP(s) for the Target Group.
 
     - Target Group protocol: **TCP** 
 

From 677b9023017b488ccc985a0b21bac2898aa71696 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 24 Jul 2023 15:54:39 -0700
Subject: [PATCH 263/703] Update sl-jdbc.md

Adding some more color on `metric_time`
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index f050c7bd635..43c1ded7bf5 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -141,6 +141,16 @@ To query metric values, here are the following parameters that are available:
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
 | `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |
 
+
+## Note on time dimensions and `metric_time`
+
+You will notice that in the list of dimensions for all metrics, there is a dimension called `metric_time`'. `Metric_time` is a reserved keyword for the measure-specific aggregation time dimensions. For any time-series metric, the `metric_time` keyword should always be available for use in queries. This is a common dimension across *all* metrics in a semantic graph. 
+
+You can look at a single metric or hundreds of metrics and if you group by `metric_time`, it will always give you the correct time series.
+
+Additionally, when performing granularity calculations, we recommend you always operate on `metric_time` and you will get the correct answer. Note `metric_time` should be available in addition to any other time dimensions that are available for the metric(s). In the case where you are looking at one metric (or multiple metrics from the same data source), the values in the series for the primary time dimension and `metric_time` are equivalent.
+
+
 ## Examples
 
 Use the following examples to help you get started with the JDBC API

From 89f872c21c25fe22ba2b092fa660ec478abd2891 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 24 Jul 2023 16:08:31 -0700
Subject: [PATCH 264/703] Update sl-jdbc.md

a few more edits here
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 43c1ded7bf5..88bc2d9818c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -146,9 +146,11 @@ To query metric values, here are the following parameters that are available:
 
 You will notice that in the list of dimensions for all metrics, there is a dimension called `metric_time`'. `Metric_time` is a reserved keyword for the measure-specific aggregation time dimensions. For any time-series metric, the `metric_time` keyword should always be available for use in queries. This is a common dimension across *all* metrics in a semantic graph. 
 
-You can look at a single metric or hundreds of metrics and if you group by `metric_time`, it will always give you the correct time series.
+You can look at a single metric or hundreds of metrics, and if you group by `metric_time`, it will always give you the correct time series.
 
-Additionally, when performing granularity calculations, we recommend you always operate on `metric_time` and you will get the correct answer. Note `metric_time` should be available in addition to any other time dimensions that are available for the metric(s). In the case where you are looking at one metric (or multiple metrics from the same data source), the values in the series for the primary time dimension and `metric_time` are equivalent.
+Additionally, when performing granularity calculations that are global (not specific to a particular time dimension), we recommend you always operate on `metric_time` and you will get the correct answer. 
+
+Note that `metric_time` should be available in addition to any other time dimensions that are available for the metric(s). In the case where you are looking at one metric (or multiple metrics from the same data source), the values in the series for the primary time dimension and `metric_time` are equivalent.
 
 
 ## Examples

From 0363e20d206b09082719fe2f6cf32700b8ba26fa Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Mon, 24 Jul 2023 16:22:04 -0700
Subject: [PATCH 265/703] CI updates phase 2

---
 website/docs/docs/deploy/job-settings.md | 45 ++++++++++++++++
 website/docs/docs/deploy/job-triggers.md | 68 ------------------------
 2 files changed, 45 insertions(+), 68 deletions(-)
 delete mode 100644 website/docs/docs/deploy/job-triggers.md

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 3b53880bddf..1f93e103873 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -56,3 +56,48 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 <Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
 
 7. Select **Save**, then click **Run Now** to run your job. Click the run and watch its progress under **Run history**.
+
+### Schedule days
+
+You can schedule which days of the week and specify customized hours or intervals to run your deploy job with the option **Exact intervals** or **Intervals**. 
+
+- **Exact Intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
+
+- **Intervals** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to **Every 2 hours**, the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
+
+
+:::info
+
+dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
+
+- 0 means 12am (midnight) UTC
+- 12 means 12pm (afternoon) UTC
+- 23 means 11pm UTC
+
+:::
+
+### Custom cron schedule
+
+You can fully customize the scheduling of your job with the **Enter custom cron schedule** option. Using cron syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
+
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
+
+Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
+
+Some examples are:
+
+- `0 * * * *`: Every hour, at minute 0
+- `*/5 * * * *`: Every 5 minutes
+- `5 4 * * *`: At exactly 4:05 AM UTC
+- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
+- `0 0 */2 * *`: At midnight UTC every other day
+- `0 0 * * 1`: At midnight UTC every Monday.
+
+
+## Related docs
+
+- [Artifacts](/docs/deploy/artifacts)
+- [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
+- [Source freshness](/docs/deploy/source-freshness)
+- [Job commands](/docs/deploy/job-commands)
+- [Webhooks for your jobs](/docs/deploy/webhooks)
\ No newline at end of file
diff --git a/website/docs/docs/deploy/job-triggers.md b/website/docs/docs/deploy/job-triggers.md
deleted file mode 100644
index cb7a1a48088..00000000000
--- a/website/docs/docs/deploy/job-triggers.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-title: "Job triggers"
-id: "job-triggers"
-description: "You can configure when and how dbt should run your job"
----
-
-In dbt Cloud, you can use the options under **Triggers** to configure when and how dbt should [run your job](/docs/deploy/job-triggers#schedule-job):
-
-- **Schedule** tab &mdash; Use the **Run on schedule** toggle to configure your job to run on either [scheduled days](#schedule-days) or [custom cron-powered schedule](#custom-cron-schedule)
-- **Continuous Integration (CI)** tab &mdash; Configure [continuous integration](/docs/deploy/continuous-integration) to run when someone opens a new pull request in your dbt repository
-- **API** tab &mdash; Use the [API](/docs/dbt-cloud-apis/admin-cloud-api) to trigger a job or send events to other systems
-
-<Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
-
-## Schedule jobs
-
-To schedule your job to run at specific days, times, and intervals:
-1. Go to the specific job settings, click **Edit**, then go to the **Triggers** section
-2. Go to the **Schedule** tab, and toggle **Run on schedule**
-3. Use either the [scheduled days](#schedule-days) or the [custom cron-powered schedule](#custom-cron-schedule) method to customize your desired days, times, and intervals.
-
-### Schedule days
-
-To set your job's schedule, use the **Schedule Days** option to choose specific days of the week, and select customized hours or intervals.
-
-Under **Timing**, you can either use customizable hours for jobs that need to run frequently throughout the day or exact intervals for jobs that need to run at specific times:
-
-- **Every n hours** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to "every 2 hours", the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
-
-- **At exact intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
-
-:::info
-
-dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
-
-- 0 means 12am (midnight) UTC
-- 12 means 12pm (afternoon) UTC
-- 23 means 11pm UTC
-
-:::
-
-### Custom cron schedule
-
-To fully customize the scheduling of your job, choose the **Custom cron schedule** option and use the "cron" syntax. With this syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
-
-
-<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
-
-Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
-
-Refer to the following example snippets:
-
-
-- `0 * * * *`: Every hour, at minute 0
-- `*/5 * * * *`: Every 5 minutes
-- `5 4 * * *`: At exactly 4:05 AM UTC
-- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
-- `0 0 */2 * *`: At midnight UTC every other day
-- `0 0 * * 1`: At midnight UTC every Monday.
-
-
-## Related docs
-
-- [Artifacts](/docs/deploy/artifacts)
-- [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
-- [Source freshness](/docs/deploy/source-freshness)
-- [Job commands](/docs/deploy/job-commands)
-- [Webhooks for your jobs](/docs/deploy/webhooks)
\ No newline at end of file

From c89cb7e4a5f105378ad1ad8e60ca8e42eafd8582 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 24 Jul 2023 16:24:48 -0700
Subject: [PATCH 266/703] Update sl-partner-integration-guide.md

a few updates to metric time in the sl integration guide
---
 .../sl-partner-integration-guide.md           | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 4068c005d2b..968afc1947c 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -35,13 +35,12 @@ The dbt Semantic Layer authenticates with:
 - `SERVICE_TOKEN`, 
 - `host`
 
-This applies to the dbt Semantic Layer APIs, which all currently use different host names.
+This applies to the dbt Semantic Layer APIs, which all currently use different host names. 
 
 We recommend you provide users with separate input fields with these components (which dbt Cloud provides). 
 
 For [JDBC](/docs/dbt-cloud-apis/sl-jdbc), you can construct the JDBC URL from these inputs. Or, you could request the full URL string.
 
-If you use both Semantic Layer APIs, users will need to provide different host information for each one. In the future, we want to unify these host names.
 
 ## Best practices on exposing metrics:
 
@@ -60,17 +59,19 @@ When working with more governed data, it's essential to establish clear guardrai
 **Aggregations control** <br />
 Users shouldn't generally be allowed to modify aggregations unless they are performing post-processing calculations on data from the Semantic Layer (such as year over year analysis).
 
-**Traceability of metric and dimension changes** <br />
-When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
-
-**Time series alignment**<br />
+**Time series alignment and using metric_time**<br />
 Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes. 
   
-Implementing guardrails in the application might help achieve this.
+When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). As such, when building an application, we recommend exposing `metric_time` as a special time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the rest is clarifying as a way to separate out the primary one. 
+
+Also, when a user requests a time granularity change for the main time series, the query should use `metric_time`. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
 
 **Units consistency**<br />
 If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
 
+**Traceability of metric and dimension changes** <br />
+When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
+
 ### Discoverability 
 
 1. Consider treating [metrics](/docs/build/metrics-overview) as first-class objects rather than measures. Metrics offer a higher-level and more contextual way to interact with data, reducing the burden on end-users to manually aggregate data.
@@ -115,14 +116,15 @@ For better analysis, it's best to have the context of the metrics close to where
 
 ### Query flexibility
 
-Allow users to query either one metric without dimensions or one or more metrics with dimensions
+Allow users to query either one metric alone without dimensions or multiple metrics with dimensions
 
 - Allow toggling between metrics / dimensions seamlessly
 - Be clear on exposing what dimensions are queryable with what metrics and hide things that don’t apply, and vice versa.
 - Include Presets for filtering date ranges and calendar for filtering time dimensions
   * For example, last 30 days, last week etc.
 - Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
-  * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity" isn't available to the user.  
+  * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity" isn't available to the user.
+- We recommend that granularity is a dimension-specific concept and can be applied to more than just the primary aggregation or `metric_time`. Initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as the solution evolves.
 
 ### Example stages of an integration
 

From 0341965d67a7b14a0fde2af75cb5a4a58399c66f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Mon, 24 Jul 2023 16:28:11 -0700
Subject: [PATCH 267/703] Update sidebar

---
 website/sidebars.js | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/sidebars.js b/website/sidebars.js
index c09e7b784c4..e52263c5e65 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -337,7 +337,6 @@ const sidebarSettings = {
           items: [
             "docs/deploy/job-settings",
             "docs/deploy/job-commands",
-            "docs/deploy/job-triggers",
           ],
         },
         {

From 0f0bf9a65f6e6068a2709e54064ddd49e0ef7eeb Mon Sep 17 00:00:00 2001
From: Sterling Paramore <gnilrets@gmail.com>
Date: Mon, 24 Jul 2023 16:50:31 -0700
Subject: [PATCH 268/703] Draft of modeling ragged time-varying hierarchies
 (#3676)

## What are you changing in this pull request and why?
Resolves #3328


## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

---------

Co-authored-by: Joel Labes <joel.labes@dbtlabs.com>
---
 ...odeling-ragged-time-varying-hierarchies.md | 448 ++++++++++++++++++
 website/blog/authors.yml                      |  13 +-
 .../hierarchy.png                             | Bin 0 -> 41053 bytes
 .../img/blog/authors/sterling-paramore.png    | Bin 0 -> 154035 bytes
 4 files changed, 460 insertions(+), 1 deletion(-)
 create mode 100644 website/blog/2023-05-02-modeling-ragged-time-varying-hierarchies.md
 create mode 100644 website/static/img/blog/2023-05-02-modeling-ragged-time-varying-hierarchies/hierarchy.png
 create mode 100644 website/static/img/blog/authors/sterling-paramore.png

diff --git a/website/blog/2023-05-02-modeling-ragged-time-varying-hierarchies.md b/website/blog/2023-05-02-modeling-ragged-time-varying-hierarchies.md
new file mode 100644
index 00000000000..d436df2628a
--- /dev/null
+++ b/website/blog/2023-05-02-modeling-ragged-time-varying-hierarchies.md
@@ -0,0 +1,448 @@
+---
+title: "Modeling ragged time-varying hierarchies"
+description: "Learn how to maximize the utility of complex hierarchical data in your analytics warehouse."
+slug: modeling-ragged-time-varying-hierarchies
+
+authors: [sterling_paramore]
+
+tags: [analytics craft]
+hide_table_of_contents: false
+
+date: 2023-05-02
+is_featured: true
+---
+
+This article covers an approach to handling time-varying ragged hierarchies in a <Term id="dimensional-modeling">dimensional model</Term>.  These kinds of data structures are commonly found in manufacturing, where components of a product have both parents and children of arbitrary depth and those components may be replaced over the product's lifetime.  The strategy described here simplifies many common types of analytical and reporting queries.
+
+To help visualize this data, we're going to pretend we are a company that manufactures and rents out eBikes in a ride share application.  When we build a bike, we keep track of the serial numbers of the components that make up the bike.  Any time something breaks and needs to be replaced, we track the old parts that were removed and the new parts that were installed.  We also precisely track the mileage accumulated on each of our bikes.  Our primary analytical goal is to be able to report on the expected lifetime of each component, so we can prioritize improving that component and reduce costly maintenance.
+
+## Data model
+
+Obviously, a real bike could have a hundred or more separate components.  To keep things simple for this article, let's just consider the bike, the frame, a wheel, the wheel rim, tire, and tube.  Our component hierarchy looks like:
+
+<Lightbox src="/img/blog/2023-05-02-modeling-ragged-time-varying-hierarchies/hierarchy.png" title="eBike Hierarchy" />
+
+This hierarchy is *ragged* because different paths through the hierarchy terminate at different depths.  It is *time-varying* because specific components can be added and removed.
+
+Now let's take a look at how this data is represented in our source data systems and how it can be transformed to make analytics queries easier.
+
+### Transactional model
+
+Our ERP system (Enterprise Resource Planning) contains records that log when a specific component serial number (`component_id`) was installed in or removed from a parent assembly component (`assembly_id`).  The top-most assembly component is the eBike itself, which has no parent assembly.  So when an eBike (specifically, the eBike with serial number "Bike-1") is originally constructed, the ERP system would contain records that look like the following.
+
+**`erp_components`:**
+
+| `assembly_id` | `component_id` | `installed_at` | `removed_at` |
+| -             | -              | -              | -            |
+|               | Bike-1         | 2023-01-01     |              |
+| Bike-1        | Frame-1        | 2023-01-01     |              |
+| Bike-1        | Wheel-1        | 2023-01-01     |              |
+| Wheel-1       | Rim-1          | 2023-01-01     |              |
+| Wheel-1       | Tire-1         | 2023-01-01     |              |
+| Tire-1        | Tube-1         | 2023-01-01     |              |
+
+Now let's suppose this bike has been ridden for a while, and on June 1, the user of the bike reported a flat tire.  A service technician then went to the site, replaced the tube that was in the wheel, and installed a new one.  They logged this in the ERP system, causing one record to be updated with a `removed_at` date, and another record to be created with the new tube `component_id`.
+
+
+**`erp_components`:**
+
+| `assembly_id` | `component_id` | `installed_at` | `removed_at` |
+| -             | -              | -              | -            |
+| ...           | ...            | ...            | ...          |
+| Tire-1        | Tube-1         | 2023-01-01     | 2023-06-01   |
+| Tire-1        | Tube-2         | 2023-06-01     |              |
+| ...           | ...            | ...            | ...          |
+
+After a few more months, there is a small crash.  Don't worry, everyone's OK!  However, the wheel (`Wheel-1`)is totally broken and must be replaced (with `Wheel-2`).  When the technician updates the ERP, the entire hierarchy under the replaced wheel is also updated, as shown below.
+
+**`erp_components`:**
+
+| `assembly_id` | `component_id` | `installed_at` | `removed_at` |
+| -             | -              | -              | -            |
+| Bike-1        | Wheel-1        | 2023-01-01     | 2023-08-01   |
+| Wheel-1       | Rim-1          | 2023-01-01     | 2023-08-01   |
+| Wheel-1       | Tire-1         | 2023-01-01     | 2023-08-01   |
+| Tire-1        | Tube-2         | 2023-06-01     | 2023-08-01   | # Note that this part has different install date
+| Bike-1        | Wheel-2        | 2023-08-01     |              |
+| Wheel-2       | Rim-2          | 2023-08-01     |              |
+| Wheel-2       | Tire-2         | 2023-08-01     |              |
+| Tire-2        | Tube-3         | 2023-08-01     |              |
+
+
+After all of the above updates and additions, our ERP data looks like the following.
+
+**`erp_components`:**
+
+| `assembly_id` | `component_id` | `installed_at` | `removed_at` |
+| -             | -              | -              | -            |
+|               | Bike-1         | 2023-01-01     |              |
+| Bike-1        | Frame-1        | 2023-01-01     |              |
+| Bike-1        | Wheel-1        | 2023-01-01     | 2023-08-01   |
+| Wheel-1       | Rim-1          | 2023-01-01     | 2023-08-01   |
+| Wheel-1       | Tire-1         | 2023-01-01     | 2023-08-01   |
+| Tire-1        | Tube-1         | 2023-01-01     | 2023-06-01   |
+| Tire-1        | Tube-2         | 2023-06-01     | 2023-08-01   |
+| Bike-1        | Wheel-2        | 2023-08-01     |              |
+| Wheel-2       | Rim-2          | 2023-08-01     |              |
+| Wheel-2       | Tire-2         | 2023-08-01     |              |
+| Tire-2        | Tube-3         | 2023-08-01     |              |
+
+So that's all fine and good from the perspective of the ERP system.  But this data structure can be difficult to work with if we want to generate reports that calculate the total mileage accumulated on various components, or the average mileage of a particular component type, or how one component type might affect the lifetime of another component.
+
+### Multivalued dimensional model
+
+In dimensional modeling, we have *fact* tables that contain measurements and *dimension* tables that contain the context for those measurements (attributes).  In our eBike data warehouse, we have a fact table that contains one record for each eBike for each day it is ridden and the measured mileage accumulated during rides that day.  This fact table contains *surrogate key* columns, indicated by the `_sk` suffix.  These are usually system-generated keys used to join to other tables in the database; the specific values of these keys are not important.
+
+**`fct_daily_mileage`:**
+
+| `bike_sk` | `component_sk` | `ride_at`    | `miles` |
+| -         | -              | -            | -       |
+| bsk1      | csk1           | 2023-01-01   | 3       |
+| bsk1      | csk1           | 2023-01-02   | 2       |
+| bsk1      | csk1           | 2023-01-03   | 0       |
+| bsk1      | csk1           | 2023-01-04   | 0       |
+| ...       | ...            | ...          | ...     |
+| bsk1      | csk3           | 2023-08-01   | 7       |
+| bsk1      | csk3           | 2023-08-02   | 8       |
+| bsk1      | csk3           | 2023-08-03   | 4       |
+
+One of the dimension tables is a simple table containing information about the individual bikes we have manufactured.
+
+**`dim_bikes`:**
+
+| `bike_sk` | `bike_id` | `color` | `model_name` |
+| -         | -         | -       | -            |
+| bsk1      | Bike-1    | Orange  | Wyld Stallyn |
+
+
+There is a simple many-to-one relationship between `fct_daily_mileage` and `dim_bikes`.  If we need to calculate the total mileage accumulated for each bike in our entire fleet of eBikes, we just join the two tables and aggregate on the `miles` measurement.
+
+```sql
+select
+    dim_bikes.bike_id,
+    sum(fct_daily_mileage.miles) as miles
+from
+    fct_daily_mileage
+inner join
+    dim_bikes
+    on
+        fct_daily_mileage.bike_sk = dim_bikes.bike_sk
+group by
+    1
+```
+
+Extending this to determine if orange bikes get more use than red bikes or whether certain models are preferred are similarly straightforward queries.
+
+Dealing with all of the components is more complicated because there are many components installed on the same day.  The relationship between days when the bikes are ridden and the components is thus *multivalued*.  In `dim_bikes`, there is one record per bike and surrogate key.  In our components dimension will have multiple records with the same surrogate key and will therefore be a *multivalued dimension*.  Of course, to make things even more complicated, the components can change from day to day.  To construct the multivalued dimension table, we break down the time-varying component hierarchy into distinct ranges of time where all of the components in a particular bike remain constant.  At specific points in time where the components are changed, a new surrogate key is created.  The final dimension table for our example above looks like the following, where the `valid_from_at` and `valid_to_at` represent the begin and end of a range of time where all the components of an eBike remain unchanged.
+
+
+**`mdim_components`:**
+
+| `component_sk` | `assembly_id` | `component_id` | `depth` | `installed_at` | `removed_at` | `valid_from_at` | `valid_to_at` |
+| -              | -             | -              | -       | -              | -            | -               | -             |
+| csk1           |               | Bike-1         | 0       | 2023-01-01     |              | 2023-01-01      | 2023-06-01    |
+| csk1           | Bike-1        | Frame-1        | 1       | 2023-01-01     |              | 2023-01-01      | 2023-06-01    |
+| csk1           | Bike-1        | Wheel-1        | 1       | 2023-01-01     | 2023-08-01   | 2023-01-01      | 2023-06-01    |
+| csk1           | Wheel-1       | Rim-1          | 2       | 2023-01-01     | 2023-08-01   | 2023-01-01      | 2023-06-01    |
+| csk1           | Wheel-1       | Tire-1         | 2       | 2023-01-01     | 2023-08-01   | 2023-01-01      | 2023-06-01    |
+| csk1           | Tire-1        | Tube-1         | 3       | 2023-01-01     | 2023-06-01   | 2023-01-01      | 2023-06-01    |
+| csk2           |               | Bike-1         | 0       | 2023-01-01     |              | 2023-06-01      | 2023-08-01    |
+| csk2           | Bike-1        | Frame-1        | 1       | 2023-01-01     |              | 2023-06-01      | 2023-08-01    |
+| csk2           | Bike-1        | Wheel-1        | 1       | 2023-01-01     | 2023-08-01   | 2023-06-01      | 2023-08-01    |
+| csk2           | Wheel-1       | Rim-1          | 2       | 2023-01-01     | 2023-08-01   | 2023-06-01      | 2023-08-01    |
+| csk2           | Wheel-1       | Tire-1         | 2       | 2023-01-01     | 2023-08-01   | 2023-06-01      | 2023-08-01    |
+| csk2           | Tire-1        | Tube-2         | 3       | 2023-06-01     | 2023-08-01   | 2023-06-01      | 2023-08-01    |
+| csk3           |               | Bike-1         | 0       | 2023-01-01     |              | 2023-08-01      |               |
+| csk3           | Bike-1        | Frame-1        | 1       | 2023-01-01     |              | 2023-08-01      |               |
+| csk3           | Bike-1        | Wheel-2        | 1       | 2023-08-01     |              | 2023-08-01      |               |
+| csk3           | Wheel-2       | Rim-2          | 2       | 2023-08-01     |              | 2023-08-01      |               |
+| csk3           | Wheel-2       | Tire-2         | 2       | 2023-08-01     |              | 2023-08-01      |               |
+| csk3           | Tire-2        | Tube-3         | 3       | 2023-08-01     |              | 2023-08-01      |               |
+
+Now, let's look at how this structure can help in writing queries.  In a later section of this article, we'll examine the SQL code that can take our ERP table and convert it into this dimensional model.
+
+### Mileage for a component
+
+Suppose we wanted to know the total mileage accumulated on "Wheel-1".  The SQL code for determining this is very similar to that for determining the mileage for a given bike.
+
+```sql
+select
+    mdim_components.component_id,
+    sum(fct_daily_mileage.miles) as miles
+from
+    fct_daily_mileage
+inner join
+    mdim_components
+    on
+        fct_daily_mileage.component_sk = mdim_components.component_sk
+group by
+    1
+where
+    component_id = 'Wheel-1'
+```
+
+:::caution
+
+One thing to be *very cautious* about when working with multivalued dimensions is that you need to be careful interpreting aggregations.  For example, suppose  we chose to aggregate on `top_assembly_id` (to reduce clutter, this field is not shown in the data model above because it is just "Bike-1" for each record).  For this aggregation, we would be over-counting the total mileage on that top assembly because the join would result in a Cartesian product and thus we'd get a ["fan-out" situation](https://community.looker.com/technical-tips-tricks-1021/the-problem-of-sql-fanouts-30232).
+:::
+
+### Bonus: Finding components installed at the same time as other components
+
+This structure simplifies other kinds of interesting analysis.  Suppose we wanted to start exploring how one component affects another, like whether certain brands of tube needed to be replaced more often if they were in a new brand of tire.  We can do this by partitioning the data into the segments of time where the components are not changing and looking for other components installed at the same time.  For example, to find all of the components that were ever installed at the same time "Tube-3" was installed, we can collect them with a simple window function.  We could then use the results of this query in a regression or other type of statistical analysis.
+
+```sql
+select distinct
+    component_id
+from
+    mdim_components
+qualify
+    sum(iff(component_id = 'Tube-3', 1, 0)) over (partition by valid_from_at, valid_to_at) > 0
+```
+
+
+## SQL code to build the dimensional model
+
+Now we get to the fun part!  This section shows how to take the ERP source data and turn it into the multivalued dimensional model.  This SQL code was written and tested using Snowflake, but should be adaptable to other dialects.
+
+### Traversing the hierarchy
+
+The first step will be to traverse the hierarchy of components to find all components that belong to the same top assembly.  In our example above, we only had one bike and thus just one top assembly; in a real system, there will be many (and we may even swap components between different top assemblies!).
+
+The key here is to use a [recursive join](https://docs.snowflake.com/en/sql-reference/constructs/with#recursive-clause) to move from the top of the hierarchy to all children and grandchildren.  The top of the hierarchy is easy to identify because they are the only records without any parents.
+
+```sql
+with recursive
+-- Contains our source data with records that link a child to a parent
+components as (
+    select
+        *,
+        -- Valid dates start as installed/removed, but may be modified as we traverse the hierarchy below
+        installed_at as valid_from_at,
+        removed_at as valid_to_at
+    from
+        erp_components
+),
+
+-- Get all the source records that are at the top of hierarchy
+top_assemblies as (
+    select * from components where assembly_id is null
+),
+
+-- This is where the recursion happens that traverses the hierarchy
+traversal as (
+    -- Start at the top of hierarchy
+    select
+        -- Keep track of the depth as we traverse down
+        0 as component_hierarchy_depth,
+        -- Flag to determine if we've entered a circular relationship
+        false as is_circular,
+        -- Define an array that will keep track of all of the ancestors of a component
+        [component_id] as component_trace,
+        -- At the top of the hierarchy, the component is the top assembly
+        component_id as top_assembly_id,
+
+        assembly_id,
+        component_id,
+
+        installed_at,
+        removed_at,
+        valid_from_at,
+        valid_to_at
+    from
+        top_assemblies
+
+    union all
+
+    -- Join the current layer of the hierarchy with the next layer down by linking
+    -- the current component id to the assembly id of the child
+    select
+        traversal.component_hierarchy_depth + 1 as component_hierarchy_depth,
+        -- Check for any circular dependencies
+        array_contains(components.component_id::variant, traversal.component_trace) as is_circular,
+        -- Append trace array
+        array_append(traversal.component_trace, components.component_id) as component_trace,
+        -- Keep track of the top of the assembly
+        traversal.top_assembly_id,
+
+        components.assembly_id,
+        components.component_id,
+
+        components.installed_at,
+        components.removed_at,
+        -- As we recurse down the hierarchy, only want to consider time ranges where both
+        -- parent and child are installed; so choose the latest "from" timestamp and the earliest "to".
+        greatest(traversal.valid_from_at, components.valid_from_at) as valid_from_at,
+        least(traversal.valid_to_at, components.valid_to_at) as valid_to_at
+    from
+        traversal
+    inner join
+        components
+        on
+            traversal.component_id = components.assembly_id
+            and
+            -- Exclude component assemblies that weren't installed at the same time
+            -- This may happen due to source data quality issues
+            (
+                traversal.valid_from_at < components.valid_to_at
+                and
+                traversal.valid_to_at >= components.valid_from_at
+            )
+    where
+        -- Stop if a circular hierarchy is detected
+        not array_contains(components.component_id::variant, traversal.component_trace)
+        -- There can be some bad data that might end up in hierarchies that are artificially extremely deep
+        and traversal.component_hierarchy_depth < 20
+),
+
+final as (
+    -- Note that there may be duplicates at this point (thus "distinct").
+    -- Duplicates can happen when a component's parent is moved from one grandparent to another.
+    -- At this point, we only traced the ancestry of a component, and fixed the valid/from dates
+    -- so that all child ranges are contained in parent ranges.
+
+    select distinct *
+    from
+        traversal
+    where
+        -- Prevent zero-time (or less) associations from showing up
+        valid_from_at < valid_to_at
+)
+
+select * from final
+```
+
+At the end of the above step, we have a table that looks very much like the `erp_components` that it used as the source, but with a few additional valuable columns:
+
+* `top_assembly_id` - This is the most important output of the hierarchy traversal.  It ties all sub components to a their common parent.  We'll use this in the next step to chop up the hierarchy into all the distinct ranges of time where the components that share a common top assembly are constant (and each distict range of time and `top_assembly_id` getting their own surrogate key).
+* `component_hierarchy_depth` - Indicates how far removed a component is from the top assembly.
+* `component_trace` - Contains an array of all the components linking this component to the top assembly.
+* `valid_from_at`/`valid_to_at` - If you have really high-quality source data, these will be identical to `installed_at`/`removed_at`.  However, in the real world, we've found cases where the installed and removal dates are not consistent between parent and child, either due to a data entry error or a technician forgetting to note when a component was removed.  So for example, we may have a parent assembly that was removed along with all of its children, but only the parent assembly has `removed_at` populated.  At this point, the `valid_from_at` and `valid_to_at` tidy up these kinds of scenarios.
+
+### Temporal range join
+
+The last step is perform a [temporal range join](https://discourse.getdbt.com/t/joining-snapshot-tables-time-range-based-joins/3226) between the top assembly and all of its descendents.  This is what splits out all of the time-varying component changes into distinct ranges of time where the component hierarchy is constant.  This range join makes use of [the dbt macro in this gist](https://gist.github.com/gnilrets/48886b4c8945dde1da13547c2373df73), the operation of which is out-of-scope for this article, but you are encouraged to investigate it and the discourse post mentioned earlier.
+
+```sql
+-- Start with all of the assemblies at the top (hierarchy depth = 0)
+with l0_assemblies as (
+    select
+        top_assembly_id,
+        component_id,
+        -- Prep fields required for temporal range join
+        {{ dbt_utils.surrogate_key(['component_id', 'valid_from_at']) }} as dbt_scd_id,
+        valid_from_at as dbt_valid_from,
+        valid_to_at as dbt_valid_to
+    from
+        component_traversal
+    where
+        component_hierarchy_depth = 0
+),
+
+components as (
+    select
+        top_assembly_id,
+        component_hierarchy_depth,
+        component_trace,
+        assembly_id,
+        component_id,
+        installed_at,
+        removed_at,
+        -- Prep fields required for temporal range join
+        {{ dbt_utils.surrogate_key(['component_trace', 'valid_from_at'])}} as dbt_scd_id,
+        valid_from_at as dbt_valid_from,
+        valid_to_at as dbt_valid_to
+    from
+        component_traversal
+),
+
+-- Perform temporal range join
+{{
+    trange_join(
+      left_model='l0_assemblies',
+      left_fields=[
+        'top_assembly_id',
+      ],
+      left_primary_key='top_assembly_id',
+      right_models={
+        'components': {
+          'fields': [
+              'component_hierarchy_depth',
+              'component_trace',
+              'assembly_id',
+              'component_id',
+              'installed_at',
+              'removed_at',
+          ],
+          'left_on': 'component_id',
+          'right_on': 'top_assembly_id',
+        }
+      }
+    )
+}}
+
+select
+    surrogate_key,
+    top_assembly_id,
+    component_hierarchy_depth,
+    component_trace,
+    assembly_id,
+    component_id,
+    installed_at,
+    removed_at,
+    valid_from_at,
+    valid_to_at
+from
+    trange_final
+order by
+    top_assembly_id,
+    valid_from_at,
+    component_hierarchy_depth
+```
+
+## Bonus: component swap
+
+Before we go, let's investigate one other interesting scenario.  Suppose we have two bikes, "Bike-1" and "Bike-2".  While performing service, a technician notices that the color on the rim of "Bike-2" matches with the frame of "Bike-1" and vice-versa.  Perhaps there was a mistake made during the initial assembly process?  The technician decides to swap the wheels between the two bikes.  The ERP system then shows that "Wheel-1" was removed from "Bike-1" on the service date and that "Wheel-1" was installed in "Bike-2" on the same date (similarly for "Wheel-2").  To reduce clutter below, we'll ignore Frames and Tubes.
+
+**`erp_components`:**
+
+| `assembly_id` | `component_id` | `installed_at` | `removed_at` |
+| -             | -              | -              | -            |
+|               | Bike-1         | 2023-01-01     |              |
+| Bike-1        | Wheel-1        | 2023-01-01     | 2023-06-01   |
+| Wheel-1       | Rim-1          | 2023-01-01     |              |
+| Wheel-1       | Tire-1         | 2023-01-01     |              |
+|               | Bike-2         | 2023-02-01     |              |
+| Bike-2        | Wheel-2        | 2023-02-01     | 2023-06-01   |
+| Wheel-2       | Rim-2          | 2023-02-01     |              |
+| Wheel-2       | Tire-2         | 2023-02-01     |              |
+| Bike-2        | Wheel-1        | 2023-06-01     |              |
+| Bike-1        | Wheel-2        | 2023-06-01     |              |
+
+When this ERP data gets converted into the multivalued dimension, we get the table below.  In the ERP data, only one kind of component assembly, the wheel, was removed/installed, but in the dimensional model all of the child components come along for the ride.  In the table below, we see that "Bike-1" and "Bike-2" each have two distinct ranges of valid time, one prior to the wheel swap, and one after.
+
+**`mdim_components`:**
+
+| `component_sk` | `top_assembly_id` | `assembly_id` | `component_id` | `valid_from_at` | `valid_to_at` |
+| -              | -                 | -             | -              | -               | -             |
+| sk1            | Bike-1            |               | Bike-1         | 2023-01-01      | 2023-06-01    |
+| sk1            | Bike-1            | Bike-1        | Wheel-1        | 2023-01-01      | 2023-06-01    |
+| sk1            | Bike-1            | Wheel-1       | Rim-1          | 2023-01-01      | 2023-06-01    |
+| sk1            | Bike-1            | Wheel-1       | Tire-1         | 2023-01-01      | 2023-06-01    |
+| sk2            | Bike-1            |               | Bike-1         | 2023-06-01      |               |
+| sk2            | Bike-1            | Bike-1        | Wheel-2        | 2023-06-01      |               |
+| sk2            | Bike-1            | Wheel-2       | Rim-2          | 2023-06-01      |               |
+| sk2            | Bike-1            | Wheel-2       | Tire-2         | 2023-06-01      |               |
+| sk3            | Bike-2            |               | Bike-2         | 2023-02-01      | 2023-06-01    |
+| sk3            | Bike-2            | Bike-2        | Wheel-2        | 2023-02-01      | 2023-06-01    |
+| sk3            | Bike-2            | Wheel-2       | Rim-2          | 2023-02-01      | 2023-06-01    |
+| sk3            | Bike-2            | Wheel-2       | Tire-2         | 2023-02-01      | 2023-06-01    |
+| sk4            | Bike-2            |               | Bike-2         | 2023-06-01      |               |
+| sk4            | Bike-2            | Bike-2        | Wheel-1        | 2023-06-01      |               |
+| sk4            | Bike-2            | Wheel-1       | Rim-1          | 2023-06-01      |               |
+| sk4            | Bike-2            | Wheel-1       | Tire-1         | 2023-06-01      |               |
+
+## Summary
+
+In this article, we've explored a strategy for creating a dimensional model for ragged time-varying hierarchies.  We used a simple toy system involving one or two eBikes.  In the real world, there would be many more individual products, deeper hierarchies, more component attributes, and the install/removal dates would likely be captured with a timestamp component as well.  The model described here works very well even in these messier real world cases.
+
+If you have any questions or comments, please reach out to me by commenting on this post or contacting me on dbt slack (@Sterling Paramore).
diff --git a/website/blog/authors.yml b/website/blog/authors.yml
index 6d222e8a543..e9b48bd02fc 100644
--- a/website/blog/authors.yml
+++ b/website/blog/authors.yml
@@ -186,7 +186,7 @@ emily_riederer:
       url: https://twitter.com/emilyriederer
     - icon: fa-readme
       url: https://emilyriederer.com
-      
+
 grace_goheen:
   image_url: /img/blog/authors/grace-goheen.jpeg
   job_title: Analytics Engineer
@@ -461,6 +461,17 @@ simon_podhajsky:
   name: Simon Podhajsky
   organization: iLife Technologies
 
+sterling_paramore:
+  description: |
+    Sterling Paramore started his career in theoretical and computation biophysics and learned that working with data was way more fun than being locked in the ivory tower.  He loves solving data engineering and data analytics problems and has been a long time evangelist for dbt.
+  image_url: /img/blog/authors/sterling-paramore.png
+  job_title: Sr Staff Data Engineer
+  links:
+    - icon: fa-linkedin
+      url: https://www.linkedin.com/in/sterlingparamore/
+  name: Sterling Paramore
+  organization: Mainspring Energy
+
 sung_chung:
   image_url: /img/blog/authors/sung.jpeg
   job_title: Solutions Architect
diff --git a/website/static/img/blog/2023-05-02-modeling-ragged-time-varying-hierarchies/hierarchy.png b/website/static/img/blog/2023-05-02-modeling-ragged-time-varying-hierarchies/hierarchy.png
new file mode 100644
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From 0c2ac12e893505adc49c30cd1ed2cb0235e86df7 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 24 Jul 2023 16:52:25 -0700
Subject: [PATCH 269/703] Update sl-partner-integration-guide.md

More changes to best practices
---
 .../sl-partner-integration-guide.md           | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 968afc1947c..21eb4bb660f 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -62,9 +62,9 @@ Users shouldn't generally be allowed to modify aggregations unless they are perf
 **Time series alignment and using metric_time**<br />
 Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes. 
   
-When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). As such, when building an application, we recommend exposing `metric_time` as a special time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the rest is clarifying as a way to separate out the primary one. 
+When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the rest is clarifying as a way to separate out the primary one so that people do not confuse how metrics should be plotted.
 
-Also, when a user requests a time granularity change for the main time series, the query should use `metric_time`. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
+Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
 
 **Units consistency**<br />
 If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
@@ -72,18 +72,16 @@ If units are supported, it's vital to avoid plotting data incorrectly with diffe
 **Traceability of metric and dimension changes** <br />
 When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
 
-### Discoverability 
-
-1. Consider treating [metrics](/docs/build/metrics-overview) as first-class objects rather than measures. Metrics offer a higher-level and more contextual way to interact with data, reducing the burden on end-users to manually aggregate data.
 
-2. Easy metric interactions: Provide users with an intuitive approach to:
+### Discoverability 
 
+- Consider treating [metrics](/docs/build/metrics-overview) as first-class objects rather than measures. Metrics offer a higher-level and more contextual way to interact with data, reducing the burden on end-users to manually aggregate data.
+- Easy metric interactions: Provide users with an intuitive approach to:
     * Search for Metrics &mdash; Users should be able to easily search and find relevant metrics. Metrics can serve as the starting point to lead users into exploring dimensions.
     * Search for Dimensions &mdash; Users should be able to query metrics with associated dimensions, allowing them to gain deeper insights into the data.
     * Filter by Dimension Values &mdash; Expose and enable users to filter metrics based on dimension values, encouraging data analysis and exploration.
     * Filter additional metadata &mdash; Allow users to filter metrics based on other available metadata, such as metric type and default time granularity.
-
-3. Suggested Metrics: Ideally, the system should intelligently suggest relevant metrics to users based on their team's activities. This approach encourages user exposure, facilitates learning, and supports collaboration among team members.
+- Suggested Metrics: Ideally, the system should intelligently suggest relevant metrics to users based on their team's activities. This approach encourages user exposure, facilitates learning, and supports collaboration among team members.
 
 By implementing these recommendations, the data interaction process becomes more user-friendly, empowering users to gain valuable insights without the need for extensive data manipulation.
 
@@ -103,6 +101,18 @@ The goal is to organize metrics into a hierarchy in our configurations, instead
 
 This hierarchy helps you organize metrics based on a specific criteria, such as business unit or team. By providing this structured organization, users can find and navigate metrics more efficiently, enhancing their overall data analysis experience.
 
+### Query flexibility
+
+Allow users to query either one metric alone without dimensions or multiple metrics with dimensions
+
+- Allow toggling between metrics / dimensions seamlessly
+- Be clear on exposing what dimensions are queryable with what metrics and hide things that don’t apply, and vice versa.
+- Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
+  * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity isn't available to the user. Our APIs have functionality that will help you surface the correct granularities
+- We recommend that time granularity is treated as a general time dimension-specific concept and that it can be applied to more than just the primary aggregation (or `metric_time`). Consider a situation where a user wants to look at `sales` over time by `customer signup month`; in this situation, having the ability able to apply granularities to both time dimensions is crucial. Note: initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as your solution evolves. 
+- You should allow users to filter on date ranges and expose a calendar and nice presets for filtering these
+  * For example: last 30 days, last week etc.
+
 ### Context and interpretation
 
 For better analysis, it's best to have the context of the metrics close to where the analysis is happening. We recommend the following:
@@ -114,20 +124,10 @@ For better analysis, it's best to have the context of the metrics close to where
   * Include lineage information to understand the metric's origin.
 - Allow for creating other metadata that’s useful for the metric. We can provide some of this information in our configuration (Display name, Default Granularity for View, Default Time range), but there may be other metadata that your tool wants to provide to make the metric richer.
 
-### Query flexibility
-
-Allow users to query either one metric alone without dimensions or multiple metrics with dimensions
-
-- Allow toggling between metrics / dimensions seamlessly
-- Be clear on exposing what dimensions are queryable with what metrics and hide things that don’t apply, and vice versa.
-- Include Presets for filtering date ranges and calendar for filtering time dimensions
-  * For example, last 30 days, last week etc.
-- Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
-  * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity" isn't available to the user.
-- We recommend that granularity is a dimension-specific concept and can be applied to more than just the primary aggregation or `metric_time`. Initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as the solution evolves.
-
 ### Example stages of an integration
 
+These are recommendations on how to evolve a Semantic Layer integration and not a strict runbook.
+
 **Stage 1 - The basic**
 * Supporting and using the new [JDBC](/docs/dbt-cloud-apis/sl-jdbc) is the first step. Refer to the [dbt Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) for more technical details. 
 

From 099d2b923445cc863eb9b305acc487f1c0e021e6 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 24 Jul 2023 16:57:58 -0700
Subject: [PATCH 270/703] Update sl-partner-integration-guide.md

A couple more updates!
---
 .../guides/dbt-ecosystem/sl-partner-integration-guide.md  | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 21eb4bb660f..b431091ffe6 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -61,8 +61,10 @@ Users shouldn't generally be allowed to modify aggregations unless they are perf
 
 **Time series alignment and using metric_time**<br />
 Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes. 
-  
-When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the rest is clarifying as a way to separate out the primary one so that people do not confuse how metrics should be plotted.
+
+When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). 
+
+As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the other time dimensions is clarifying so that people do not confuse how metrics should be plotted. 
 
 Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
 
@@ -110,7 +112,7 @@ Allow users to query either one metric alone without dimensions or multiple metr
 - Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
   * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity isn't available to the user. Our APIs have functionality that will help you surface the correct granularities
 - We recommend that time granularity is treated as a general time dimension-specific concept and that it can be applied to more than just the primary aggregation (or `metric_time`). Consider a situation where a user wants to look at `sales` over time by `customer signup month`; in this situation, having the ability able to apply granularities to both time dimensions is crucial. Note: initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as your solution evolves. 
-- You should allow users to filter on date ranges and expose a calendar and nice presets for filtering these
+- You should allow users to filter on date ranges and expose a calendar and nice presets for filtering these. 
   * For example: last 30 days, last week etc.
 
 ### Context and interpretation

From e47fc4ee074fa42ae83404993354f7aeff699957 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Mon, 24 Jul 2023 19:04:52 -0700
Subject: [PATCH 271/703] update getting started guide

---
 website/docs/docs/build/about-metricflow.md   | 185 ++--
 website/docs/docs/build/cumulative-metrics.md | 114 ++-
 website/docs/docs/build/derived-metrics.md    |  63 +-
 .../docs/docs/build/metricflow-time-spine.md  |  12 +-
 website/docs/docs/build/ratio-metrics.md      |  50 +-
 website/docs/docs/build/simple.md             |  35 +-
 website/docs/docs/build/sl-getting-started.md | 134 ++-
 .../docs/docs/dbt-cloud-apis/sl-manifest.md   | 866 +-----------------
 8 files changed, 405 insertions(+), 1054 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 67915af7f0a..eabf3b43fc2 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -70,23 +70,19 @@ MetricFlow supports different metric types:
 
 In the upcoming sections, we'll show how data practitioners currently calculate metrics and compare it to how MetricFlow makes defining metrics easier and more flexible. 
 
-The following example data schema image shows a number of different types of data tables:
+The following example data is based off the Jaffle Shop repo. You can view the complete [dbt project here](https://github.com/dbt-labs/jaffle-sl-template). The tables we're using in our example model are:
 
-- `transactions` is a production data platform export that has been cleaned up and organized for analytical consumption
-- `visits` is a raw event log
-- `stores` is a cleaned-up and fully normalized dimensional table from a daily production database export
-- `products` is a dimensional table that came from an external source such as a wholesale vendor of the goods this store sells.
-- `customers` is a partially denormalized table in this case with a column derived from the transactions table through some upstream process
+- `orders` is a production data platform export that has been cleaned up and organized for analytical consumption
+- `customers` is a partially denormalized table in this case with a column derived from the orders table through some upstream process
 
-![MetricFlow-SchemaExample](/img/docs/building-a-dbt-project/MetricFlow-SchemaExample.jpeg)
+<!-- ![MetricFlow-SchemaExample](/img/docs/building-a-dbt-project/MetricFlow-SchemaExample.jpeg) -->
 
-To make this more concrete, consider the metric `revenue`, which is defined using the SQL expression:
+To make this more concrete, consider the metric `order_total`, which is defined using the SQL expression:
 
-`select sum(price * quantity) as revenue from transactions` 
-
-This expression calculates the total revenue by multiplying the price and quantity for each transaction and then adding up all the results. In business settings, the metric `revenue` is often calculated according to different categories, such as:
-- Time, for example `date_trunc(created_at, 'day')`
-- Product, using `product_category` from the `product` table.
+`select sum(order_total) as order_total from orders` 
+This expression caclulates the revenue from each order by summing the order_total column in the orders table. In a business setting, the metric order_total is often calcualted according to different categoris, such as"
+- Time, for example `date_trunc(ordered_at, 'day')`
+- Order Type, using `is_food_order` dimension from the `orders` table.
 
 ### Calculate metrics
 
@@ -95,21 +91,21 @@ Next, we'll compare how data practitioners currently calculate metrics with mult
 <Tabs>
 <TabItem value="mulqueries" label="Calculate with multiple queries">
 
-The following example displays how data practitioners typically would calculate the revenue metric aggregated. It's also likely that analysts are asked for more details on a metric, like how much revenue came from bulk purchases. 
+The following example displays how data practitioners typically would calculate the order_total metric aggregated. It's also likely that analysts are asked for more details on a metric, like how much revenue came from new customers. 
 
 Using the following query creates a situation where multiple analysts working on the same data, each using their own query method &mdash; this can lead to confusion, inconsistencies, and a headache for data management.
 
 ```sql
 select
-    date_trunc(transactions.created_at, 'day') as day
-  , products.category as product_category
-  , sum(transactions.price * transactions.quantity) as revenue
+    date_trunc('day',orders.ordered_at) as day, 
+    case when customers.first_ordered_at is not null then true else false end as is_new_customer,
+    sum(orders.order_total) as order_total
 from
-  transactions
+  orders
 left join
-  products
+  customers
 on
-  transactions.product_id = products.product_id
+  orders.customer_id = customers.customer_id
 group by 1, 2
 ```
 
@@ -118,126 +114,139 @@ group by 1, 2
 
 > Introducing MetricFlow, a key component of the dbt Semantic Layer 🤩 - simplifying data collaboration and governance.
 
-In the following three example tabs, use MetricFlow to define a semantic model that uses revenue as a metric and a sample schema to create consistent and accurate results &mdash; eliminating confusion, code duplication, and streamlining your workflow.
+In the following three example tabs, use MetricFlow to define a semantic model that uses order_total as a metric and a sample schema to create consistent and accurate results &mdash; eliminating confusion, code duplication, and streamlining your workflow.
 
 <Tabs>
 <TabItem value="example1" label="Revenue example">
 
-In this example, a measure named revenue is defined based on two columns in the `schema.transactions` table. The time dimension `ds` provides daily granularity and can be aggregated to weekly or monthly time periods. Additionally, a categorical dimension called `is_bulk_transaction` is specified using a case statement to capture bulk purchases.
+In this example, a measure named `order_total` is defined based on the order_total column in the `orders` table. The time dimension `metric_time` provides daily granularity and can be aggregated to weekly or monthly time periods. Additionally, a categorical dimension called `is_new_customer` is specified in the `customers` semantic model.
 
 
 ```yaml
 semantic_models:
-  - name: transactions
-    description: "A record for every transaction that takes place. Carts are considered multiple transactions for each SKU."
-    owners: support@getdbt.com
-    model: (ref('transactions'))
+  #The name of the semantic model.
+  - name: orders
+    description: |
+      Model containting order data. The grain of the table is the order id.
+    
+    #The name of the dbt model and schema
+    model: ref('orders')
     defaults:
-        agg_time_dimension: metric_time
-
-  # --- entities ---
+      agg_time_dimension: metric_time
+    #Entities. These usually corespond to keys in the table.table.
     entities:
-      - name: transaction_id
+      - name: order_id
         type: primary
-      - name: customer_id
-        type: foreign
-      - name: store_id
-        type: foreign
-      - name: product_id
+      - name: customer
         type: foreign
+        expr: customer_id
 
-    # --- measures ---
+    #Measures. These are the aggregations on the columns in the table.
     measures:
-      - name: revenue
-        description:
-        expr: price * quantity
-        agg: sum
-      - name: quantity
-        description: Quantity of products sold
-        expr: quantity
+      - name: order_total
         agg: sum
-      - name: active_customers
-        description: A count of distinct customers completing transactions
-        expr: customer_id
-        agg: count_distinct
-
-    # --- dimensions ---
+   #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
     dimensions:
       - name: metric_time
+        expr: cast(ordered_at as date)
         type: time
-        expr: date_trunc('day', ts)
         type_params:
           time_granularity: day
-      - name: is_bulk_transaction
+ - name: customers
+    defaults:
+      agg_time_dimension: first_ordered_at
+    description: |
+      Customer dimension table. The grain of the table is one row per customer.
+    #The name of the dbt model and schema
+    model: ref('customers')
+    #Entities. These usually corespond to keys in the table.
+    entities:
+      - name: customer
+        type: primary
+        expr: customer_id
+    dimensions:
+      - name: is_new_customer
         type: categorical
-        expr: case when quantity > 10 then true else false end
+        expr: case when first_ordered_at is not null then true else false end
+      - name: first_ordered_at
+        type: time 
+        type_params:
+          time_granularity: day
+
   ```
 
 </TabItem>
-<TabItem value="example2" label="Product example">
+<TabItem value="example2" label="More dimensions example">
 
-Similarly, you could then add a `products` semantic model on top of the `products` model to incorporate even more dimensions to slice and dice your revenue metric. 
-
-Notice the identifiers present in the semantic models `products` and `transactions`. MetricFlow does the heavy-lifting for you by traversing the appropriate join keys to identify the available dimensions to slice and dice your `revenue` metric. 
+Similarly, you could then add additonal dimensions like `is_food_order` to your semantic models to incorporate even more dimensions to slice and dice your revenue order_total. 
 
 ```yaml
 semantic_models:
-  - name: products
-    description: A record for every product available through our retail stores.
-    owners: support@getdbt.com
-    model: ref('products')
-
-  # --- identifiers ---
+- name: orders
+    description: |
+      Model containting order data. The grain of the table is the order id.
+    
+    #The name of the dbt model and schema
+    model: ref('orders')
+    defaults:
+      agg_time_dimension: metric_time
+    #Entities. These usually corespond to keys in the table.table.
     entities:
-      - name: product_id
+      - name: order_id
         type: primary
+      - name: customer
+        type: foreign
+        expr: customer_id
 
-  # --- dimensions ---
+    #Measures. These are the aggregations on the columns in the table.
+    measures:
+      - name: order_total
+        agg: sum
+   #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
     dimensions:
-      - name: category
-        type: categorical
-      - name: brand
-        type: categorical
-      - name: is_perishable
-        type: categorical
-        expr: |
-          category in ("vegetables", "fruits", "dairy", "deli")
+      - name: metric_time
+        expr: cast(ordered_at as date)
+        type: time
+        type_params:
+          time_granularity: day
+       - name: is_food_order
+         type: categorical
 ```
 </TabItem>
 <TabItem value="example3" label="Advanced example">
 
-Imagine an even more difficult metric is needed, like the amount of money earned each day by selling perishable goods per active customer. Without MetricFlow the data practitioner's original SQL might look like this:
+Imagine an even more complex metric is needed, like the amount of money earned each day from food orders from returning customers. Without MetricFlow the data practitioner's original SQL might look like this:
 
 ```sql
 select
-    date_trunc(transactions.created_at, 'day') as day
-  , products.category as product_category
-  , sum(transactions.price * transactions.quantity) as revenue
-  , count(distinct customer_id) as active_customers
-  , sum(transactions.price * transactions.quantity)/count(distinct customer_id) as perishable_revenues_per_active_customer
+    date_trunc('day',orders.ordered_at) as day, 
+    sum(case when is_food_order = true then order_total else null end) as food_order,
+    sum(orders.order_total) as sum_order_total,
+    food_order/sum_order_total
 from
-  transactions
+  orders
 left join
-  products
+  customers
 on
-  transactions.product_id = products.product_id
-where 
-  products.category in ("vegetables", "fruits", "dairy", "deli")
-group by 1, 2
+  orders.customer_id = customers.customer_id
+where
+  case when customers.first_ordered_at is not null then true else false end = true
+group by 1
 ```
 
 MetricFlow simplifies the SQL process via metric YAML configurations as seen below. You can also commit them to your git repository to ensure everyone on the data and business teams can see and approve them as the true and only source of information.
 
 ```yaml
 metrics:
-  - name: perishables_revenue_per_active_customer
-    description: Revenue from perishable goods (vegetables, fruits, dairy, deli) for each active store.
+  - name: food_order_pct_of_order_total
+    description: Revenue from food orders in each store
+    label: "Food % of Order Total"
     type: ratio
     type_params:
-      numerator: revenue
+      numerator: food_order
       denominator: active_customers
     filter: |
-      {{dimension('perishable_goods')}} in ('vegetables',' fruits', 'dairy', 'deli')
+      {{dimension('is_new_customer')}} = true
 ```
 </TabItem>
 </Tabs>
diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index efdde600635..518b538300a 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -6,7 +6,22 @@ sidebar_label: Cumulative
 tags: [Metrics, Semantic Layer]
 ---
 
-Cumulative metrics aggregate a measure over a given window. If no window is specified, the window is considered infinite and accumulates values over all time.
+Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. This metric is common for calculating things like weekly active users, or month to date revenue. The full metrics spec, as well as examples of cumulative metric are given below.
+
+# Cumulative Metrics Spec
+```yaml
+metrics:
+  - name: the metric name # Required
+    description: the metric description # Optinal
+    type: simple # Required
+    label: The value that will be displayed in downstream tools # Required
+    type_params: # Required
+      measure: the measure you are refrencing # Required
+      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Can not be used with window
+      grain_to_date: sets the accumulation grain i.e month will accumulated data for one month, then restart at the beggining of the next. # Optional. Can not be used with grain_to_date. 
+
+
+```
 
 :::info MetricFlow time spine required
 
@@ -14,18 +29,31 @@ You will need to create the [time spine model](/docs/build/metricflow-time-spine
 
 :::
 
+# Cumulative Metrics Example
+
 ```yaml
 # Cumulative metrics aggregate a measure over a given window. The window is considered infinite if no window parameter is passed (accumulate the measure over all time)
 metrics:
-- name: wau_rolling_7
-  owners:
-    - support@getdbt.com
-  type: cumulative
-  type_params:
-    measures:
-      - distinct_users
-    #Omitting window will accumulate the measure over all time
-    window: 7 days
+  - name: cumulative_order_total
+    label: Cumulative Order total (All Time)    
+    description: The cumulative value of all orders
+    type: cumulative
+    type_params:
+      measure: order_total
+  - name: cumulative_order_total_l1m
+    label: Cumulative Order total (L1M)   
+    description: Trailing 1 month cumulative order amount
+    type: cumulative
+    type_params:
+      measure: order_total
+      window: 1 month
+  - name: cumulative_order_total_mtd
+    label: Cumulative Order total (MTD)
+    description: The month to date value of all orders
+    type: cumulative
+    type_params:
+      measure: order_total
+      grain_to_date: month
 ```
 
 ### Window options
@@ -38,27 +66,25 @@ This section details examples of when you specify and don't specify window optio
 
 If a window option is specified, the MetricFlow framework applies a sliding window to the underlying measure. 
 
-Suppose the underlying measure `distinct_users` is configured as such to reflect a count of distinct users by user_id and user_status. 
+Suppose the underlying measure `customers` which is configured to count the unique customers making orders at the Jaffle shop.
 
 ```yaml
 measures:
-  - name: distinct_users
-  description: The number of distinct users creating mql queries
-  expr: case when user_status in ('PENDING','ACTIVE') then user_id else null end
-  agg: count_distinct
+      - name: customers
+        expr: customer_id
+        agg: count_distinct
 ```
 
-We can write a cumulative metric `wau_rolling_7` as such: 
+We can write a cumulative metric `weekly_customers` as such: 
 
 ``` yaml
 metrics: 
-  name: wau_rolling_7
+  - name: weekly_customers
   # Define the measure and the window.
   type: cumulative
   type_params:
-    measures:
-      - distinct_users
-    # the default window is infinity - omitting window will accumulate the measure over all time
+    measure: customers
+    # Setting the window to 7 days since we want to track weekly active 
     window: 7 days
 ```
 
@@ -67,7 +93,7 @@ From the sample YAML above, note the following:
 * `type`: Specify cumulative to indicate the type of metric. 
 * `type_params`: Specify the measure you want to aggregate as a cumulative metric. You have the option of specifying a `window`, or a `grain to date`.  
 
-For example, in the `wau_rolling_7` cumulative metric, MetricFlow takes a sliding 7-day window of relevant users and applies a count distinct function.
+For example, in the `weekly_customers` cumulative metric, MetricFlow takes a sliding 7-day window of relevant customers and applies a count distinct function.
 
 If you omit the `window`, the measure will accumulate over all time. Otherwise, you can choose from granularities like day, week, quarter, or month, and describe the window using phrases like "7 days" or "1 month."
 
@@ -102,12 +128,14 @@ measures:
 metrics: 
 - name: current_revenue
   description: Current revenue 
+  label: Current Revenue
   type: cumulative 
   type_params: 
     measures: 
       - revenue
 - name: active_subscriptions 
   description: Count of active subscriptions 
+  label: Active Subscriptions
   type: cumulative 
   type_params: 
     measures: 
@@ -122,38 +150,32 @@ metrics:
 
 You can choose to specify a grain to date in your cumulative metric configuration to accumulate a metric from the start of a grain (such as week, month, or year). When using a window, such as a month, MetricFlow will go back one full calendar month. However, grain to date will always start accumulating from the beginning of the grain, regardless of the latest date of data.
 
-For example, let's consider an underlying measure of `total_revenue.`
+For example, let's consider an underlying measure of `order_total.`
 
 ```yaml
-measures: 
-  - name: total_revenue 
-    description: Total revenue (summed) 
-    agg: sum 
-    expr: revenue 
+    measures:
+      - name: order_total
+        agg: sum
 ```
 
 We can compare the difference between a 1-month window and a monthly grain to date. The cumulative metric in a window approach applies a sliding window of 1 month, whereas the grain to date by month resets at the beginning of each month.
 
 ```yaml
-metrics: 
-  name: revenue_monthly_window #For this metric, we use a window of 1 month 
-  description: Monthly revenue using a window of 1 month (think of this as a sliding window of 30 days)
-  type: cumulative 
-  type_params: 
-    measures: 
-      - total_revenue 
-    window: 1 month 
-```
-
-```yaml
-metrics: 
-  name: revenue_monthly_grain_to_date #For this metric, we use a monthly grain to date 
-  description: Monthly revenue using grain to date of 1 month (think of this as a monthly resetting point) 
-  type: cumulative 
-  type_params: 
-    measures: 
-      - total_revenue 
-    grain_to_date: month 
+metrics:
+ - name: cumulative_order_total_l1m #For this metric, we use a window of 1 month 
+    label: Cumulative Order total (L1M)   
+    description: Trailing 1 month cumulative order amount
+    type: cumulative
+    type_params:
+      measure: order_total
+      window: 1 month
+  - name: cumulative_order_total_mtd #For this metric, we use a monthly grain to date 
+    label: Cumulative Order total (MTD)
+    description: The month to date value of all orders
+    type: cumulative
+    type_params:
+      measure: order_total
+      grain_to_date: month
 ```
 
 ### Implementation
diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index 0ca14d1c6f2..64c3e8c33e2 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -6,21 +6,66 @@ sidebar_label: Derived
 tags: [Metrics, Semantic Layer]
 ---
 
-Derived metrics in MetricFlow refer to metrics that are created by defining an expression using other metrics. Derived metrics allow for calculations on top of metrics. For example, you can define a metric called "Net Sales Per User" by using other metrics in the calculation.
+Derived metrics in MetricFlow refer to metrics that are created by defining an expression using other metrics. Derived metrics allow for calculations on top of metrics. This is useful when you want to combine metrics and perform arthematic fucntions ontop of aggregated columns. For example, defining a profit metric. Below, we'll show the full spec for derived metrics as well as an example.
 
+# Derived Metrics Spec
 ```yaml
 metrics:
-  - name: net_sales_per_user
+  - name: the metric name # Required
+    description: the metric description # Optinal
+    type: derived # Required
+    label: The value that will be displayed in downstream tools #Required
+    type_params: # Required
+      expr: the derived expression # Required
+      metrics: # The list of metrics used in the derived metrics # Required
+        - name: the name of the metrics. must refrence a metric you have already defined # Required
+          alias: optional alias for the metric that you can use in the expr # Optional
+          filter: optional filter to apply to the metric # Optional
+          offset_window: set the period for the offset window i.e 1 month. This will retrun the value of the metric one month from the metric time. # Required.
+
+
+
+```
+
+```yaml
+metrics:
+  - name: order_gross_profit
+    description: Gross profit from each order.
+    type: derived
+    label: Order Gross Profit
+    type_params:
+      expr: revenue - cost
+      metrics:
+        - name: order_total
+          alias: revenue
+        - name: order_cost
+          alias: cost
+  - name: food_order_gross_profit
+    label: Food Order Gross Profit  
+    description: "The gross profit for each food order."
     type: derived
     type_params:
-      expr: gross_sales - cogs / active_users
+      expr: revenue - cost
       metrics:
-        - name: gross_sales # these are all metrics (can be a derived metric, meaning building a derived metric with derived metrics)
-        - name: cogs
-        - name: users
-          filter: | # Optional additional constraint
-          {{dimension('filter')}} is_active
-          alias: active_users # Optional alias to use in the expr
+        - name: order_total
+          alias: revenue
+          filter: |
+            {{dimension('is_food_order')}} = True
+        - name: order_cost
+          alias: cost
+          filter: |
+            {{dimension('is_food_order')}} = True
+  - name: order_total_growth_mom
+    description: "Percentage growth of orders total compated to 1 month ago"
+    type: derived
+    label: Order Total Growth % M/M
+    type_params:
+      expr: (order_total - order_total_prev_month)*100/order_total_prev_month
+      metrics: 
+        - name: order_total
+        - name: order_total
+          offset_window: 1 month
+          alias: order_total_prev_month
 ```
 
 ## Derived metric offset
diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 0f258e30d8d..a04c8702280 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -11,7 +11,7 @@ MetricFlow uses a timespine table to construct cumulative metrics. By default, M
 To create this table, you need to create a model in your dbt project called `metricflow_time_spine` and add the following code:
 
 ```sql
-filename: metricflow_time_spine.sql
+--filename: metricflow_time_spine.sql
 {{
     config(
         materialized = 'table',
@@ -38,12 +38,14 @@ final as (
 select * from final
 ```
 ```sql
---- BigQuery supports DATE() instead of TO_DATE(). Use this model if you're using BigQuery
+-- filename: metricflow_time_spine.sql
+-- BigQuery supports DATE() instead of TO_DATE(). Use this model if you're using BigQuery
 {{config(materialized='table')}}
 with days as (
-    {{dbt_utils.date_spine('day'
-    , "DATE(2000,01,01)"
-    , "DATE(2030,01,01)"
+    {{dbt_utils.date_spine(
+        'day',
+        "DATE(2000,01,01)",
+        "DATE(2030,01,01)"
     )
     }}
 ),
diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index d70815f140d..44aa9a52c4e 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -6,35 +6,37 @@ sidebar_label: Ratio
 tags: [Metrics, Semantic Layer]
 ---
 
-Ratio allows you to create a ratio between two measures. You simply specify a numerator and a denominator measure. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric.
+Ratio allows you to create a ratio between two measures. You simply specify a numerator and a denominator measure. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. The full spec for ratio metrics, and examples are belwo:
 
+# Ratio Metric Spec
 ```yaml
-# Ratio Metric
-  metrics:
-    - name: cancellation_rate
-    owners:
-      - support@getdbt.com
-    type: ratio # Ratio metrics create a ratio out of two measures. Define the measures from the semantic model as numerator or denominator
-    type_params:
-      numerator: cancellations_usd
-      denominator: transaction_amount_usd
-        filter: | # add optional constraint string. This applies to both the numerator and denominator
-      {{ dimension('country', entity_path=['customer']) }} = 'MX'
+metrics:
+  - name: the metric name # Required
+    description: the metric description # Optinal
+    type: ratio # Required
+    label: The value that will be displayed in downstream tools #Required
+    type_params: # Required
+      numerator: the measure used for the numerator # Required
+      filter: filter for the numerator# Optional
+      alias: alias for the numerator # Optional
+      denominator: the meausure used for the denominator # Required
+      filter: filter for the denominator # Optional
+      alias: alias for the denominator # Optional
+```
 
-    - name: enterprise_cancellation_rate
-      owners:
-        - support@getdbt.com
-      type: ratio # Ratio metrics create a ratio out of two measures. Define the measures from the semantic model as numerator or denominator
-      type_params:
-        numerator: 
-          name: cancellations_usd
-          filter: tier = 'enterprise' #constraint only applies to the numerator
-        denominator: transaction_amount_usd 
-          filter: | # add optional constraint string. This applies to both the numerator and denominator
-      {{ dimension('country', entity_path=['customer']) }} = 'MX'
+```yaml
+# Ratio Metric
+metrics:
+  - name: food_order_total_pct
+    description: "The food order total as the % of the total order"
+    label: Food Order Total % 
+    type: ratio
+    type_params: 
+      numerator: food_order_total
+      denominator: order_total
   
 ```
-### Different semantic models
+### Ratio metrics using different semantic models
 
 If the numerator and denominator in a ratio metric come from different semantic models, the system will compute their values in subqueries and then join the result set based on common dimensions to calculate the final ratio. Here's an example of the generated SQL for such a ratio metric.
 
diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md
index 0092427699d..68b796b14ce 100644
--- a/website/docs/docs/build/simple.md
+++ b/website/docs/docs/build/simple.md
@@ -6,7 +6,20 @@ sidebar_label: Simple
 tags: [Metrics, Semantic Layer]
 ---
 
-Simple metrics are metrics that directly reference a single measure, without any additional measures involved.
+Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your warhouse, and can be filtered by one or multiple dimensions. The full spec for simple metric as well as an example is below: 
+
+# Simple Metrics Spec
+```yaml
+metrics:
+  - name: the metric name # Required
+    description: the metric description # Optinal
+    type: simple # Required
+    label: The value that will be displayed in downstream tools #Required
+    type_params: # Required
+      measure: the measure you're refrencing # Required
+
+```
+
 <!-- create_metric not supported yet
 :::tip
 
@@ -14,13 +27,21 @@ If you've already defined the measure using the `create_metric: true` parameter,
 :::
 -->
 
+# Simple Metrics Example
 ``` yaml
 metrics: 
-  - name: cancellations
-    type: simple # Pointers to a measure you created in a data source
+  - name: customers
+    description: Count of customers
+    type: simple # Pointers to a measure you created in a semantic model
+    label: Count of customers
+    type_params:
+      measure: customers # The measure you're creating a proxy of.
+  - name: large_orders
+    description: "Order with order values over 20."
+    type: SIMPLE
+    label: Large Orders
     type_params:
-      measure: cancellations_usd # The measure you're creating a proxy of.
-  # For any metric optionally include a filter string which applies a dimensional filter when computing the metric
-      filter: | 
-      {{dimension('value')}} > 100 and {{dimension('acquisition', entity_path=['user'])}}
+      measure: orders
+    filter: | # For any metric you can optionally include a filter on dimension values
+      {{dimension('order_total_dim')}} >= 20
 ```
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 8d12d9715d6..1eec1ab1d68 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,7 +8,7 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
-This getting started page recommends a workflow to help you get started creating your first metrics. Here are the following steps you'll take:
+This getting started page recommends a workflow to help you get started creating your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you would like to use a sandbox project to follow the guide, you can clone the repo here. Otherwise, you can create semantic models and metrics for your own dbt project. 
 
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 
@@ -25,7 +25,6 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
-  * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - To query with dbt Cloud:
   * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
   * Set up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions.
@@ -43,7 +42,7 @@ Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an
 
 Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
 
-- Create or activate your virtual environment. `python -m venv venv`
+- Create or activate your virtual environment. `python -m venv venv` or `source your-venv/bin/activate`
 - `pip install "dbt-metricflow[your_adapter_name]"`
   * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
@@ -51,41 +50,89 @@ Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databric
 
 ## Create a semantic model
 
-This step will guide you through setting up your semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
+This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures). It is reccomended that you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuidling it step by step in this example. If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
 
-1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
+1. Create a new semantic model file i.e orders.yaml. We recomending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project and specify model defaults for now only `default_agg_time_dimension` is supported. 
 
 ```yaml
 semantic_models:
-  - name: transactions
+  #The name of the semantic model.
+  - name: orders
     description: |
-    This table captures every transaction starting July 02, 2014. Each row represents one transaction
-    model: ref('fact_transactions')
+      Model containting order data. The grain of the table is the order id.
+    #The name of the dbt model and schema
+    model: ref('orders')
+    defaults:
+      agg_time_dimension: ordered_at
   ```
 
-2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `transaction_id`, and so on.
+2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `order_id`, and so on.
 
 ```yaml
-entities:
-  - name: transaction
-    type: primary
-    expr: id_transaction
-  - name: customer
-    type: foreign
-    expr: id_customer
+  #Entities. These usually corespond to keys in the table.
+    entities:
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
   ```
 
-3. Define your dimensions and measures. dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
+3. Define your dimensions and measures. Dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
 
 ```yaml
-measures:
-  - name: transaction_amount_usd
-    description: The total USD value of the transaction.
-    agg: sum
-dimensions:
-  - name: is_large
-    type: categorical
-    expr: case when transaction_amount_usd >= 30 then true else false end
+  #Measures. These are the aggregations on the columns in the table.
+    measures:
+          - name: order_total
+            agg: sum
+  #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions:
+      - name: location_name
+        type: categorical
+      - name: ordered_at
+        expr: cast(ordered_at as date)
+        type: time
+        type_params:
+          time_granularity: day
+```
+
+Putting it all together, a complete semantic model configurations based on the order model would look like the example below. You can view this semantic model in the example repo. 
+```yaml
+semantic_models:
+  #The name of the semantic model.
+  - name: orders
+    description: |
+      Model containting order data. The grain of the table is the order id.
+    #The name of the dbt model and schema
+    model: ref('orders')
+    defaults:
+      agg_time_dimension: ordered_at
+    #Entities. These usually corespond to keys in the table.
+    entities:
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
+    #Measures. These are the aggregations on the columns in the table.
+    measures:
+      - name: order_total
+        agg: sum
+    #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions:
+      - name: location_name
+        type: categorical
+      - name: ordered_at
+        expr: cast(ordered_at as date)
+        type: time
+        type_params:
+          time_granularity: day
 ```
 
 :::tip
@@ -103,44 +150,47 @@ group by metric_time_day, country  -- dimensions
 
 ## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). 
+Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](https://docs.getdbt.com/docs/build/metrics-overview) before getting started. 
 
-1. You can define metrics in the same YAML files as your semantic models, or create a new file.
+1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recomend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from if you need to further organize your metrics, for example by data source or business line. 
 
-2. The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
+2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recomend deleteing the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuidling the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 
 ```yaml
 metrics:
-  - name: transaction_amount_usd
+  - name: order_total
+    description: "Sum of orders value"
     type: simple
-    type_params: null
-    measure: transaction_amount_usd
+    label: "Order Total"
+    type_params:
+      measure: order_total
 ```
  
-3. Save your code and make sure you test your metrics before committing them to your repository.
+3. Save your code and validate your configs (we'll do this in the next step of the guide 😀) before committing them to your repository.
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
+## Configure the MetricFlow timespine
+MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article walks thorough how to add the `metricflow_time_spine` model to your project.](/docs/build/metricflow-time-spine) 
+
 ## Test and query metrics
 
 :::note
-For public beta, testing and querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). However, install the [MetricFlow CLI](/docs/build/metricflow-cli) to test and query metrics locally.
+For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. However, you can install the [MetricFlow CLI](/docs/build/metricflow-cli) to test and query metrics locally.
   * dbt Core or Developer plan users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
 :::
 
-Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for more detailed MetricFlow commands.
+Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install the CLI, and a refrence for the CLI commands.
 
-**Test using the CLI:**
+**Query and commit your metrics using the CLI:**
+Metricflow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working in off off the example Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
-3. Run `mf validate-configs` to validate the changes before committing them
-
-**Query using the CLI:**
-
-1. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
-2. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
-3. Commit and merge the code changes that contain the metric definitions.
+3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. i.e `mf query --metrics order_total --group-by metric_time`
+4. Verify that the metric values are what you expect. To futher understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
+5. Run `mf validate-configs` to run validation on your semantic models and metrics. 
+6. Commit and merge the code changes that contain the metric definitions.
 
 ## Run a production job
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index cde72760f8c..08bfe416c4e 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -14,11 +14,13 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 </VersionBlock>
 
-The dbt Semantic Layer creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`). This artifact contains comprehensive information about your Semantic Layer. You can use it as a valuable reference to understand the structure and details of your data models.
+dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`) that MetricFlow needs to build properly, and that the Semantic Layer needs to run metric queries. This artifact contains comprehensive information about your Semantic Layer. It is an internal file, that acts as the integration point with MetricFlow. MetricFlow will use the semantic manifest produced by core to instantiate a data flow plan, and ultimatley generate SQL from Semantic Layer query requests. You can use it as a valuable reference to understand the structure and details of your data models.
 
-Similar to the `manifest.json` file, the Semantic Manifest also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+Similar to the `manifest.json` file, the `semantic_manifest.json` also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+
+# How is it Produced
+The `semantic_manifest.json` is produced whenever your dbt project is parsed. The easiest way to generate the file yourself is to run `dbt parse`. Since `dbt run`, `dbt build`, and `dbt compile` all parse your dbt project, these commands will generate a semantic manifest as well. 
 
-The Semantic Manifest allows you to gain insights into the entities and relationships defined in your Semantic Layer, making it easier to understand your data models and their dependencies.
 
 QUESTIONS:
 - WHAT IS IT PRODUCED BY?
@@ -32,850 +34,48 @@ QUESTIONS:
 {
     "semantic_models": [
         {
-            "name": "orders",
-            "defaults": {
-                "agg_time_dimension": "ds"
-            },
-            "description": "Model containing order data. The grain of the table is the order id.\n",
-            "node_relation": {
-                "alias": "orders",
-                "schema_name": "dbt_sl_test",
-                "database": "ANALYTICS",
-                "relation_name": "ANALYTICS.dbt_sl_test.orders"
-            },
-            "entities": [
-                {
-                    "name": "order",
-                    "description": null,
-                    "type": "primary",
-                    "role": null,
-                    "expr": "order_id",
-                    "metadata": null
-                },
-                {
-                    "name": "location",
-                    "description": null,
-                    "type": "foreign",
-                    "role": null,
-                    "expr": "location_id",
-                    "metadata": null
-                },
-                {
-                    "name": "customer",
-                    "description": null,
-                    "type": "foreign",
-                    "role": null,
-                    "expr": "customer_id",
-                    "metadata": null
-                }
-            ],
-            "measures": [
-                {
-                    "name": "order_total",
-                    "agg": "sum",
-                    "description": null,
-                    "create_metric": true,
-                    "expr": null,
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "food_orders",
-                    "agg": "sum",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "case when is_food_order then order_total else 0 end",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "orders",
-                    "agg": "sum",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "1",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "max_order_value",
-                    "agg": "max",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "order_total",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "min_order_value",
-                    "agg": "min",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "order_total",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "customers",
-                    "agg": "count_distinct",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "customer_id",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "average_order_value",
-                    "agg": "average",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": "order_total",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "order_cost",
-                    "agg": "sum",
-                    "description": null,
-                    "create_metric": false,
-                    "expr": null,
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "locations_with_orders",
-                    "agg": "count_distinct",
-                    "description": null,
-                    "create_metric": true,
-                    "expr": "location_id",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "median_order_value",
-                    "agg": "median",
-                    "description": null,
-                    "create_metric": true,
-                    "expr": "order_total",
-                    "agg_params": null,
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "order_value_p99",
-                    "agg": "percentile",
-                    "description": null,
-                    "create_metric": true,
-                    "expr": "order_total",
-                    "agg_params": {
-                        "percentile": 0.99,
-                        "use_discrete_percentile": true,
-                        "use_approximate_percentile": false
-                    },
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                },
-                {
-                    "name": "discrete_order_value_p99",
-                    "agg": "percentile",
-                    "description": null,
-                    "create_metric": true,
-                    "expr": "order_total",
-                    "agg_params": {
-                        "percentile": 0.99,
-                        "use_discrete_percentile": true,
-                        "use_approximate_percentile": false
-                    },
-                    "metadata": null,
-                    "non_additive_dimension": null,
-                    "agg_time_dimension": "ds"
-                }
-            ],
-            "dimensions": [
-                {
-                    "name": "location_name",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "ds",
-                    "description": null,
-                    "type": "time",
-                    "is_partition": false,
-                    "type_params": {
-                        "time_granularity": "day",
-                        "validity_params": null
-                    },
-                    "expr": "ordered_at",
-                    "metadata": null
-                },
-                {
-                    "name": "is_food_order",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "is_drink_order",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "order_total_dim",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": "order_total",
-                    "metadata": null
-                }
-            ],
-            "metadata": null
-        },
-        {
-            "name": "customers",
-            "defaults": {
-                "agg_time_dimension": "first_ordered_at"
-            },
-            "description": "Customer dimension table. The grain of the table is one row per customer.\n",
+            "name": "semantic model name",
+            "defaults": null,
+            "description": "semantic model description",
             "node_relation": {
-                "alias": "customers",
-                "schema_name": "dbt_sl_test",
-                "database": "ANALYTICS",
-                "relation_name": "ANALYTICS.dbt_sl_test.customers"
-            },
-            "entities": [
-                {
-                    "name": "customer",
-                    "description": null,
-                    "type": "primary",
-                    "role": null,
-                    "expr": "customer_id",
-                    "metadata": null
-                }
-            ],
-            "measures": [],
-            "dimensions": [
-                {
-                    "name": "customer_name",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "count_lifetime_orders",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "lifetime_spend_pre_tax",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "lifetime_spend",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "customer_type",
-                    "description": null,
-                    "type": "categorical",
-                    "is_partition": false,
-                    "type_params": null,
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "first_ordered_at",
-                    "description": null,
-                    "type": "time",
-                    "is_partition": false,
-                    "type_params": {
-                        "time_granularity": "day",
-                        "validity_params": null
-                    },
-                    "expr": null,
-                    "metadata": null
-                },
-                {
-                    "name": "last_ordered_at",
-                    "description": null,
-                    "type": "time",
-                    "is_partition": false,
-                    "type_params": {
-                        "time_granularity": "day",
-                        "validity_params": null
-                    },
-                    "expr": null,
-                    "metadata": null
-                }
-            ],
-            "metadata": null
-        }
-    ],
+                "alias": "model alias",
+                "schema_name": "model schema",
+                "database": "model db",
+                "relation_name": "Fully qualified relation name"
+            },
+            "entities": ["entities in the semantic model"],
+            "measures": ["measures in the semantic model"],
+            "dimensions": ["dimensions in the semantic model" ],
     "metrics": [
         {
-            "name": "east_coast_order_amount",
-            "description": "Sum of orders from the east coast.",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "orders",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": {
-                "where_sql_template": "dimension('location_name') in ('Philadelphia')\""
-            },
-            "metadata": null
-        },
-        {
-            "name": "large_order",
-            "description": "Sum of orders from the east coast.",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "orders",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": {
-                "where_sql_template": "dimension('order_total_dim')\" >= 20"
-            },
-            "metadata": null
-        },
-        {
-            "name": "food_order_amount",
-            "description": "Sum order amount for food orders.",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "food_orders",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "food_order_pct",
-            "description": "The food cost as the % of the total order",
-            "type": "ratio",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": {
-                    "name": "food_orders",
-                    "filter": null,
-                    "alias": null
-                },
-                "denominator": {
-                    "name": "order_total",
-                    "filter": null,
-                    "alias": null
-                },
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "new_customer_order_pct",
-            "description": "The food cost as the % of the total order",
-            "type": "ratio",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": {
-                    "name": "customers",
-                    "filter": {
-                        "where_sql_template": "dimension('customer_type', entity_path=['customer'])\" = 'new'"
-                    },
-                    "alias": "customer_numerator"
-                },
-                "denominator": {
-                    "name": "customers",
-                    "filter": null,
-                    "alias": null
-                },
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "order_gross_profit",
-            "description": "Gross profit from each order.",
-            "type": "derived",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": null,
-                "denominator": null,
-                "expr": "revenue - cost",
-                "window": null,
-                "grain_to_date": null,
-                "metrics": [
-                    {
-                        "name": "food_order_amount",
-                        "filter": null,
-                        "alias": "revenue",
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    },
-                    {
-                        "name": "order_amount",
-                        "filter": null,
-                        "alias": "cost",
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    }
-                ]
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "food_order_pct_cumulative",
-            "description": "Example to show a ratio referenced in a derived ",
-            "type": "derived",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": null,
-                "denominator": null,
-                "expr": "food_order_pct * 100",
-                "window": null,
-                "grain_to_date": null,
-                "metrics": [
-                    {
-                        "name": "food_order_pct",
-                        "filter": null,
-                        "alias": null,
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    }
-                ]
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "cumulative_growth_mom",
-            "description": "Example to show a ratio referenced in a derived ",
-            "type": "derived",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": null,
-                "denominator": null,
-                "expr": "cumulative_order_amount/cumulative_order_amount_lm",
-                "window": null,
-                "grain_to_date": null,
-                "metrics": [
-                    {
-                        "name": "cumulative_order_amount",
-                        "filter": null,
-                        "alias": null,
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    },
-                    {
-                        "name": "cumulative_order_amount",
-                        "filter": null,
-                        "alias": "cumulative_order_amount_lm",
-                        "offset_window": {
-                            "count": 1,
-                            "granularity": "month"
-                        },
-                        "offset_to_grain": null
-                    }
-                ]
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "food_order_gross_profit",
-            "description": "The gross profit for each order",
-            "type": "derived",
-            "type_params": {
-                "measure": null,
-                "measures": [],
-                "numerator": null,
-                "denominator": null,
-                "expr": "revenue - cost",
-                "window": null,
-                "grain_to_date": null,
-                "metrics": [
-                    {
-                        "name": "order_total",
-                        "filter": {
-                            "where_sql_template": "dimension('is_food_order')\" = True"
-                        },
-                        "alias": "revenue",
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    },
-                    {
-                        "name": "order_amount",
-                        "filter": {
-                            "where_sql_template": "dimension('is_food_order')\" = True"
-                        },
-                        "alias": "cost",
-                        "offset_window": null,
-                        "offset_to_grain": null
-                    }
-                ]
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "cumulative_order_amount",
-            "description": "The cumulative value of all orders",
-            "type": "cumulative",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "order_total",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "cumulative_order_amount_l1m",
-            "description": "The cumulative value of all orders",
-            "type": "cumulative",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "order_total",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": {
-                    "count": 1,
-                    "granularity": "month"
-                },
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "cumulative_order_amount_mtd",
-            "description": "The cumulative value of all orders",
-            "type": "cumulative",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "order_total",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": "month",
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "order_count",
-            "description": "The number of orders placed",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "orders",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "max_order_amount",
-            "description": "The highest order value for a given period",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "max_order_value",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "min_order_amount",
-            "description": "The lowest order value for a given period",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "min_order_value",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "customers_with_orders",
-            "description": "Unique count of customers placing orders",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "customers",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "returning_customers_with_orders",
-            "description": "Unique count of customers placing orders",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "customers",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": {
-                "where_sql_template": "dimension('customer_type', entity_path=['customer']) \" = 'returning'"
-            },
-            "metadata": null
-        },
-        {
-            "name": "new_customer_with_orders",
-            "description": "Unique count of customers placing orders",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "customers",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": {
-                "where_sql_template": "dimension('customer_type', entity_path=['customer']) \" = 'new'"
-            },
-            "metadata": null
-        },
-        {
-            "name": "average_order_amount",
-            "description": "The average order value",
-            "type": "simple",
-            "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "average_order_value",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
-                "numerator": null,
-                "denominator": null,
-                "expr": null,
-                "window": null,
-                "grain_to_date": null,
-                "metrics": []
-            },
-            "filter": null,
-            "metadata": null
-        },
-        {
-            "name": "order_amount",
-            "description": "The cost of fulfilling each order",
-            "type": "simple",
+            "name": "name of the metric",
+            "description": "metric description",
+            "type": "metric type",
             "type_params": {
-                "measure": null,
-                "measures": [
-                    {
-                        "name": "order_cost",
-                        "filter": null,
-                        "alias": null
-                    }
-                ],
+                "measure": {"measures for the metric"},
                 "numerator": null,
                 "denominator": null,
                 "expr": null,
                 "window": null,
                 "grain_to_date": null,
-                "metrics": []
+                "metrics": ["metrics used in defining the metric. this is used in derived metrics"],
+                "input_measures": []
             },
             "filter": null,
             "metadata": null
         }
     ],
-    "interfaces_version": "0.1.0.dev3"
+    "project_configuration": {
+        "time_spine_table_configurations": [
+            {
+                "location": "fully qualified table name for timespine",
+                "column_name": "date column",
+                "grain": "day"
+            }
+        ],
+        "metadata": null,
+        "dsi_package_version": {}
+    }
 }
 ```
 </details>

From 377606e3fcd8deccdbeef4a27a2b229d0558b712 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Mon, 24 Jul 2023 19:23:08 -0700
Subject: [PATCH 272/703] update typos

---
 website/docs/docs/build/sl-getting-started.md | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 1eec1ab1d68..de60034bc56 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,7 +8,7 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
-This getting started page recommends a workflow to help you get started creating your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you would like to use a sandbox project to follow the guide, you can clone the repo here. Otherwise, you can create semantic models and metrics for your own dbt project. 
+This getting started page recommends a workflow to help you get started creating your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you would like to use a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
 
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 
@@ -46,13 +46,13 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 - `pip install "dbt-metricflow[your_adapter_name]"`
   * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
-Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). 
+Currently, the supported adapters for querying locally are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). If you use an adpater other then Snowflake you can still create metrics, but you will need to test querying them through the Semantic Layer.
 
 ## Create a semantic model
 
 This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures). It is reccomended that you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuidling it step by step in this example. If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
 
-1. Create a new semantic model file i.e orders.yaml. We recomending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project and specify model defaults for now only `default_agg_time_dimension` is supported. 
+1. Create a new semantic model file i.e orders.yaml. We recomending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project and specify model defaults. For now `default_agg_time_dimension` is the only supported default. 
 
 ```yaml
 semantic_models:
@@ -88,7 +88,7 @@ semantic_models:
     measures:
           - name: order_total
             agg: sum
-  #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
+  #Dimensions, either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
     dimensions:
       - name: location_name
         type: categorical
@@ -99,7 +99,7 @@ semantic_models:
           time_granularity: day
 ```
 
-Putting it all together, a complete semantic model configurations based on the order model would look like the example below. You can view this semantic model in the example repo. 
+Putting it all together, a complete semantic model configurations based on the order model would look like the example below.
 ```yaml
 semantic_models:
   #The name of the semantic model.
@@ -152,7 +152,7 @@ group by metric_time_day, country  -- dimensions
 
 Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](https://docs.getdbt.com/docs/build/metrics-overview) before getting started. 
 
-1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recomend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from if you need to further organize your metrics, for example by data source or business line. 
+1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recomend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example by data source or business line. 
 
 2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recomend deleteing the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuidling the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 
@@ -170,7 +170,7 @@ metrics:
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
-## Configure the MetricFlow timespine
+## Configure the MetricFlow time spine model
 MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article walks thorough how to add the `metricflow_time_spine` model to your project.](/docs/build/metricflow-time-spine) 
 
 ## Test and query metrics
@@ -183,7 +183,8 @@ For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Comi
 Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install the CLI, and a refrence for the CLI commands.
 
 **Query and commit your metrics using the CLI:**
-Metricflow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working in off off the example Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
+
+Metricflow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.

From ee61767880b9885004f65af574e14d0ff9f5423a Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Mon, 24 Jul 2023 20:41:16 -0700
Subject: [PATCH 273/703] update migration guide

---
 website/docs/guides/migration/sl-migration.md | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index f9b34ebd35d..5868f358a2e 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -48,11 +48,11 @@ To help ensure a smooth transition, you can use our [custom-built migration tool
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
+Please be aware that you will need to upgrade your development environment to dbt>1.6 if you're developing Metrics and Semantic Models in the IDE. This will upgrade the version for other analysts on your team who are using the IDE for that project, and may cause errors if they still have metrics defined using the spec in dbt 1.5 or below.
 
 #### Test your metrics
 
-Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools:
+Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools. Note this is only available to users developing metrics locally:
 
 1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
 2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
@@ -60,6 +60,8 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
+If you are migrating using the IDE in cloud, we will run semantic validations whenever we parse or compile your project. This will ensure that your metrics and semantic models are configured correctly. You will need to test querying metrics values in downstream tools. 
+
 Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new Semantic Layer.
 
 ### Step 2: Switch to the new Semantic Layer
@@ -128,7 +130,7 @@ To help ensure a smooth transition, you can use our [custom-built migration tool
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-THIS IS INCORRECT BUT TO BE CONFIRMED- Please be aware that developing Metrics and Semantic Models in the IDE is not supported in the Beta release.
+Please be aware that you will need to upgrade your development environment to dbt>1.6 if you're developing Metrics and Semantic Models in the IDE. This will upgrade the version for other analysts on your team who are using the IDE, and may cause errors if they still have metrics defined using the spec in dbt 1.5 or below.
 
 #### Test your metrics
 
@@ -140,6 +142,8 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
+If you are migrating using the IDE in cloud, we will run semantic validations whenever we parse or compile your project. This will ensure that your metrics and semantic models are configured correctly. You will need to test querying metrics values in downstream tools. 
+
 Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. 
 
 ### Step 3: Update connection in downstream integrations

From 37c25246be6dfad26932f4a9fd7f09dc24fcbabe Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:09:38 +0100
Subject: [PATCH 274/703] Update
 website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index ac6317a9c9d..ac5e9f1d542 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -18,7 +18,7 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 Discover the diverse range of data applications that seamlessly integrate with the powerful dbt Semantic Layer, powered by MetricFlow &mdash; from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-<span>Query dbt metrics via external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+<span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
 

From 8c896ba86a5396f4cdafb48c2fc27531c3050af8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:09:56 +0100
Subject: [PATCH 275/703] Update
 website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index ac5e9f1d542..271397a00b9 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -29,7 +29,7 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
   * Supports a generic JDBC driver option (such as DataGrip) or 
   * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
 
-For information on the partner integrations, their documentation, and more &mdash; refer to the [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) page.
+Refer to [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) for information on the partner integrations, their documentation, and more. 
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 

From fa8fd42d2c1f8bbf5975a78b05f4cad6e59efb44 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:10:07 +0100
Subject: [PATCH 276/703] Update
 website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 271397a00b9..00ab879af31 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -31,7 +31,7 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
 Refer to [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) for information on the partner integrations, their documentation, and more. 
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="The universal dbt Semantic Layer connecting to integration tools."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="dbt Semantic Layer supports many different tool integrations."/>
 
 
 ## Related docs

From 8a5ab7afc40c6770483817eec6863c8dfd3116b6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:10:15 +0100
Subject: [PATCH 277/703] Update
 website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 00ab879af31..d976262f563 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -56,6 +56,6 @@ For information on the partner integrations, their documentation, and more &mdas
 
 ## Related docs
 
-- [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) to learn more about the dbt Semantic Layer.
+- [About the dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl)
 
 </VersionBlock>

From 6b58e55ec5dff94b4d281904241aed126ec5d7db Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:10:46 +0100
Subject: [PATCH 278/703] Update
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 89429c18d55..34ffcbf8b3a 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -16,7 +16,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads.
 
-The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) allows users to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help the organizations that uses your product make more efficient and trustworthy decisions with their data. It also helps avoid duplicative coding, optimizes development workflow, ensures data governance, and guarantee consistency for data consumers.  
+The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) allows you to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help organizations that use your product make more efficient and trustworthy decisions with their data. It also helps you to avoid duplicative coding, optimize development workflow, ensure data governance, and guarantee consistency for data consumers.  
 
 You can use the dbt Semantic Layer for a variety of tools and applications of data. Here are some common use cases:
 

From 691a9f28997e5bda170640a2ee2bdd8e2691c0ef Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:11:03 +0100
Subject: [PATCH 279/703] Update
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 34ffcbf8b3a..a05f7fc5d16 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -18,7 +18,7 @@ The rapid growth of different tools in the modern data stack has helped data pro
 
 The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) allows you to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help organizations that use your product make more efficient and trustworthy decisions with their data. It also helps you to avoid duplicative coding, optimize development workflow, ensure data governance, and guarantee consistency for data consumers.  
 
-You can use the dbt Semantic Layer for a variety of tools and applications of data. Here are some common use cases:
+You can use the dbt Semantic Layer for a variety of tools and applications of data. Some common use cases are:
 
 * Business intelligence (BI), reporting, and analytics
 * Data quality and monitoring

From 3428c7dd0e31ab8548cbca29f8730f655882b847 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 25 Jul 2023 11:23:46 +0100
Subject: [PATCH 280/703] tweaks

---
 website/docs/docs/dbt-cloud-apis/sl-api-overview.md | 2 --
 website/docs/docs/dbt-cloud-apis/sl-graphql.md      | 4 ++--
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md         | 6 ++++--
 website/static/_redirects                           | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 89429c18d55..8742cd35f8d 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -63,6 +63,4 @@ commenting out until it's available
 
 </div>
 
-## Authentication
 
-dbt Cloud authorizes requests to the dbt Semantic Layer API. You need to provide an environment ID, host, and [service account tokens](/docs/dbt-cloud-apis/service-tokens).
diff --git a/website/docs/docs/dbt-cloud-apis/sl-graphql.md b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
index e834346d016..43c43b68441 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-graphql.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-graphql.md
@@ -19,6 +19,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 With GraphQL, users can request specific data using a single query, reducing the need for many server round trips. This improves performance and minimizes network overhead.
 
 GraphQL has several advantages, such as self-documenting, having a strong typing system, supporting versioning and evolution, enabling rapid development, and having a robust ecosystem. These features make GraphQL a powerful choice for APIs that prioritize flexibility, performance, and developer productivity.
+
 ## dbt Semantic Layer GraphQL API
 
 The dbt Semantic Layer GraphQL API allows you to explore and query metrics and dimensions. Due to it's self-documenting nature, you can explore the calls conveniently through the [schema explorer](https://cloud.getdbt.com/semantic-layer/api/graphql). 
@@ -33,7 +34,7 @@ Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/q
 
 ### Authentication 
 
-Authentication uses a dbt Cloud Service token passed through a header as follows. To explore the schema, you can enter this information in the "header" section.
+Authentication uses a dbt Cloud [service account tokens](/docs/dbt-cloud-apis/service-tokens) passed through a header as follows. To explore the schema, you can enter this information in the "header" section.
 
 ```
 {"Authorization": "Bearer <SERVICE TOKEN>"}
@@ -41,7 +42,6 @@ Authentication uses a dbt Cloud Service token passed through a header as follows
 
 Each GQL request also comes with a dbt Cloud environmentId. The API uses both the service token in the header and environmentId for authentication.
 
-
 ### Metric metadata calls
 
 Use the following example calls to provide you with an idea of the types of commands you can use:
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 88bc2d9818c..d383ae3f12c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -26,13 +26,16 @@ A JDBC driver is a software component enabling a Java application to interact wi
 
 dbt Labs partners can use the JDBC API to build integrations in their tools with the dbt Semantic Layer
 
-
 ## Using the JDBC API
 
 If you are a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
 
 Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
+## Authentication
+
+dbt Cloud authorizes requests to the dbt Semantic Layer API. You need to provide an environment ID, host, and [service account tokens](/docs/dbt-cloud-apis/service-tokens).
+
 ## Connection parameters
 
 The JDBC connection requires a few different connection parameters. 
@@ -52,7 +55,6 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 
 *Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable at the end or beginning of the JDBC URL `&disableCertificateVerification=true`.
 
-
 ## Querying the API for metric metadata
 
 The Semantic Layer JDBC API has built-in metadata calls which can provide a user with information about their metrics and dimensions. Here are some metadata commands and examples:
diff --git a/website/static/_redirects b/website/static/_redirects
index 1b77d12b841..f72df13a7f0 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -2,8 +2,8 @@
 
 https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
 https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
+
 /guides/dbt-ecosystem/sl-partner-integration-guide  /docs/use-dbt-semantic-layer/avail-sl-integrations 301
-/docs/build/sl-getting-started  /docs/use-dbt-semantic-layer/quickstart-semantic-layer 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
 /docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
 /docs/use-dbt-semantic-layer/quickstart-semantic-layer /docs/use-dbt-semantic-layer/quickstart-sl 301

From 7a6bcf64500d2b5356c6972999400ab10b3b3a56 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:24:08 +0100
Subject: [PATCH 281/703] Update
 website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index d976262f563..8f955986e07 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -16,7 +16,7 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 
 
-Discover the diverse range of data applications that seamlessly integrate with the powerful dbt Semantic Layer, powered by MetricFlow &mdash; from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
+There are a number of data applications that seamlessly integrate with the dbt Semantic Layer, powered by MetricFlow, from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
 <span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 

From 168b91594710620ab43aeb6b898f2a9080b9044b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 25 Jul 2023 13:02:44 +0100
Subject: [PATCH 282/703] fix typos and language

---
 website/docs/docs/build/about-metricflow.md   | 74 ++++++++-----------
 website/docs/docs/build/cumulative-metrics.md | 27 ++++---
 website/docs/docs/build/derived-metrics.md    | 21 +++---
 website/docs/docs/build/ratio-metrics.md      | 19 +++--
 website/docs/docs/build/simple.md             | 13 ++--
 website/docs/docs/build/sl-getting-started.md | 43 ++++++-----
 .../docs/docs/dbt-cloud-apis/sl-manifest.md   |  9 ++-
 website/docs/guides/migration/sl-migration.md | 19 ++---
 8 files changed, 118 insertions(+), 107 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index eabf3b43fc2..38b64f492cb 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -55,7 +55,6 @@ For a semantic model, there are three main pieces of metadata:
 * [Dimensions](/docs/build/dimensions) &mdash; These are the ways you want to group or slice/dice your metrics.
 * [Measures](/docs/build/measures) &mdash; The aggregation functions that give you a numeric result and can be used to create your metrics.
 
-
 ### Metrics 
 
 Metrics, which is a key concept, are functions that combine measures, constraints, or other mathematical functions to define new quantitative indicators. MetricFlow uses measures and various aggregation types, such as average, sum, and count distinct, to create metrics.  Dimensions add context to metrics and without them, a metric is simply a number for all time. You can define metrics in the same YAML files as your semantic models, or create a new file.
@@ -66,11 +65,12 @@ MetricFlow supports different metric types:
 - [Derived](/docs/build/derived) &mdash; An expression of other metrics, which allows you to do calculations on top of metrics.
 - [Ratio](/docs/build/ratio) &mdash; Create a ratio out of two measures, like revenue per customer.
 - [Simple](/docs/build/simple) &mdash; Metrics that refer directly to one measure. 
+
 ## Use case
 
 In the upcoming sections, we'll show how data practitioners currently calculate metrics and compare it to how MetricFlow makes defining metrics easier and more flexible. 
 
-The following example data is based off the Jaffle Shop repo. You can view the complete [dbt project here](https://github.com/dbt-labs/jaffle-sl-template). The tables we're using in our example model are:
+The following example data is based off the Jaffle Shop repo. You can view the complete [dbt project](https://github.com/dbt-labs/jaffle-sl-template). The tables we're using in our example model are:
 
 - `orders` is a production data platform export that has been cleaned up and organized for analytical consumption
 - `customers` is a partially denormalized table in this case with a column derived from the orders table through some upstream process
@@ -80,7 +80,7 @@ The following example data is based off the Jaffle Shop repo. You can view the c
 To make this more concrete, consider the metric `order_total`, which is defined using the SQL expression:
 
 `select sum(order_total) as order_total from orders` 
-This expression caclulates the revenue from each order by summing the order_total column in the orders table. In a business setting, the metric order_total is often calcualted according to different categoris, such as"
+This expression calculates the revenue from each order by summing the order_total column in the orders table. In a business setting, the metric order_total is often calculated according to different categories, such as"
 - Time, for example `date_trunc(ordered_at, 'day')`
 - Order Type, using `is_food_order` dimension from the `orders` table.
 
@@ -119,49 +119,43 @@ In the following three example tabs, use MetricFlow to define a semantic model t
 <Tabs>
 <TabItem value="example1" label="Revenue example">
 
-In this example, a measure named `order_total` is defined based on the order_total column in the `orders` table. The time dimension `metric_time` provides daily granularity and can be aggregated to weekly or monthly time periods. Additionally, a categorical dimension called `is_new_customer` is specified in the `customers` semantic model.
+In this example, a measure named `order_total` is defined based on the order_total column in the `orders` table. 
+
+The time dimension `metric_time` provides daily granularity and can be aggregated to weekly or monthly time periods. Additionally, a categorical dimension called `is_new_customer` is specified in the `customers` semantic model.
 
 
 ```yaml
 semantic_models:
-  #The name of the semantic model.
-  - name: orders
+  - name: orders    #The name of the semantic model
     description: |
-      Model containting order data. The grain of the table is the order id.
-    
-    #The name of the dbt model and schema
-    model: ref('orders')
+      Model containing order data. The grain of the table is the order id.
+    model: ref('orders') #The name of the dbt model and schema
     defaults:
       agg_time_dimension: metric_time
-    #Entities. These usually corespond to keys in the table.table.
-    entities:
+    entities: #Entities. These usually correspond to keys in the table.table.
       - name: order_id
         type: primary
       - name: customer
         type: foreign
         expr: customer_id
-
-    #Measures. These are the aggregations on the columns in the table.
-    measures:
+    measures:   #Measures. These are the aggregations on the columns in the table.
       - name: order_total
         agg: sum
-   #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
-    dimensions:
+    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
       - name: metric_time
         expr: cast(ordered_at as date)
         type: time
         type_params:
           time_granularity: day
- - name: customers
-    defaults:
-      agg_time_dimension: first_ordered_at
-    description: |
-      Customer dimension table. The grain of the table is one row per customer.
-    #The name of the dbt model and schema
-    model: ref('customers')
-    #Entities. These usually corespond to keys in the table.
-    entities:
-      - name: customer
+      - name: customers
+        defaults: null
+        agg_time_dimension: first_ordered_at
+        description: >
+          Customer dimension table. The grain of the table is one row per
+          customer.
+        model: ref('customers') # The name of the dbt model and schema
+    entities: #Entities. These usually correspond to keys in the table.
+      - name: customer 
         type: primary
         expr: customer_id
     dimensions:
@@ -169,7 +163,7 @@ semantic_models:
         type: categorical
         expr: case when first_ordered_at is not null then true else false end
       - name: first_ordered_at
-        type: time 
+        type: time
         type_params:
           time_granularity: day
 
@@ -178,39 +172,33 @@ semantic_models:
 </TabItem>
 <TabItem value="example2" label="More dimensions example">
 
-Similarly, you could then add additonal dimensions like `is_food_order` to your semantic models to incorporate even more dimensions to slice and dice your revenue order_total. 
+Similarly, you could then add additional dimensions like `is_food_order` to your semantic models to incorporate even more dimensions to slice and dice your revenue order_total. 
 
 ```yaml
 semantic_models:
-- name: orders
+  - name: orders
     description: |
-      Model containting order data. The grain of the table is the order id.
-    
-    #The name of the dbt model and schema
-    model: ref('orders')
+      Model containing order data. The grain of the table is the order id.
+    model: ref('orders')  #The name of the dbt model and schema
     defaults:
       agg_time_dimension: metric_time
-    #Entities. These usually corespond to keys in the table.table.
-    entities:
+    entities: #Entities. These usually correspond to keys in the table.table.
       - name: order_id
         type: primary
       - name: customer
         type: foreign
         expr: customer_id
-
-    #Measures. These are the aggregations on the columns in the table.
-    measures:
+    measures: #Measures. These are the aggregations on the columns in the table.
       - name: order_total
         agg: sum
-   #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
-    dimensions:
+    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
       - name: metric_time
         expr: cast(ordered_at as date)
         type: time
         type_params:
           time_granularity: day
-       - name: is_food_order
-         type: categorical
+      - name: is_food_order
+        type: categorical
 ```
 </TabItem>
 <TabItem value="example3" label="Advanced example">
diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index 518b538300a..aebe3fd7ecc 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -6,19 +6,22 @@ sidebar_label: Cumulative
 tags: [Metrics, Semantic Layer]
 ---
 
-Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. This metric is common for calculating things like weekly active users, or month to date revenue. The full metrics spec, as well as examples of cumulative metric are given below.
+Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. 
+
+This metric is common for calculating things like weekly active users, or month to date revenue. The following displays the full metrics spec, along with an example:
+
 
-# Cumulative Metrics Spec
 ```yaml
 metrics:
   - name: the metric name # Required
-    description: the metric description # Optinal
+    description: the metric description # Optional
     type: simple # Required
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
-      measure: the measure you are refrencing # Required
-      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Can not be used with window
-      grain_to_date: sets the accumulation grain i.e month will accumulated data for one month, then restart at the beggining of the next. # Optional. Can not be used with grain_to_date. 
+      measure: the measure you are referencing # Required
+      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Cannot be used with window
+      grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next. 
+      # Optional. Can not be used with grain_to_date. 
 
 
 ```
@@ -29,10 +32,12 @@ You will need to create the [time spine model](/docs/build/metricflow-time-spine
 
 :::
 
-# Cumulative Metrics Example
+## Cumulative metrics example
+
+Cumulative metrics measure data over a given window and consider the window infinite when no window parameter is passed, accumulating the data over all time.
 
 ```yaml
-# Cumulative metrics aggregate a measure over a given window. The window is considered infinite if no window parameter is passed (accumulate the measure over all time)
+
 metrics:
   - name: cumulative_order_total
     label: Cumulative Order total (All Time)    
@@ -79,13 +84,11 @@ We can write a cumulative metric `weekly_customers` as such:
 
 ``` yaml
 metrics: 
-  - name: weekly_customers
-  # Define the measure and the window.
+  - name: weekly_customers # Define the measure and the window.
   type: cumulative
   type_params:
     measure: customers
-    # Setting the window to 7 days since we want to track weekly active 
-    window: 7 days
+    window: 7 days # Setting the window to 7 days since we want to track weekly active 
 ```
 
 From the sample YAML above, note the following: 
diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index 64c3e8c33e2..010c86c2c33 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -6,27 +6,27 @@ sidebar_label: Derived
 tags: [Metrics, Semantic Layer]
 ---
 
-Derived metrics in MetricFlow refer to metrics that are created by defining an expression using other metrics. Derived metrics allow for calculations on top of metrics. This is useful when you want to combine metrics and perform arthematic fucntions ontop of aggregated columns. For example, defining a profit metric. Below, we'll show the full spec for derived metrics as well as an example.
+In MetricFlow, derived metrics are metrics created by defining an expression using other metrics. They allow performing calculations on top of existing metrics. This proves useful for combining metrics and applying arithmetic functions to aggregated columns, such as, you can define a profit metric. 
+
+The following example presents the complete specification for derived metrics, along with an example.
 
-# Derived Metrics Spec
 ```yaml
 metrics:
   - name: the metric name # Required
-    description: the metric description # Optinal
+    description: the metric description # Optional
     type: derived # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
       expr: the derived expression # Required
       metrics: # The list of metrics used in the derived metrics # Required
-        - name: the name of the metrics. must refrence a metric you have already defined # Required
+        - name: the name of the metrics. must reference a metric you have already defined # Required
           alias: optional alias for the metric that you can use in the expr # Optional
           filter: optional filter to apply to the metric # Optional
-          offset_window: set the period for the offset window i.e 1 month. This will retrun the value of the metric one month from the metric time. # Required.
-
-
-
+          offset_window: set the period for the offset window i.e 1 month. This will return the value of the metric one month from the metric time. # Required
 ```
 
+## Derived metrics example
+
 ```yaml
 metrics:
   - name: order_gross_profit
@@ -56,7 +56,7 @@ metrics:
           filter: |
             {{dimension('is_food_order')}} = True
   - name: order_total_growth_mom
-    description: "Percentage growth of orders total compated to 1 month ago"
+    description: "Percentage growth of orders total completed to 1 month ago"
     type: derived
     label: Order Total Growth % M/M
     type_params:
@@ -70,7 +70,7 @@ metrics:
 
 ## Derived metric offset
 
-You may want to use an offset value of a metric in the definition of a derived metric. For example, if you define retention rate as (active customers at the end of the month/active customers at the beginning of the month)-1 you can model this using a derived metric with an offset. 
+You may want to use an offset value of a metric in the definition of a derived metric. For example, you can model the retention rate by using a derived metric with an offset, which involves calculating (active customers at the end of the month/active customers at the beginning of the month) - 1.
 
 ```yaml
 metrics:
@@ -83,3 +83,4 @@ metrics:
       - name: active_customers
         offset_window: 1 month
         alias: active_customers_t1m
+```
diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index 44aa9a52c4e..95ce22db5b6 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -6,24 +6,27 @@ sidebar_label: Ratio
 tags: [Metrics, Semantic Layer]
 ---
 
-Ratio allows you to create a ratio between two measures. You simply specify a numerator and a denominator measure. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. The full spec for ratio metrics, and examples are belwo:
+Ratio allows you to create a ratio between two measures. You simply specify a numerator and a denominator measure. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. 
+
+The following displays the full spec for ratio metrics, along with an example:
 
-# Ratio Metric Spec
 ```yaml
 metrics:
   - name: the metric name # Required
-    description: the metric description # Optinal
+    description: the metric description # Optional
     type: ratio # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
       numerator: the measure used for the numerator # Required
       filter: filter for the numerator# Optional
       alias: alias for the numerator # Optional
-      denominator: the meausure used for the denominator # Required
+      denominator: the measure used for the denominator # Required
       filter: filter for the denominator # Optional
       alias: alias for the denominator # Optional
 ```
 
+## Ratio metrics example
+
 ```yaml
 # Ratio Metric
 metrics:
@@ -36,12 +39,12 @@ metrics:
       denominator: order_total
   
 ```
-### Ratio metrics using different semantic models
+## Ratio metrics using different semantic models
 
-If the numerator and denominator in a ratio metric come from different semantic models, the system will compute their values in subqueries and then join the result set based on common dimensions to calculate the final ratio. Here's an example of the generated SQL for such a ratio metric.
+The system will simplify and turn the numerator and denominator in a ratio metric from different semantic models by computing their values in sub-queries. It will then join the result set based on common dimensions to calculate the final ratio. Here's an example of the SQL generated for such a ratio metric.
 
 
-```SQL
+```sql
 select
   subq_15577.metric_time as metric_time
   , cast(subq_15577.mql_queries_created_test as double) / cast(nullif(subq_15582.distinct_query_users, 0) as double) as mql_queries_per_active_user
@@ -85,7 +88,7 @@ on
   )
 ```
 
-### Add filter
+## Add filter
 
 Users can define constraints on input measures for a metric by applying a filter directly to the measure, like so:
 
diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md
index 68b796b14ce..29f70bb89e7 100644
--- a/website/docs/docs/build/simple.md
+++ b/website/docs/docs/build/simple.md
@@ -6,17 +6,18 @@ sidebar_label: Simple
 tags: [Metrics, Semantic Layer]
 ---
 
-Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your warhouse, and can be filtered by one or multiple dimensions. The full spec for simple metric as well as an example is below: 
+Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your data platform, and can be filtered by one or multiple dimensions.
+
+The following displays the full spec for ratio metrics, along with an example:
 
-# Simple Metrics Spec
 ```yaml
 metrics:
   - name: the metric name # Required
-    description: the metric description # Optinal
+    description: the metric description # Optional
     type: simple # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
-      measure: the measure you're refrencing # Required
+      measure: the measure you're referencing # Required
 
 ```
 
@@ -27,7 +28,9 @@ If you've already defined the measure using the `create_metric: true` parameter,
 :::
 -->
 
-# Simple Metrics Example
+## Simple metrics example
+
+```yaml
 ``` yaml
 metrics: 
   - name: customers
diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index de60034bc56..52979e5c077 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,7 +8,7 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
-This getting started page recommends a workflow to help you get started creating your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you would like to use a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
+This getting started page recommends a workflow to help you create your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you prefer using a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
 
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 
@@ -46,20 +46,26 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 - `pip install "dbt-metricflow[your_adapter_name]"`
   * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
-Currently, the supported adapters for querying locally are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). If you use an adpater other then Snowflake you can still create metrics, but you will need to test querying them through the Semantic Layer.
+Currently, Snowflake and Postgres are the supported adapters for querying locally (BigQuery, Databricks, and Redshift will be available soon). If you use an adapter other than Snowflake, you can still create metrics, but you will need to test querying them through the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl).
 
 ## Create a semantic model
 
-This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures). It is reccomended that you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuidling it step by step in this example. If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
+This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
 
-1. Create a new semantic model file i.e orders.yaml. We recomending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project and specify model defaults. For now `default_agg_time_dimension` is the only supported default. 
+We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuilding it step by step in this example. 
+
+If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
+
+1. Create a new semantic model file, such as `orders.yaml`. 
+
+We recommending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now `default_agg_time_dimension` is the only supported default. 
 
 ```yaml
 semantic_models:
   #The name of the semantic model.
   - name: orders
     description: |
-      Model containting order data. The grain of the table is the order id.
+      Model containing order data. The grain of the table is the order id.
     #The name of the dbt model and schema
     model: ref('orders')
     defaults:
@@ -69,7 +75,7 @@ semantic_models:
 2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `order_id`, and so on.
 
 ```yaml
-  #Entities. These usually corespond to keys in the table.
+  #Entities. These usually correspond to keys in the table.
     entities:
       - name: order_id
         type: primary
@@ -88,7 +94,7 @@ semantic_models:
     measures:
           - name: order_total
             agg: sum
-  #Dimensions, either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
+  #Dimensions, either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
     dimensions:
       - name: location_name
         type: categorical
@@ -99,18 +105,19 @@ semantic_models:
           time_granularity: day
 ```
 
-Putting it all together, a complete semantic model configurations based on the order model would look like the example below.
+Putting it all together, a complete semantic model configurations based on the order model would look like the following example:
+
 ```yaml
 semantic_models:
   #The name of the semantic model.
   - name: orders
     description: |
-      Model containting order data. The grain of the table is the order id.
+      Model containing order data. The grain of the table is the order id.
     #The name of the dbt model and schema
     model: ref('orders')
     defaults:
       agg_time_dimension: ordered_at
-    #Entities. These usually corespond to keys in the table.
+    #Entities. These usually correspond to keys in the table.
     entities:
       - name: order_id
         type: primary
@@ -124,7 +131,7 @@ semantic_models:
     measures:
       - name: order_total
         agg: sum
-    #Dimensions,either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.
+    #Dimensions, either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
     dimensions:
       - name: location_name
         type: categorical
@@ -137,6 +144,7 @@ semantic_models:
 
 :::tip
 If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
+
 ```sql
 select
   metric_time_day,  -- time
@@ -152,9 +160,9 @@ group by metric_time_day, country  -- dimensions
 
 Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](https://docs.getdbt.com/docs/build/metrics-overview) before getting started. 
 
-1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recomend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example by data source or business line. 
+1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recommend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example by data source or business line. 
 
-2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recomend deleteing the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuidling the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
+2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 
 ```yaml
 metrics:
@@ -171,7 +179,8 @@ metrics:
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 
 ## Configure the MetricFlow time spine model
-MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article walks thorough how to add the `metricflow_time_spine` model to your project.](/docs/build/metricflow-time-spine) 
+
+MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article](/docs/build/metricflow-time-spine) walks thorough how to add the `metricflow_time_spine` model to your project.
 
 ## Test and query metrics
 
@@ -180,16 +189,16 @@ For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Comi
   * dbt Core or Developer plan users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
 :::
 
-Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install the CLI, and a refrence for the CLI commands.
+Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install the CLI and a reference for the CLI commands.
 
 **Query and commit your metrics using the CLI:**
 
-Metricflow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
+MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
 3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. i.e `mf query --metrics order_total --group-by metric_time`
-4. Verify that the metric values are what you expect. To futher understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
+4. Verify that the metric values are what you expect. To further understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
 5. Run `mf validate-configs` to run validation on your semantic models and metrics. 
 6. Commit and merge the code changes that contain the metric definitions.
 
diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 08bfe416c4e..6d388c01e44 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -14,11 +14,14 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 </VersionBlock>
 
-dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`) that MetricFlow needs to build properly, and that the Semantic Layer needs to run metric queries. This artifact contains comprehensive information about your Semantic Layer. It is an internal file, that acts as the integration point with MetricFlow. MetricFlow will use the semantic manifest produced by core to instantiate a data flow plan, and ultimatley generate SQL from Semantic Layer query requests. You can use it as a valuable reference to understand the structure and details of your data models.
+dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`), which MetricFlow requires to build and run metric queries properly for the Semantic Layer. This artifact contains comprehensive information about your Semantic Layer. It is an internal file, that acts as the integration point with MetricFlow. 
 
-Similar to the `manifest.json` file, the `semantic_manifest.json` also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+By using the semantic manifest produced by dbt Core, MetricFlow will instantiate a data flow plan and generate SQL from Semantic Layer query requests. You can it as a valuable reference to understand the structure and details of your data models.
+
+Similar to the [`manifest.json` file](/reference/artifacts/manifest-json), the `semantic_manifest.json` also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
+
+## How it's produced 
 
-# How is it Produced
 The `semantic_manifest.json` is produced whenever your dbt project is parsed. The easiest way to generate the file yourself is to run `dbt parse`. Since `dbt run`, `dbt build`, and `dbt compile` all parse your dbt project, these commands will generate a semantic manifest as well. 
 
 
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 5868f358a2e..43facab9d50 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -6,7 +6,7 @@ description: "This migration guide will help you migrate from the legacy dbt Sem
 tags: [Semantic Layer]
 ---
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and supported on dbt v1.6 or higher. It's available to all dbt Cloud plans (Developer, Team, and Enterprise) and introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and supported on dbt v1.6 or higher. It's available to all dbt Cloud plans (Developer, Team, and Enterprise) and introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
 
 This guide is for users who have the legacy dbt Semantic Layer set up and would like to migrate to the new and revamped  [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). The legacy dbt Semantic Layer is supported on dbt v1.5 or lower, uses the dbt_metrics package, and will be deprecated. 
 
@@ -40,19 +40,21 @@ Starting from dbt v1.6, the metrics specification dbt Core has been updated to s
 - [Metrics](/docs/build/metrics-overview)
 
 To migrate to the new specification, you'll need to:
+
 - Create net new semantic model configs using YAML files in your dbt project
 - Upgrade your existing metrics configs to the new spec
 
-To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
-**Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
+To help ensure a smooth transition, use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
+
+**Note**, this doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-Please be aware that you will need to upgrade your development environment to dbt>1.6 if you're developing Metrics and Semantic Models in the IDE. This will upgrade the version for other analysts on your team who are using the IDE for that project, and may cause errors if they still have metrics defined using the spec in dbt 1.5 or below.
+* Note, if you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
 
 #### Test your metrics
 
-Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools. Note this is only available to users developing metrics locally:
+Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools. Note, this is only available to users developing metrics locally:
 
 1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
 2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
@@ -60,7 +62,7 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-If you are migrating using the IDE in cloud, we will run semantic validations whenever we parse or compile your project. This will ensure that your metrics and semantic models are configured correctly. You will need to test querying metrics values in downstream tools. 
+If you migrate using the dbt Cloud IDE, we will perform semantic validations every time we parse or compile your project. This will ensure correct configuration of your metrics and semantic models. You will need to test querying metrics values in downstream tools. 
 
 Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new Semantic Layer.
 
@@ -130,7 +132,7 @@ To help ensure a smooth transition, you can use our [custom-built migration tool
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-Please be aware that you will need to upgrade your development environment to dbt>1.6 if you're developing Metrics and Semantic Models in the IDE. This will upgrade the version for other analysts on your team who are using the IDE, and may cause errors if they still have metrics defined using the spec in dbt 1.5 or below.
+* Note, if you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
 
 #### Test your metrics
 
@@ -142,7 +144,7 @@ Once you’ve created your semantic models and created or updated your metrics,
 4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
 5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-If you are migrating using the IDE in cloud, we will run semantic validations whenever we parse or compile your project. This will ensure that your metrics and semantic models are configured correctly. You will need to test querying metrics values in downstream tools. 
+If you migrate using the dbt Cloud IDE, we will perform semantic validations every time we parse or compile your project. This will ensure correct configuration of your metrics and semantic models. You will need to test querying metrics values in downstream tools. 
 
 Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. 
 
@@ -163,4 +165,3 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 - [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)
 - [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
-- Developer workflow demo

From 0c27848115cb1ab8dacc0e4ccf2df3ab8b22da7c Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 25 Jul 2023 13:32:19 +0100
Subject: [PATCH 283/703] update commands

---
 .../dbt-cloud-ide/develop-in-the-cloud.md     |  1 +
 website/docs/reference/dbt-commands.md        | 28 ++-----------------
 2 files changed, 3 insertions(+), 26 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index 3c33114ac26..fd7eefdb4c3 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -7,6 +7,7 @@ tags: [IDE]
 ---
 
 The dbt Cloud integrated development environment (IDE) is a single interface for building, testing, running, and version-controlling dbt projects from your browser. With the Cloud IDE, you can compile dbt code into SQL and run it against your database directly. The IDE leverages the dbt-server, making the IDE faster and more reliable.
+
 ## Prerequisites
 
 To develop in the Cloud IDE, make sure you have the following:
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index b0adf00e212..ea5dbddef16 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -3,12 +3,11 @@ title: "dbt Command reference"
 ---
 
 dbt is typically run one of two ways:
+
 * In [dbt Cloud](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud)
 * On the [command line interface](/docs/core/about-the-cli) (CLI)
 
-The following sections outline the commands supported by dbt and their relevant flags. Note that some commands are only supported when using the CLI. 
-
-For information about selecting models on the command line, consult the docs on [Model selection syntax](/reference/node-selection/syntax).
+The following sections outline the commands supported by dbt and their relevant flags. For information about selecting models on the command line, consult the docs on [Model selection syntax](/reference/node-selection/syntax).
 
 ### Available commands
 
@@ -86,26 +85,3 @@ Use the following dbt commands in the [CLI](/docs/core/about-the-cli) and use th
 
 </Tabs>
 -->
-<!-- leaving original list here below for reference (as of April 2023)
-
-- [build](build): build and test all selected resources (models, seeds, snapshots, tests)
-- [clean](clean) (CLI only): deletes artifacts present in the dbt project
-- [compile](compile): compiles (but does not run) the models in a project
-- [debug](debug) (CLI only): debugs dbt connections and projects
-- [deps](deps): downloads dependencies for a project
-- [docs](cmd-docs) : generates documentation for a project
-- [init](init) (CLI only): initializes a new dbt project
-- [list](list) (CLI only): lists resources defined in a dbt project
-- [parse](parse) (CLI only): parses a project and writes detailed timing info
-- [run](run): runs the models in a project
-- [seed](seed): loads CSV files into the database
-- [show](show): preview table rows post-transformation
-- [snapshot](snapshot): executes "snapshot" jobs defined in a project
-- [source](commands/source): provides tools for working with source data (including validating that sources are "fresh")
-- [test](commands/test): executes tests defined in a project
-- [rpc](rpc) (CLI only): runs an RPC server that clients can submit queries to
-- [run-operation](run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
-
-
--->
-

From 39464efbf9085b68d94a335acff039e08482253c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 14:44:34 +0100
Subject: [PATCH 284/703] Update dbt-commands.md

---
 website/docs/reference/dbt-commands.md | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index ea5dbddef16..a5d9fe39dc5 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -11,11 +11,13 @@ The following sections outline the commands supported by dbt and their relevant
 
 ### Available commands
 
+<VersionBlock firstVersion="1.6">
+
 Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [CLI](/docs/core/about-the-cli). Use the `dbt` prefix. For example, to run the `test` command, type `dbt test`.
 
 - [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
 - [clean](/reference/commands/clean): deletes artifacts present in the dbt project
-- [clone](/reference/commands/clone): clone selected models from specified state (requires dbt 1.6 or higher)
+- [clone](/reference/commands/clone): clone selected models from the specified state (requires dbt 1.6 or higher)
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [debug](/reference/commands/debug): debugs dbt connections and projects
 - [deps](/reference/commands/deps): downloads dependencies for a project
@@ -31,10 +33,12 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 - [source](/reference/commands/source): provides tools for working with source data (including validating that sources are "fresh")
 - [test](/reference/commands/test): executes tests defined in a project
 - [init](/reference/commands/init): initializes a new dbt project (CLI only)
-- [rpc](/reference/commands/rpc): runs an RPC server that clients can submit queries to (CLI only)
 
-<!-- commenting out the 2nd iteration July 2023
-Select the tabs that are relevant to the your development workflow. For example, if you develop in the dbt Cloud IDE, select **dbt Cloud**. 
+</VersionBlock>
+
+<VersionBlock lasttVersion="1.5">
+
+Select the tabs that are relevant to your development workflow. For example, if you develop in the dbt Cloud IDE, select **dbt Cloud**.  
 
 <Tabs>
 <TabItem value="cloud" label="dbt Cloud">
@@ -84,4 +88,4 @@ Use the following dbt commands in the [CLI](/docs/core/about-the-cli) and use th
 </TabItem>
 
 </Tabs>
--->
+</VersionBlock>

From 1ecc9d24e9b87bcfadc461cfc25b580849c5b264 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 25 Jul 2023 14:55:59 +0100
Subject: [PATCH 285/703] fix version

---
 website/docs/reference/dbt-commands.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index a5d9fe39dc5..22cac459f4b 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -36,7 +36,7 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 
 </VersionBlock>
 
-<VersionBlock lasttVersion="1.5">
+<VersionBlock lastVersion="1.5">
 
 Select the tabs that are relevant to your development workflow. For example, if you develop in the dbt Cloud IDE, select **dbt Cloud**.  
 

From 41230c750b0ae2bdc8e157ff1917f5f3975b2980 Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Tue, 25 Jul 2023 09:26:59 -0500
Subject: [PATCH 286/703] fix indenting, add Snowflake SSO note

---
 .../docs/cloud/secure/redshift-privatelink.md    | 16 ++++++++--------
 .../docs/cloud/secure/snowflake-privatelink.md   |  7 +++++++
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/cloud/secure/redshift-privatelink.md b/website/docs/docs/cloud/secure/redshift-privatelink.md
index 56d5dd13a36..fc0ceeea334 100644
--- a/website/docs/docs/cloud/secure/redshift-privatelink.md
+++ b/website/docs/docs/cloud/secure/redshift-privatelink.md
@@ -55,19 +55,19 @@ Creating an Interface VPC PrivateLink connection requires creating multiple AWS
     
     - Target Type: **IP**
 
-        _Standard Redshift_
+        - **Standard Redshift**
 
-        - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
-        <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
+            - Use IP addresses from the Redshift cluster’s **Network Interfaces** whenever possible. While IPs listed in the **Node IP addresses** section will work, they are also more likely to change.
+            <Lightbox src="/img/docs/dbt-cloud/redshiftprivatelink4.png" title="Target type: IP address"/>
 
-        - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will also be created for that AZ. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group. If adding additional IPs, note that the NLB will also need to add the corresponding AZ. Once created, the NI(s) should stay the same (This is our observation from testing, but AWS does not officially document it).
+            - There will likely be only one Network Interface (NI) to start, but if the cluster fails over to another availability zone (AZ), a new NI will also be created for that AZ. The NI IP from the original AZ will still work, but the new NI IP can also be added to the Target Group. If adding additional IPs, note that the NLB will also need to add the corresponding AZ. Once created, the NI(s) should stay the same (This is our observation from testing, but AWS does not officially document it).
 
-        _Redshift Serverless_
+        - **Redshift Serverless**
 
-        - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
-        <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>
+            - To find the IP addresses for Redshift Serverless instance locate and copy the endpoint (only the URL listed before the port) in the Workgroup configuration section of the AWS console for the instance.
+            <Lightbox src="/img/docs/dbt-cloud/redshiftserverless.png" title="Redshift Serverless endpoint"/>
 
-        - From a command line run the command `nslookup <endpoint>` using the endpoint found in the previous step and use the associated IP(s) for the Target Group.
+            - From a command line run the command `nslookup <endpoint>` using the endpoint found in the previous step and use the associated IP(s) for the Target Group.
 
     - Target Group protocol: **TCP** 
 
diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md
index 0cbd6e96aad..0a607529ffe 100644
--- a/website/docs/docs/cloud/secure/snowflake-privatelink.md
+++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md
@@ -7,6 +7,13 @@ sidebar_label: "PrivateLink for Snowflake"
 
 The following steps will walk you through the setup of a Snowflake AWS PrivateLink endpoint in the dbt Cloud multi-tenant environment.
 
+:::info Snowflake SSO with PrivateLink
+Users connecting to Snowflake using SSO over a PrivateLink connection from dbt Cloud will also require access to a PrivateLink endpoint from their local workstation.
+
+>Currently, for any given Snowflake account, SSO works with only one account URL at a time: either the public account URL or the URL associated with the private connectivity service
+- [Snowflake SSO with Private Connectivity](https://docs.snowflake.com/en/user-guide/admin-security-fed-auth-overview#label-sso-private-connectivity)
+:::
+
 ## Configure PrivateLink
 
 1. Open a Support case with Snowflake to allow access from the dbt Cloud AWS account

From d0b13b39fae57fb47028b36d7e050434f7c5aa4f Mon Sep 17 00:00:00 2001
From: Dave Haworth <david.haworth@dbtlabs.com>
Date: Tue, 25 Jul 2023 09:37:34 -0500
Subject: [PATCH 287/703] fix Snowflake SSO note formatting

---
 website/docs/docs/cloud/secure/snowflake-privatelink.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md
index 0a607529ffe..6e82550e8d8 100644
--- a/website/docs/docs/cloud/secure/snowflake-privatelink.md
+++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md
@@ -7,10 +7,11 @@ sidebar_label: "PrivateLink for Snowflake"
 
 The following steps will walk you through the setup of a Snowflake AWS PrivateLink endpoint in the dbt Cloud multi-tenant environment.
 
-:::info Snowflake SSO with PrivateLink
+:::note Snowflake SSO with PrivateLink
 Users connecting to Snowflake using SSO over a PrivateLink connection from dbt Cloud will also require access to a PrivateLink endpoint from their local workstation.
 
 >Currently, for any given Snowflake account, SSO works with only one account URL at a time: either the public account URL or the URL associated with the private connectivity service
+
 - [Snowflake SSO with Private Connectivity](https://docs.snowflake.com/en/user-guide/admin-security-fed-auth-overview#label-sso-private-connectivity)
 :::
 

From 737b59d37c6e089600bccd1395c96ded9be43862 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 15:49:03 +0100
Subject: [PATCH 288/703] Update metricflow-time-spine.md

fix file name
---
 website/docs/docs/build/metricflow-time-spine.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 0f258e30d8d..39413014a67 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -9,9 +9,10 @@ tags: [Metrics, Semantic Layer]
 MetricFlow uses a timespine table to construct cumulative metrics. By default, MetricFlow expects the timespine table to be named `metricflow_time_spine` and doesn't support using a different name.
 
 To create this table, you need to create a model in your dbt project called `metricflow_time_spine` and add the following code:
+<File name='tricflow_time_spine.sql'>
 
 ```sql
-filename: metricflow_time_spine.sql
+
 {{
     config(
         materialized = 'table',
@@ -37,6 +38,8 @@ final as (
 
 select * from final
 ```
+</File>
+
 ```sql
 --- BigQuery supports DATE() instead of TO_DATE(). Use this model if you're using BigQuery
 {{config(materialized='table')}}

From d2d4cfc74c800c8c6641b9d98e79dada4b462910 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 25 Jul 2023 15:58:43 +0100
Subject: [PATCH 289/703] Update metricflow-time-spine.md

updating filename error
---
 website/docs/docs/build/metricflow-time-spine.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 39413014a67..bfab9c76bca 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -9,7 +9,7 @@ tags: [Metrics, Semantic Layer]
 MetricFlow uses a timespine table to construct cumulative metrics. By default, MetricFlow expects the timespine table to be named `metricflow_time_spine` and doesn't support using a different name.
 
 To create this table, you need to create a model in your dbt project called `metricflow_time_spine` and add the following code:
-<File name='tricflow_time_spine.sql'>
+<File name='metricflow_time_spine.sql'>
 
 ```sql
 

From 129858df706de78632acb0d62af3625b354b7245 Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Tue, 25 Jul 2023 10:24:14 -0700
Subject: [PATCH 290/703] Update
 website/docs/docs/core/connect-data-platform/redshift-setup.md

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/docs/docs/core/connect-data-platform/redshift-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index d0978513bf0..96d73777137 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -130,7 +130,7 @@ company-name:
 
 <TabItem value="IAM">
 
-The following table lists the authentication parameters to use IAM autentication. 
+The following table lists the authentication parameters to use IAM authentication. 
   
 To set up a Redshift profile using IAM Authentication, set the `method` parameter to `iam` as shown below. Note that a password is not required when using IAM Authentication. For more information on this type of authentication,
 consult the [Redshift Documentation](https://docs.aws.amazon.com/redshift/latest/mgmt/generating-user-credentials.html)

From 4a3040a530f405874cdccfb6bbfc2788b5f2470f Mon Sep 17 00:00:00 2001
From: Jessie Chen <121250701+jiezhen-chen@users.noreply.github.com>
Date: Tue, 25 Jul 2023 10:24:19 -0700
Subject: [PATCH 291/703] Update
 website/docs/docs/core/connect-data-platform/redshift-setup.md

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/docs/docs/core/connect-data-platform/redshift-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 96d73777137..344e1f51639 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -150,7 +150,7 @@ please refer to the official AWS documentation on [Configuration and credential
 | `iam_profile` | analyst | dbt will use the specified profile from your ~/.aws/config file |
 | `cluster_id` | CLUSTER_ID| Required for IAM |
 | `user`   | username | Account user to log into your cluster |
-| `region`  | us-east-1 | Required for IAM autentication | 
+| `region`  | us-east-1 | Required for IAM authentication | 
 
 
 <br/>

From a02ea1655036baeae0a2111b8184411dbfd4a5b5 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:47:24 -0600
Subject: [PATCH 292/703] Update number of indirect selection modes for Core
 1.5+ (#3811)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

[Preview](https://deploy-preview-3811--docs-getdbt-com.netlify.app/reference/node-selection/test-selection-examples?version=1.5&indirect-selection-mode=empty#indirect-selection)

## What are you changing in this pull request and why?

The count is off by one
[here](https://docs.getdbt.com/reference/node-selection/test-selection-examples?version=1.5&indirect-selection-mode=empty#indirect-selection):

<img width="650" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/ef63b00a-a922-4da0-b796-0e930a237f0f">

## 🎩

After the fix:
<img width="650" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/18fe97d6-e313-435a-a788-8e5910b3fb8f">


## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.

---------

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 .../docs/reference/node-selection/test-selection-examples.md    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/node-selection/test-selection-examples.md b/website/docs/reference/node-selection/test-selection-examples.md
index 7e79754ed31..85141c8cd01 100644
--- a/website/docs/reference/node-selection/test-selection-examples.md
+++ b/website/docs/reference/node-selection/test-selection-examples.md
@@ -74,7 +74,7 @@ The "buildable" and "cautious" modes can be useful in environments when you're o
 
 <VersionBlock firstVersion="1.5" >
 
-There are three modes to configure the behavior when performing indirect selection (with `eager` as the default):
+These are the modes to configure the behavior when performing indirect selection (with `eager` as the default):
 
 1. `eager` (default) - include ANY test that references the selected nodes
 1. `cautious` - restrict to tests that ONLY refer to selected nodes

From ea743024764044eb2492ec93ba23afc849cc180b Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 25 Jul 2023 14:47:44 -0400
Subject: [PATCH 293/703] Added table with commands

---
 .../migration/versions/02-upgrading-to-v1.5.md    | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 98605e2ee0f..836b2373ef1 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -56,16 +56,17 @@ models:
     tests: [] # todo! add tests later
     config: ...
 ```
-Some options that could previously be specified _before_ a sub-command can now only be specified afterward. For example, `dbt --profiles-dir . run` isn't valid anymore, and instead, you need to use `dbt run --profiles-dir .`. The list of affected commands are:
 
-- 
-- 
-
-Also, there are some options that could be previously specified _after_ a sub-command can now only be specified before. For example, `dbt run --log-path foo` isn't valid anymore, and instead, you need to use `dbt --log-path foo run`. The list of affected commands are: 
-- 
-- 
+Some options that could previously be specified _after_ a sub-command can now only be specified _before__. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
 
+| --cache-selected-only | --debug | --deprecated-print | --enable-legacy-logger| --fail-fast |
+|--------------|--------------|-----------------|-----------|---------------------------|
+| --log-cache-events  | --log-format | --log-level | --log-path | --macro-debugging |
+| --partial-parse      | --populate-cache | --print      | --printer-width | --quiet |
+| --record-timing-info | --send-anonymous-usage-stats | --single-threaded | --static-parser | --use-colors |
+| --use-experimental-parser  | --version | --version-check | --warn-error | --write-json | 
 
+Additionally, some options could be previously specified _before_ a sub-command can now only be specified _after_. Any command _not_ on the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
 
 
 The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object; otherwise, some of your dbt commands will never finish. For example:

From d2785518d8ff282db187e6a63b7303d16114bedc Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 25 Jul 2023 15:54:16 -0400
Subject: [PATCH 294/703] Update
 website/docs/docs/cloud/secure/snowflake-privatelink.md

---
 website/docs/docs/cloud/secure/snowflake-privatelink.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/secure/snowflake-privatelink.md b/website/docs/docs/cloud/secure/snowflake-privatelink.md
index 6e82550e8d8..bbbdf04ddf0 100644
--- a/website/docs/docs/cloud/secure/snowflake-privatelink.md
+++ b/website/docs/docs/cloud/secure/snowflake-privatelink.md
@@ -10,7 +10,7 @@ The following steps will walk you through the setup of a Snowflake AWS PrivateLi
 :::note Snowflake SSO with PrivateLink
 Users connecting to Snowflake using SSO over a PrivateLink connection from dbt Cloud will also require access to a PrivateLink endpoint from their local workstation.
 
->Currently, for any given Snowflake account, SSO works with only one account URL at a time: either the public account URL or the URL associated with the private connectivity service
+>Currently, for any given Snowflake account, SSO works with only one account URL at a time: either the public account URL or the URL associated with the private connectivity service.
 
 - [Snowflake SSO with Private Connectivity](https://docs.snowflake.com/en/user-guide/admin-security-fed-auth-overview#label-sso-private-connectivity)
 :::

From 50bf7de2928972e94d46299d7c178916d64eace8 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 14:46:33 -0700
Subject: [PATCH 295/703] Update steps sections and fold in PM feedback

---
 website/docs/docs/deploy/job-settings.md | 47 +++++++++++++++++++--
 website/docs/docs/deploy/slim-ci-jobs.md | 52 +++++++++++++++++++++---
 2 files changed, 90 insertions(+), 9 deletions(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 1f93e103873..6b38fd43501 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -19,12 +19,22 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 - You must have a dbt Cloud account and [Developer seat license](/docs/cloud/manage-access/seats-and-users). If you don't, you can [sign up](https://www.getdbt.com/signup/) for a [free account](https://www.getdbt.com/pricing/). 
 - You must have a dbt project connected to a [data platform](/docs/cloud/connect-data-platform/about-connections).
-- You must [create and schedule a dbt Cloud job](#create-and-schedule-jobs).
 - You must have [access permission](/docs/cloud/manage-access/about-user-access) to view, create, modify, or run jobs.
 - You must set up a [deployment environment](/docs/deploy/deploy-environments). 
 
 ## Create and schedule jobs {#create-and-schedule-jobs}
 
+:::tip Join our beta 
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the beta tab below.
+
+If you're interested in joining our beta, please [contact us](mailto:support@getdbt.com).
+
+:::
+
+<Tabs queryString="version">
+<TabItem value="ga" label="Setup steps (GA version)" default>
+
 1. Create a new job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
 1. Provide a job name, for example "Hourly Customer Job". 
 1. Under **Environment**, add the following:
@@ -57,6 +67,36 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 7. Select **Save**, then click **Run Now** to run your job. Click the run and watch its progress under **Run history**.
 
+</TabItem>
+
+<TabItem value="beta" label="Setup steps (beta version)">
+
+1. On your deployment environment page, click **Create Job** > **Deploy Job** to create a new deploy job. 
+2. Options in the **Job Description** section:
+    - **Job Name** &mdash; Specify the name for this deploy job. For example, `Daily build`.
+    - **Environment** &mdash;  By default, it’s set to the deployment environment you created the deploy job from.
+3. Options in the **Execution Settings** section:
+    - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs.
+    - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
+    - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
+    - **Add API trigger** &mdash; Set up an [API](/docs/dbt-cloud-apis/overview) trigger to run this deploy job.
+4. Options in the **Schedule** section:
+    - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
+    - **Timing** and **Run at** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Exact Intervals** that runs the job at specific times of day, **Intervals** that runs the job every specified number of hours, or to **Enter custom cron schedule (UTC)** that runs the job specified using [cron syntax](#custom-cron-schedule).
+    - **Days of the Week** &mdash; By default, it’s set to every day.
+5. (optional) Options in the **Advanced Settings** section: 
+    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this deploy job runs.
+    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this deploy job to the settings in your project.
+    - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
+    - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment.
+    - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
+
+
+</TabItem>
+
+</Tabs>
+
 ### Schedule days
 
 You can schedule which days of the week and specify customized hours or intervals to run your deploy job with the option **Exact intervals** or **Intervals**. 
@@ -98,6 +138,5 @@ Some examples are:
 
 - [Artifacts](/docs/deploy/artifacts)
 - [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
-- [Source freshness](/docs/deploy/source-freshness)
-- [Job commands](/docs/deploy/job-commands)
-- [Webhooks for your jobs](/docs/deploy/webhooks)
\ No newline at end of file
+- [Webhooks](/docs/deploy/webhooks)
+- [CI jobs](/docs/deploy/slim-ci-jobs)
\ No newline at end of file
diff --git a/website/docs/docs/deploy/slim-ci-jobs.md b/website/docs/docs/deploy/slim-ci-jobs.md
index 35fa3eff46c..638de5b5d5e 100644
--- a/website/docs/docs/deploy/slim-ci-jobs.md
+++ b/website/docs/docs/deploy/slim-ci-jobs.md
@@ -1,10 +1,10 @@
 ---
-title: "Slim CI jobs in dbt Cloud"
-sidebar_label: "Slim CI jobs"
-description: "Learn how to create and set up Slim CI checks to test code changes before deploying to production."
+title: "Continuous integration jobs in dbt Cloud"
+sidebar_label: "Continuous integration jobs"
+description: "Learn how to create and set up CI checks to test code changes before deploying to production."
 ---
 
-You can set up Slim [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request in your dbt repository. By running and testing only _modified_ models &mdash; which is what _slim_ refers to &mdash; dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
+You can set up [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request in your dbt repository. By running and testing only _modified_ models &mdash, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
 
 ## Prerequisites
 
@@ -14,10 +14,21 @@ You can set up Slim [continuous integration](/docs/deploy/continuous-integration
     - If you’re using GitLab, you must use a paid or self-hosted account which includes support for GitLab webhooks.
     - If you previously configured your dbt project by providing a generic git URL that clones using SSH, you must reconfigure the project to connect through dbt Cloud's native integration.
 
-## Set up Slim CI jobs
+## Set up CI jobs
 
 dbt Labs recommends that you create your Slim CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their Slim CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings), and accordingly the CI job in that dedicated environment will be triggered only when PRs are made to the specified, custom branch.
 
+:::tip Join our beta 
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the beta tab below.
+
+If you're interested in joining our beta, please [contact us](mailto:support@getdbt.com).
+
+:::
+
+<Tabs queryString="version">
+<TabItem value="ga" label="Setup steps (GA version)" default>
+
 1. On your deployment environment page, click **Create One** to create a new CI job.
 2. In the **Execution Settings** section: 
     - For the option **Defer to a previous run state**, choose whichever production job that's set to run often. If you don't see any jobs to select from the dropdown, you first need to run a production job successfully. Deferral tells dbt Cloud to compare the manifest of the current CI job against the project representation that was materialized the last time the deferred job was run successfully. By setting this option, dbt Cloud only checks the modified code and compares the changes against what’s running in production, instead of building the full table or the entire DAG.
@@ -29,6 +40,37 @@ dbt Labs recommends that you create your Slim CI job in a dedicated dbt Cloud [d
 
 3. In the **Triggers** section, choose the **Continuous Integration** (CI) tab. Then, enable the **Run on Pull Requests** option. This configures pull requests and new commits to be a trigger for the Slim CI job.
 
+</TabItem>
+
+<TabItem value="beta" label="Setup steps (beta version)">
+
+To make CI job creation easier, many options on the **CI job** page are set to default values that dbt Labs recommends that you use. You can change some of these defaults and there are some that you can’t (which are greyed out).
+
+1. On your **Staging** environment page, click **Create Job** > **Continuous Integration Job** to create a new CI job. 
+
+2. Options in the **Job Description** section:
+    - **Job Name** &mdash; Specify the name for this CI job.
+    - **Environment** &mdash; By default, it’s set to the environment you created the CI job from.
+    - **Triggered by pull requests** &mdash; By default, it’s enabled.
+
+3. Options in the **Execution Settings** section:
+    - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
+    - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred job selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
+    - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since doc generation testing isn't typically performed for every CI check.
+    - **Add API trigger** &mdash; Set up an [API](/docs/dbt-cloud-apis/overview) trigger to run this CI job. In the POST request method, you must also include the pull request ID and `get_sha` value.
+
+4. (optional) Options in the **Advanced Settings** section: 
+    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context.
+    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
+    - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too many of your warehouse resources.
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment.
+    - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
+    - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this CI job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
+
+
+</TabItem>
+
+</Tabs>
 
 ## Example pull requests
 

From af3cddfeeda20191a0835ed5181dd6daeab464cf Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 15:08:10 -0700
Subject: [PATCH 296/703] Revert "CI updates phase 2"

This reverts commit 0363e20d206b09082719fe2f6cf32700b8ba26fa.
---
 website/docs/docs/deploy/job-settings.md | 44 ---------------
 website/docs/docs/deploy/job-triggers.md | 68 ++++++++++++++++++++++++
 2 files changed, 68 insertions(+), 44 deletions(-)
 create mode 100644 website/docs/docs/deploy/job-triggers.md

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 6b38fd43501..3fcd6dabb76 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -96,47 +96,3 @@ If you're interested in joining our beta, please [contact us](mailto:support@get
 </TabItem>
 
 </Tabs>
-
-### Schedule days
-
-You can schedule which days of the week and specify customized hours or intervals to run your deploy job with the option **Exact intervals** or **Intervals**. 
-
-- **Exact Intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
-
-- **Intervals** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to **Every 2 hours**, the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
-
-
-:::info
-
-dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
-
-- 0 means 12am (midnight) UTC
-- 12 means 12pm (afternoon) UTC
-- 23 means 11pm UTC
-
-:::
-
-### Custom cron schedule
-
-You can fully customize the scheduling of your job with the **Enter custom cron schedule** option. Using cron syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
-
-<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
-
-Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
-
-Some examples are:
-
-- `0 * * * *`: Every hour, at minute 0
-- `*/5 * * * *`: Every 5 minutes
-- `5 4 * * *`: At exactly 4:05 AM UTC
-- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
-- `0 0 */2 * *`: At midnight UTC every other day
-- `0 0 * * 1`: At midnight UTC every Monday.
-
-
-## Related docs
-
-- [Artifacts](/docs/deploy/artifacts)
-- [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
-- [Webhooks](/docs/deploy/webhooks)
-- [CI jobs](/docs/deploy/slim-ci-jobs)
\ No newline at end of file
diff --git a/website/docs/docs/deploy/job-triggers.md b/website/docs/docs/deploy/job-triggers.md
new file mode 100644
index 00000000000..cb7a1a48088
--- /dev/null
+++ b/website/docs/docs/deploy/job-triggers.md
@@ -0,0 +1,68 @@
+---
+title: "Job triggers"
+id: "job-triggers"
+description: "You can configure when and how dbt should run your job"
+---
+
+In dbt Cloud, you can use the options under **Triggers** to configure when and how dbt should [run your job](/docs/deploy/job-triggers#schedule-job):
+
+- **Schedule** tab &mdash; Use the **Run on schedule** toggle to configure your job to run on either [scheduled days](#schedule-days) or [custom cron-powered schedule](#custom-cron-schedule)
+- **Continuous Integration (CI)** tab &mdash; Configure [continuous integration](/docs/deploy/continuous-integration) to run when someone opens a new pull request in your dbt repository
+- **API** tab &mdash; Use the [API](/docs/dbt-cloud-apis/admin-cloud-api) to trigger a job or send events to other systems
+
+<Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
+
+## Schedule jobs
+
+To schedule your job to run at specific days, times, and intervals:
+1. Go to the specific job settings, click **Edit**, then go to the **Triggers** section
+2. Go to the **Schedule** tab, and toggle **Run on schedule**
+3. Use either the [scheduled days](#schedule-days) or the [custom cron-powered schedule](#custom-cron-schedule) method to customize your desired days, times, and intervals.
+
+### Schedule days
+
+To set your job's schedule, use the **Schedule Days** option to choose specific days of the week, and select customized hours or intervals.
+
+Under **Timing**, you can either use customizable hours for jobs that need to run frequently throughout the day or exact intervals for jobs that need to run at specific times:
+
+- **Every n hours** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to "every 2 hours", the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
+
+- **At exact intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
+
+:::info
+
+dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
+
+- 0 means 12am (midnight) UTC
+- 12 means 12pm (afternoon) UTC
+- 23 means 11pm UTC
+
+:::
+
+### Custom cron schedule
+
+To fully customize the scheduling of your job, choose the **Custom cron schedule** option and use the "cron" syntax. With this syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
+
+
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
+
+Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
+
+Refer to the following example snippets:
+
+
+- `0 * * * *`: Every hour, at minute 0
+- `*/5 * * * *`: Every 5 minutes
+- `5 4 * * *`: At exactly 4:05 AM UTC
+- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
+- `0 0 */2 * *`: At midnight UTC every other day
+- `0 0 * * 1`: At midnight UTC every Monday.
+
+
+## Related docs
+
+- [Artifacts](/docs/deploy/artifacts)
+- [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
+- [Source freshness](/docs/deploy/source-freshness)
+- [Job commands](/docs/deploy/job-commands)
+- [Webhooks for your jobs](/docs/deploy/webhooks)
\ No newline at end of file

From 56538d407e2aa9a6ce4650faccb094f2cc4d3678 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 15:10:11 -0700
Subject: [PATCH 297/703] Revert "Update sidebar"

This reverts commit 0341965d67a7b14a0fde2af75cb5a4a58399c66f.
---
 website/sidebars.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/sidebars.js b/website/sidebars.js
index e52263c5e65..c09e7b784c4 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -337,6 +337,7 @@ const sidebarSettings = {
           items: [
             "docs/deploy/job-settings",
             "docs/deploy/job-commands",
+            "docs/deploy/job-triggers",
           ],
         },
         {

From 580f971abe60a2001c5c493b51a51f47fc815358 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 15:15:37 -0700
Subject: [PATCH 298/703] Change filename, add redirect

---
 website/docs/docs/deploy/{slim-ci-jobs.md => ci-jobs.md} | 0
 website/sidebars.js                                      | 2 +-
 website/static/_redirects                                | 1 +
 3 files changed, 2 insertions(+), 1 deletion(-)
 rename website/docs/docs/deploy/{slim-ci-jobs.md => ci-jobs.md} (100%)

diff --git a/website/docs/docs/deploy/slim-ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
similarity index 100%
rename from website/docs/docs/deploy/slim-ci-jobs.md
rename to website/docs/docs/deploy/ci-jobs.md
diff --git a/website/sidebars.js b/website/sidebars.js
index c09e7b784c4..66c52558189 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -345,7 +345,7 @@ const sidebarSettings = {
           label: "Continuous integration",
           link: { type: "doc", id: "docs/deploy/continuous-integration" },
           items: [
-            "docs/deploy/slim-ci-jobs",
+            "docs/deploy/ci-jobs",
           ],
         },
         {
diff --git a/website/static/_redirects b/website/static/_redirects
index 27e30e83c9e..7e41abd7805 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,3 +1,4 @@
+/docs/docs/deploy/slim-ci-jobs /docs/docs/deploy/ci-jobs 301
 
 ## refocus deploy page
 /docs/collaborate/environments/environments-in-dbt /docs/environments-in-dbt 301

From e275056025f022dc799029a393094ba9e75ba29c Mon Sep 17 00:00:00 2001
From: Jeremy Yeo <jeremyyeo@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:33:52 +1200
Subject: [PATCH 299/703] Update lint-format.md

Clear up what we mean by root project directory.
---
 website/docs/docs/cloud/dbt-cloud-ide/lint-format.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
index c486ac8b69c..099641cc22f 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
@@ -67,7 +67,7 @@ SQLFluff is a configurable SQL linter, which means you can configure your own li
 
 To configure your own linting rules:
 
-1. Create a new file in the root project directory (the parent or top-level directory for your files).
+1. Create a new file in the root project directory (the parent or top-level directory for your files). Note: The root project directory is the directory where your `dbt_project.yml` file resides.
 2. Name the file `.sqlfluff` (make sure you add the `.` before `sqlfluff`).
 3. [Create](https://docs.sqlfluff.com/en/stable/configuration.html#new-project-configuration) and add your custom config code. 
 4. Save and commit your changes.

From 63038976031e387ed1e55a7021f862955c73999f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 16:26:35 -0700
Subject: [PATCH 300/703] Fix links bc of filename change

---
 .../release-notes/07-June-2023/ci-updates-phase1-rn.md      | 2 +-
 website/docs/docs/deploy/ci-jobs.md                         | 4 ++--
 website/docs/docs/deploy/continuous-integration.md          | 6 +++---
 website/docs/docs/deploy/job-settings.md                    | 4 ++--
 .../environment-setup/2-one-deployment-environment.md       | 2 +-
 .../productionizing-your-dbt-databricks-project.md          | 2 +-
 website/docs/guides/legacy/best-practices.md                | 2 +-
 .../custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md          | 2 +-
 8 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/07-June-2023/ci-updates-phase1-rn.md b/website/docs/docs/dbt-versions/release-notes/07-June-2023/ci-updates-phase1-rn.md
index c4caf42f355..205049c908b 100644
--- a/website/docs/docs/dbt-versions/release-notes/07-June-2023/ci-updates-phase1-rn.md
+++ b/website/docs/docs/dbt-versions/release-notes/07-June-2023/ci-updates-phase1-rn.md
@@ -21,4 +21,4 @@ We're excited to introduce a few critical capabilities to dbt Cloud CI that will
 
 - **Run CI checks without blocking production runs**. CI checks will no longer consume run slots, meaning you can have as many CI checks running as you want, without impeding your production jobs.
 
-To learn more, refer to [Continuous integration](/docs/deploy/continuous-integration) and [Slim CI jobs](/docs/deploy/slim-ci-jobs).
+To learn more, refer to [Continuous integration](/docs/deploy/continuous-integration) and [CI jobs](/docs/deploy/ci-jobs).
diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 638de5b5d5e..931da8465ff 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -27,7 +27,7 @@ If you're interested in joining our beta, please [contact us](mailto:support@get
 :::
 
 <Tabs queryString="version">
-<TabItem value="ga" label="Setup steps (GA version)" default>
+<TabItem value="current" label="Current version" default>
 
 1. On your deployment environment page, click **Create One** to create a new CI job.
 2. In the **Execution Settings** section: 
@@ -42,7 +42,7 @@ If you're interested in joining our beta, please [contact us](mailto:support@get
 
 </TabItem>
 
-<TabItem value="beta" label="Setup steps (beta version)">
+<TabItem value="beta" label="Beta version">
 
 To make CI job creation easier, many options on the **CI job** page are set to default values that dbt Labs recommends that you use. You can change some of these defaults and there are some that you can’t (which are greyed out).
 
diff --git a/website/docs/docs/deploy/continuous-integration.md b/website/docs/docs/deploy/continuous-integration.md
index fbe28173ff6..4f634d4caae 100644
--- a/website/docs/docs/deploy/continuous-integration.md
+++ b/website/docs/docs/deploy/continuous-integration.md
@@ -4,7 +4,7 @@ sidebar_label: "Continuous integration"
 description: "You can set up Slim continuous integration (CI) checks to test every single change prior to deploying the code to production just like in a software development workflow."
 ---
 
-To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [Slim CI jobs](/docs/deploy/slim-ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a Slim CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a Slim CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks be approved for merging.  
+To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [CI jobs](/docs/deploy/ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a Slim CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a Slim CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks be approved for merging.  
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/ci-workflow.png" width="90%" title="Workflow of continuous integration in dbt Cloud"/>
 
@@ -16,7 +16,7 @@ Using Slim CI helps:
 
 ## How Slim CI works
 
-When you [set up Slim CI jobs](/docs/deploy/slim-ci-jobs#set-up-slim-ci-jobs), dbt Cloud listens for webhooks from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these webhooks, it enqueues a new run of the Slim CI job. If you want CI checks to run on each new commit, you need to mark your PR as **Ready for review** in your Git provider &mdash; draft PRs _don't_ trigger CI jobs. 
+When you [set up CI jobs](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud listens for webhooks from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these webhooks, it enqueues a new run of the Slim CI job. If you want CI checks to run on each new commit, you need to mark your PR as **Ready for review** in your Git provider &mdash; draft PRs _don't_ trigger CI jobs. 
 
 dbt Cloud builds and tests the models affected by the code change in a temporary schema, unique to the PR. This process ensures that the code builds without error and that it matches the expectations as defined by the project's dbt tests. The unique schema name follows the naming convention `dbt_cloud_pr_<job_id>_<pr_id>` (for example, `dbt_cloud_pr_1862_1704`) and can be found in the run details for the given run, as shown in the following image:
 
@@ -24,7 +24,7 @@ dbt Cloud builds and tests the models affected by the code change in a temporary
 
 When the Slim CI run completes, you can view the run status directly from within the pull request. dbt Cloud updates the pull request in GitHub, GitLab, or Azure DevOps with a status message indicating the results of the run. The status message states whether the models and tests ran successfully or not. 
 
-dbt Cloud deletes the temporary schema from your <Term id="data-warehouse" /> when you close or merge the pull request. If your project has database or schema customization using the [generate_database_name](/docs/build/custom-databases#generate_database_name) or [generate_schema_name](/docs/build/custom-schemas#how-does-dbt-generate-a-models-schema-name) macros, dbt Cloud might not drop the temporary schema from your data warehouse. For more information, refer to [Temp PR schema limitations](/docs/deploy/slim-ci-jobs#temp-pr-schema-limitations).
+dbt Cloud deletes the temporary schema from your <Term id="data-warehouse" /> when you close or merge the pull request. If your project has database or schema customization using the [generate_database_name](/docs/build/custom-databases#generate_database_name) or [generate_schema_name](/docs/build/custom-schemas#how-does-dbt-generate-a-models-schema-name) macros, dbt Cloud might not drop the temporary schema from your data warehouse. For more information, refer to [Temp PR schema limitations](/docs/deploy/ci-jobs#temp-pr-schema-limitations).
 
 ## Differences between Slim CI jobs and other deployment jobs
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 3fcd6dabb76..629fe78f299 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -33,7 +33,7 @@ If you're interested in joining our beta, please [contact us](mailto:support@get
 :::
 
 <Tabs queryString="version">
-<TabItem value="ga" label="Setup steps (GA version)" default>
+<TabItem value="current" label="Current version" default>
 
 1. Create a new job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
 1. Provide a job name, for example "Hourly Customer Job". 
@@ -69,7 +69,7 @@ If you're interested in joining our beta, please [contact us](mailto:support@get
 
 </TabItem>
 
-<TabItem value="beta" label="Setup steps (beta version)">
+<TabItem value="beta" label="Beta version">
 
 1. On your deployment environment page, click **Create Job** > **Deploy Job** to create a new deploy job. 
 2. Options in the **Job Description** section:
diff --git a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md b/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
index d7d64eda548..a1d500b1cba 100644
--- a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
+++ b/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
@@ -38,7 +38,7 @@ hoverSnippet: Learn how to configure a single deployment environment setup in db
 2. Create your **[production deployment environment](/docs/deploy/deploy-environments)**.
 3. Define your **dbt Cloud jobs** in the production deployment environment from step 2.
     1. **Production job(s)**: You will need to set up **at least one scheduled job** that deploys your project to your production databases/schemas. You may create multiple jobs based on your business SLAs.
-    2. **Slim CI Job**: Unlike the production jobs, which are triggered via the scheduler, this job will be triggered when PRs are opened in your repository. Refer to [Slim CI jobs](/docs/deploy/slim-ci-jobs) for details.
+    2. **CI Job**: Unlike the production jobs, which are triggered via the scheduler, this job will be triggered when PRs are opened in your repository. Refer to [CI jobs](/docs/deploy/ci-jobs) for details.
 
 
 ### When this works well
diff --git a/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md b/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
index 5da8cc6616b..bbda397eac2 100644
--- a/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
+++ b/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
@@ -85,7 +85,7 @@ Your CI job will ensure that the models build properly and pass any tests applie
 - A service principal called *dbt_test_sp*
 - A new dbt Cloud environment called *test* that defaults to the *test* catalog and uses the *dbt_test_sp* token in the deployment credentials
 
-We recommend setting up a dbt Cloud Slim CI job. This will decrease the job’s runtime by running and testing only modified models, which also reduces compute spend on the lakehouse. To create a Slim CI job, refer to [Set up Slim CI jobs](/docs/deploy/slim-ci-jobs) for details.
+We recommend setting up a dbt Cloud Slim CI job. This will decrease the job’s runtime by running and testing only modified models, which also reduces compute spend on the lakehouse. To create a Slim CI job, refer to [Set up CI jobs](/docs/deploy/ci-jobs) for details.
 
 With dbt tests and SlimCI, you can feel confident that your production data will be timely and accurate even while delivering at high velocity.
 
diff --git a/website/docs/guides/legacy/best-practices.md b/website/docs/guides/legacy/best-practices.md
index 0aad86dd2bc..018d48ba181 100644
--- a/website/docs/guides/legacy/best-practices.md
+++ b/website/docs/guides/legacy/best-practices.md
@@ -108,7 +108,7 @@ We often:
 When developing, it often makes sense to only run the model you are actively working on and any downstream models. You can choose which models to run by using the [model selection syntax](/reference/node-selection/syntax).
 
 ### Run only modified models to test changes ("slim CI")
-To merge code changes with confidence, you want to know that those changes will not cause breakages elsewhere in your project. For that reason, we recommend running models and tests in a sandboxed environment, separated from your production data, as an automatic check in your git workflow. (If you use GitHub and dbt Cloud, read about [how to set up CI jobs](/docs/deploy/slim-ci-jobs).
+To merge code changes with confidence, you want to know that those changes will not cause breakages elsewhere in your project. For that reason, we recommend running models and tests in a sandboxed environment, separated from your production data, as an automatic check in your git workflow. (If you use GitHub and dbt Cloud, read about [how to set up CI jobs](/docs/deploy/ci-jobs).
 
 At the same time, it costs time (and money) to run and test all the models in your project. This inefficiency feels especially painful if your PR only proposes changes to a handful of models.
 
diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md b/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
index 8a6f8965b87..5ea07a1be96 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
+++ b/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
@@ -5,7 +5,7 @@ id: 4-dbt-cloud-job-on-pr
 
 :::info Run on PR
 
-If your git provider has a native integration with dbt Cloud, you can take advantage of the setup instructions [here](/docs/deploy/slim-ci-jobs).
+If your git provider has a native integration with dbt Cloud, you can take advantage of the setup instructions [here](/docs/deploy/ci-jobs).
 This section is only for those projects that connect to their git repository using an SSH key.
 
 :::

From beb31d270f37dee3dc53df031e24657d70e7685f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 16:28:27 -0700
Subject: [PATCH 301/703] Update beta callouts

---
 website/docs/docs/deploy/ci-jobs.md      | 2 +-
 website/docs/docs/deploy/job-settings.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 931da8465ff..fe2286faab5 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -20,7 +20,7 @@ dbt Labs recommends that you create your Slim CI job in a dedicated dbt Cloud [d
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the beta tab below.
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the **Beta version** tab below.
 
 If you're interested in joining our beta, please [contact us](mailto:support@getdbt.com).
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 629fe78f299..78561a363db 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -26,7 +26,7 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the beta tab below.
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the **Beta version** tab below.
 
 If you're interested in joining our beta, please [contact us](mailto:support@getdbt.com).
 

From b231909d3481badc423b2f28cc25569d3265028e Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Wed, 26 Jul 2023 11:38:41 +1200
Subject: [PATCH 302/703] Outdent the user_signup semantic model key

---
 website/docs/docs/build/join-logic.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/join-logic.md b/website/docs/docs/build/join-logic.md
index eb4e02ed423..548f03f236a 100644
--- a/website/docs/docs/build/join-logic.md
+++ b/website/docs/docs/build/join-logic.md
@@ -115,7 +115,7 @@ semantic_models:
         type: time
         type_params:
         is_primary: true
-      - name: user_signup
+  - name: user_signup
     entities:
       - name: user_id
         type: primary

From 5c3faf5d5c46534e81fa41782076d5a49460a0c9 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 16:39:42 -0700
Subject: [PATCH 303/703] Remove "Slim" from  CI overview page

---
 .../docs/deploy/continuous-integration.md     | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/website/docs/docs/deploy/continuous-integration.md b/website/docs/docs/deploy/continuous-integration.md
index 4f634d4caae..b2f26af728e 100644
--- a/website/docs/docs/deploy/continuous-integration.md
+++ b/website/docs/docs/deploy/continuous-integration.md
@@ -1,57 +1,57 @@
 ---
 title: "Continuous integration in dbt Cloud"
 sidebar_label: "Continuous integration"
-description: "You can set up Slim continuous integration (CI) checks to test every single change prior to deploying the code to production just like in a software development workflow."
+description: "You can set up continuous integration (CI) checks to test every single change prior to deploying the code to production just like in a software development workflow."
 ---
 
-To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [CI jobs](/docs/deploy/ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a Slim CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a Slim CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks be approved for merging.  
+To implement a continuous integration (CI) workflow in dbt Cloud, you can set up automation that tests code changes by running [CI jobs](/docs/deploy/ci-jobs) before merging to production. dbt Cloud tracks the state of what’s running in your production environment so, when you run a CI job, only the modified data assets in your pull request (PR) and their downstream dependencies are built and tested in a staging schema. You can also view the status of the CI checks (tests) directly from within the PR; this information is posted to your Git provider as soon as a CI job completes. Additionally, you can enable settings in your Git provider that allow PRs only with successful CI checks be approved for merging.  
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/ci-workflow.png" width="90%" title="Workflow of continuous integration in dbt Cloud"/>
 
-Using Slim CI helps:
+Using CI helps:
 
 - Provide increased confidence and assurances that project changes will work as expected in production.
 - Reduce the time it takes to push code changes to production, through build and test automation, leading to better business outcomes.
 - Allow organizations to make code changes in a standardized and governed way that ensure code quality without sacrificing speed.
 
-## How Slim CI works
+## How CI works
 
-When you [set up CI jobs](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud listens for webhooks from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these webhooks, it enqueues a new run of the Slim CI job. If you want CI checks to run on each new commit, you need to mark your PR as **Ready for review** in your Git provider &mdash; draft PRs _don't_ trigger CI jobs. 
+When you [set up CI jobs](/docs/deploy/ci-jobs#set-up-ci-jobs), dbt Cloud listens for webhooks from your Git provider indicating that a new PR has been opened or updated with new commits. When dbt Cloud receives one of these webhooks, it enqueues a new run of the CI job. If you want CI checks to run on each new commit, you need to mark your PR as **Ready for review** in your Git provider &mdash; draft PRs _don't_ trigger CI jobs. 
 
 dbt Cloud builds and tests the models affected by the code change in a temporary schema, unique to the PR. This process ensures that the code builds without error and that it matches the expectations as defined by the project's dbt tests. The unique schema name follows the naming convention `dbt_cloud_pr_<job_id>_<pr_id>` (for example, `dbt_cloud_pr_1862_1704`) and can be found in the run details for the given run, as shown in the following image:
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/using_ci_dbt_cloud.png" width="90%"title="Viewing the temporary schema name for a run triggered by a PR"/>
 
-When the Slim CI run completes, you can view the run status directly from within the pull request. dbt Cloud updates the pull request in GitHub, GitLab, or Azure DevOps with a status message indicating the results of the run. The status message states whether the models and tests ran successfully or not. 
+When the CI run completes, you can view the run status directly from within the pull request. dbt Cloud updates the pull request in GitHub, GitLab, or Azure DevOps with a status message indicating the results of the run. The status message states whether the models and tests ran successfully or not. 
 
 dbt Cloud deletes the temporary schema from your <Term id="data-warehouse" /> when you close or merge the pull request. If your project has database or schema customization using the [generate_database_name](/docs/build/custom-databases#generate_database_name) or [generate_schema_name](/docs/build/custom-schemas#how-does-dbt-generate-a-models-schema-name) macros, dbt Cloud might not drop the temporary schema from your data warehouse. For more information, refer to [Temp PR schema limitations](/docs/deploy/ci-jobs#temp-pr-schema-limitations).
 
-## Differences between Slim CI jobs and other deployment jobs
+## Differences between CI jobs and other deployment jobs
 
-The [dbt Cloud scheduler](/docs/deploy/job-scheduler) executes Slim CI jobs differently from other deployment jobs in these important ways:
+The [dbt Cloud scheduler](/docs/deploy/job-scheduler) executes CI jobs differently from other deployment jobs in these important ways:
 
-- **Concurrent CI checks** &mdash; Slim CI runs triggered by the same dbt Cloud Slim CI job execute concurrently (in parallel), when appropriate
-- **Smart cancellation of stale builds** &mdash; Automatically cancels stale, in-flight Slim CI runs when there are new commits to the PR
-- **Run slot treatment** &mdash; Slim CI runs don't consume a run slot
+- **Concurrent CI checks** &mdash; CI runs triggered by the same dbt Cloud CI job execute concurrently (in parallel), when appropriate
+- **Smart cancellation of stale builds** &mdash; Automatically cancels stale, in-flight CI runs when there are new commits to the PR
+- **Run slot treatment** &mdash; CI runs don't consume a run slot
 
 ### Concurrent CI checks
 
-When you have teammates collaborating on the same dbt project creating pull requests on the same dbt repository, the same Slim CI job will get triggered. Since each run builds into a dedicated, temporary schema that’s tied to the pull request, dbt Cloud can safely execute Slim CI runs _concurrently_ instead of _sequentially_ (differing from what is done with deployment dbt Cloud jobs). Because no one needs to wait for one Slim CI run to finish before another one can start, with concurrent CI checks, your whole team can test and integrate dbt code faster.
+When you have teammates collaborating on the same dbt project creating pull requests on the same dbt repository, the same CI job will get triggered. Since each run builds into a dedicated, temporary schema that’s tied to the pull request, dbt Cloud can safely execute CI runs _concurrently_ instead of _sequentially_ (differing from what is done with deployment dbt Cloud jobs). Because no one needs to wait for one CI run to finish before another one can start, with concurrent CI checks, your whole team can test and integrate dbt code faster.
 
 Below describes the conditions when CI checks are run concurrently and when they’re not:  
 
-- Slim CI runs with different PR numbers execute concurrently. 
-- Slim CI runs with the _same_ PR number and _different_ commit SHAs execute serially because they’re building into the same schema. dbt Cloud will run the latest commit and cancel any older, stale commits. For details, refer to [Smart cancellation of stale builds](#smart-cancellation). 
-- Slim CI runs with the same PR number and same commit SHA, originating from different dbt Cloud projects will execute jobs concurrently. This can happen when two CI jobs are set up in different dbt Cloud projects that share the same dbt repository.
+- CI runs with different PR numbers execute concurrently. 
+- CI runs with the _same_ PR number and _different_ commit SHAs execute serially because they’re building into the same schema. dbt Cloud will run the latest commit and cancel any older, stale commits. For details, refer to [Smart cancellation of stale builds](#smart-cancellation). 
+- CI runs with the same PR number and same commit SHA, originating from different dbt Cloud projects will execute jobs concurrently. This can happen when two CI jobs are set up in different dbt Cloud projects that share the same dbt repository.
 
 ### Smart cancellation of stale builds {#smart-cancellation}
 
-When you push a new commit to a PR, dbt Cloud enqueues a new Slim CI run for the latest commit and cancels any Slim CI run that is (now) stale and still in flight. This can happen when you’re pushing new commits while a CI build is still in process and not yet done. By cancelling runs in a safe and deliberate way, dbt Cloud helps improve productivity and reduce data platform spend on wasteful CI runs.
+When you push a new commit to a PR, dbt Cloud enqueues a new CI run for the latest commit and cancels any CI run that is (now) stale and still in flight. This can happen when you’re pushing new commits while a CI build is still in process and not yet done. By cancelling runs in a safe and deliberate way, dbt Cloud helps improve productivity and reduce data platform spend on wasteful CI runs.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/example-smart-cancel-job.png" width="70%" title="Example of an automatically cancelled run"/>
 
 ### Run slot treatment
 
-Your Slim CI runs don't consume run slots so a CI check will never block a production run.
+Your CI runs don't consume run slots so a CI check will never block a production run.
 
 

From 4a41613080e182589d93c89bcc5dd3b38b5cf4db Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 25 Jul 2023 16:40:26 -0700
Subject: [PATCH 304/703] Nit

---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index fe2286faab5..00c9b729b44 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -4,7 +4,7 @@ sidebar_label: "Continuous integration jobs"
 description: "Learn how to create and set up CI checks to test code changes before deploying to production."
 ---
 
-You can set up [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request in your dbt repository. By running and testing only _modified_ models &mdash, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
+You can set up [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request in your dbt repository. By running and testing only _modified_ models, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
 
 ## Prerequisites
 

From a035f7f812881712dd004781b0f45b153a33e982 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:42:59 +0100
Subject: [PATCH 305/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 02674fba3df..daa64e1dfac 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -12,7 +12,7 @@ Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key comp
 Use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 
 :::info
-MetricFlow is currently available on dbt v1.6 or higher. MetricFlow, is a new way to define metrics in dbt and  replaces the dbt_metrics package.
+MetricFlow is currently available on dbt v1.6 or higher. MetricFlow provides a new way to define metrics in dbt and replaces the dbt_metrics package.
 
 To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 :::

From 4762266aba895ddabbe2f7d00a1958048d6a3e79 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:43:33 +0100
Subject: [PATCH 306/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index daa64e1dfac..2f6b40a873e 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -7,7 +7,7 @@ tags: [Metrics, Semantic Layer, Governance]
 hide_table_of_contents: true
 ---
 
-Use MetricFlow in dbt to centrally define your metrics. MetricFlow is a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and is responsible for SQL query construction and defining specifications for dbt semantic models and metrics.
+Use MetricFlow in dbt to centrally define your metrics. As a key component of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), MetricFlow is responsible for SQL query construction and defining specifications for dbt semantic models and metrics.
 
 Use familiar constructs like semantic models and metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
 

From f457064f8e6e10af7c72434a6416759a160c9c8d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:44:39 +0100
Subject: [PATCH 307/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 2f6b40a873e..316213f077f 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -14,7 +14,7 @@ Use familiar constructs like semantic models and metrics to avoid duplicative co
 :::info
 MetricFlow is currently available on dbt v1.6 or higher. MetricFlow provides a new way to define metrics in dbt and replaces the dbt_metrics package.
 
-To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
+To use the dbt Semantic Layer, you must have a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 :::
 
 Before you start, keep the following considerations in mind:

From 514b991cd73fb9dda21b7a0ff5ce80eea95b17b9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:45:13 +0100
Subject: [PATCH 308/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 316213f077f..21a32a350d6 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -19,7 +19,7 @@ To use the dbt Semantic Layer, you must have a [dbt Cloud Team or Enterprise acc
 
 Before you start, keep the following considerations in mind:
 
-- Define metrics in YAML and query them using the [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
+- Define metrics in YAML and query them using these [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
 - MetricFlow currently supports Snowflake, Postgres, BigQuery, Databricks, and Redshift.
 - Unlock insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).

From b033129f33533e4626cabcb5aec5cdd1f2849217 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:45:22 +0100
Subject: [PATCH 309/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 21a32a350d6..1d810dfac56 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -21,7 +21,7 @@ Before you start, keep the following considerations in mind:
 
 - Define metrics in YAML and query them using these [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
-- MetricFlow currently supports Snowflake, Postgres, BigQuery, Databricks, and Redshift.
+- Use MetricFlow with Snowflake, Postgres, BigQuery, Databricks, or Redshift.
 - Unlock insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
 

From 85195f203bfee330bd41798625760c01128fbdd1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:45:33 +0100
Subject: [PATCH 310/703] Update website/docs/docs/build/build-metrics-intro.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index 1d810dfac56..ed10a874a3c 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -17,7 +17,7 @@ MetricFlow is currently available on dbt v1.6 or higher. MetricFlow provides a n
 To use the dbt Semantic Layer, you must have a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
 :::
 
-Before you start, keep the following considerations in mind:
+Before you start, consider the following guidelines:
 
 - Define metrics in YAML and query them using these [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.

From d6e0c61245e921db7264ed0ed509ecc24d40c77d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:45:53 +0100
Subject: [PATCH 311/703] Update
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index f350ab6996c..8bcfb7ffbf0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -2,7 +2,7 @@
 title: "dbt Semantic Layer product architecture"
 id: sl-architecture
 description: "dbt Semantic Layer product architecture and related questions."
-sidebar_label: "Product architecture"
+sidebar_label: "Architecture"
 tags: [Semantic Layer]
 ---
 

From 5a1b1349804f7bb821d0ad59c4a07cd0e4cf0f31 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:46:02 +0100
Subject: [PATCH 312/703] Update
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 8bcfb7ffbf0..b78c0723a70 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -1,5 +1,5 @@
 ---
-title: "dbt Semantic Layer product architecture"
+title: "dbt Semantic Layer architecture"
 id: sl-architecture
 description: "dbt Semantic Layer product architecture and related questions."
 sidebar_label: "Architecture"

From a849444241c049e8d4a5e105b3dcb650145f5d43 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:47:01 +0100
Subject: [PATCH 313/703] Update
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index b78c0723a70..7bfa1fb4a20 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -8,7 +8,7 @@ tags: [Semantic Layer]
 
 <VersionBlock firstVersion="1.6">
 
-At a high level, the dbt Semantic Layer allows you to define metrics and query them using various interfaces. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). This is possible with the following components:
+The dbt Semantic Layer allows you to define metrics and use various interfaces to query them. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). The Semantic Layer includes these components:
 
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |

From 5664af8a8bf94ea2f6ecc236dd007fa27b73b283 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 10:47:08 +0100
Subject: [PATCH 314/703] Update
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 7bfa1fb4a20..86bea37b032 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -13,7 +13,7 @@ The dbt Semantic Layer allows you to define metrics and use various interfaces t
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
 | --- | --- | :---: | :---: | :---: | --- |
-| **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with the YAML specifications. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
+| **[MetricFlow](/docs/build/about-metricflow)** | MetricFlow in dbt allows users to centrally define their semantic models and metrics with YAML specifications. | ✅ | ✅ |  ✅  | BSL package (code is source available) |
 | **MetricFlow Server**| A proprietary server that takes metric requests and generates optimized SQL for the specific data platform. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **Semantic Layer API** | The interfaces that allow users to submit metric queries include the MetricFlow CLI and JDBC API. They also serve as the foundation for building first-class integrations with various tools. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|

From cd41a64afc68f9d360e49d1e7164c3c1e1bdaf0a Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 11:08:43 +0100
Subject: [PATCH 315/703] Update join-logic.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

## What are you changing in this pull request and why?
[Spotted by](https://getdbt.slack.com/archives/C046L0VTVR6/p1690326468319189?thread_ts=1690241401.197939&cid=C046L0VTVR6) @alexandrafetterman 🦅👀

user_signup was over-indented, this fixes it up
---
 website/docs/docs/build/join-logic.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/join-logic.md b/website/docs/docs/build/join-logic.md
index 548f03f236a..96426424c87 100644
--- a/website/docs/docs/build/join-logic.md
+++ b/website/docs/docs/build/join-logic.md
@@ -60,7 +60,7 @@ semantic_models:
       - name: average_purchase_price
         agg: avg
         expr: purchase_price
-      - name: user_signup
+  - name: user_signup
     entities:
       - name: user
         type: primary

From 2be37316983ae54bad81425038f8bc4eb9a0ee68 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 11:32:28 +0100
Subject: [PATCH 316/703] removing jordan's platform sentence

---
 website/docs/docs/build/sl-getting-started.md     | 15 +++++++--------
 .../release-notes/06-July-2023/sl-revamp-beta.md  |  5 +++--
 .../use-dbt-semantic-layer/sl-architecture.md     |  4 +++-
 website/snippets/_new-sl-setup.md                 |  4 +++-
 website/static/_redirects                         |  6 +++---
 5 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 52979e5c077..705635162b2 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -23,7 +23,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 
 - Have an understanding of key concepts in [MetricFlow](/docs/build/about-metricflow), which powers the revamped dbt Semantic Layer.
 - Have both your production and development environments running dbt version 1.6 or higher 
-- Use Snowflake, BigQuery, Databricks, and Redshift data platform
+- Use Snowflake, BigQuery, Databricks, or Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
 - To query with dbt Cloud:
   * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
@@ -46,8 +46,6 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 - `pip install "dbt-metricflow[your_adapter_name]"`
   * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
-Currently, Snowflake and Postgres are the supported adapters for querying locally (BigQuery, Databricks, and Redshift will be available soon). If you use an adapter other than Snowflake, you can still create metrics, but you will need to test querying them through the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl).
-
 ## Create a semantic model
 
 This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
@@ -184,12 +182,13 @@ MetricFlow requires a time spine for certain metric types and join resolution pa
 
 ## Test and query metrics
 
-:::note
-For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. However, you can install the [MetricFlow CLI](/docs/build/metricflow-cli) to test and query metrics locally.
-  * dbt Core or Developer plan users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
-:::
+This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install it and a reference for the CLI commands.
 
-Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install the CLI and a reference for the CLI commands.
+:::tip
+- dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternative, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
+
+- dbt Core or Developer plan &mdash; Users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
+:::
 
 **Query and commit your metrics using the CLI:**
 
diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index d8bb7c71940..c29468199a8 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -54,7 +54,8 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
 To experience the universal dbt Semantic Layer and its enhanced beta capabilities, check out:
 
-- [New dbt Semantic Layer documents](/docs/use-dbt-semantic-layer/dbt-sl)
+- [Introducing the new dbt Semantic Layer](https://www.getdbt.com/blog/introducing-new-look-dbt-semantic-layer)
+- [dbt Semantic Layer docs](/docs/use-dbt-semantic-layer/dbt-sl)
 - [dbt Semantic Layer get started guide](/docs/use-dbt-semantic-layer/quickstart-sl)
 - [Build your metrics with MetricFlow](/docs/build/build-metrics-intro)
-- BLOG announcement link.
+
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index 86bea37b032..da634a87b73 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -8,7 +8,9 @@ tags: [Semantic Layer]
 
 <VersionBlock firstVersion="1.6">
 
-The dbt Semantic Layer allows you to define metrics and use various interfaces to query them. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). The Semantic Layer includes these components:
+The dbt Semantic Layer allows you to define metrics and use various interfaces to query them. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). 
+
+The Semantic Layer includes these components:
 
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 71488507fa1..6a01fd8bc86 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -1,4 +1,6 @@
-You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. To configure the new Semantic Layer, you must have a successful run in your new environment.
+You can set up the dbt Semantic Layer in dbt Cloud at the environment and project level. Before you begin:
+- You must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America. 
+- You must have a successful run in your new environment.
 
 :::tip
 If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
diff --git a/website/static/_redirects b/website/static/_redirects
index f72df13a7f0..ddb585f17b4 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,12 +1,12 @@
 ## semantic layer
 
-https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
-https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  https://www.getdbt.com/blog/new-dbt-semantic-layer-spec-dna/ 301
-
+https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer  /docs/use-dbt-semantic-layer/dbt-sl?version=1.6 301
+https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  /docs/use-dbt-semantic-layer/dbt-sl?version=1.6  301
 /guides/dbt-ecosystem/sl-partner-integration-guide  /docs/use-dbt-semantic-layer/avail-sl-integrations 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
 /docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
 /docs/use-dbt-semantic-layer/quickstart-semantic-layer /docs/use-dbt-semantic-layer/quickstart-sl 301
+
 ## refocus deploy page
 /docs/collaborate/environments/environments-in-dbt /docs/environments-in-dbt 301
 /docs/collaborate/environments/dbt-cloud-environments /docs/deploy/dbt-cloud-environments 301

From 01967daf5f75d500c4534371fb0e335bce739547 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 11:37:04 +0100
Subject: [PATCH 317/703] fix typo

---
 website/docs/docs/build/sl-getting-started.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 705635162b2..b052ba14eda 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -43,7 +43,7 @@ Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an
 Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
 
 - Create or activate your virtual environment. `python -m venv venv` or `source your-venv/bin/activate`
-- `pip install "dbt-metricflow[your_adapter_name]"`
+- Run `pip install "dbt-metricflow[your_adapter_name]"`
   * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
  
 ## Create a semantic model
@@ -178,7 +178,7 @@ To continue building out your metrics based on your organization's needs, refer
 
 ## Configure the MetricFlow time spine model
 
-MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article](/docs/build/metricflow-time-spine) walks thorough how to add the `metricflow_time_spine` model to your project.
+MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article](/docs/build/metricflow-time-spine) explains how to add the `metricflow_time_spine` model to your project.
 
 ## Test and query metrics
 

From e0b6a49401e498aa274be717ba4952d5771b0d4c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 12:21:18 +0100
Subject: [PATCH 318/703] Update develop-in-the-cloud.md

fix bullet spacing
---
 .../docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md   | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index a128cbb9bd1..000723a933c 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -37,7 +37,7 @@ The Cloud IDE is a powerful tool that can help streamline and govern your data p
 
 All of these [features](#cloud-ide-features) work together to create a powerful editing environment that can help you write and maintain high-quality SQL code in less time. Whether you're a seasoned developer or just starting out, the Cloud IDE has everything you need to be productive, collaborative, and efficient.
 
-<Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-basic-layout.jpg" width="85%" title="The Cloud IDE layout includes version control on the upper left, files/folders on the left, editor on the right and command/console at the bottom"/>
+<Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-basic-layout.jpg" width="85%" title="The Cloud IDE layout includes version control on the upper left, files/folders on the left, editor on the right, and command/console at the bottom"/>
 
 ## Cloud IDE features
 
@@ -76,7 +76,7 @@ There are three start-up states when using or launching the Cloud IDE:
 
 - **Creation start &mdash;** This is the state where you are starting the IDE for the first time. You can also view this as a *cold start* (see below), and you can expect this state to take longer because the git repository is being cloned.
 - **Cold start &mdash;** This is the process of starting a new develop session, which will be available for you for three hours. The environment automatically turns off three hours after the last activity with the rpc server. This includes compile, preview, or any dbt invocation, however, it *does not* include editing and saving a file.
--** Hot start &mdash;** This is the state of resuming an existing or active develop session within three hours of the last activity.
+- **Hot start &mdash;** This is the state of resuming an existing or active develop session within three hours of the last activity.
 
 ### Work retention
 
@@ -152,7 +152,7 @@ The dbt Cloud IDE makes it possible to [build and view](/docs/collaborate/build-
 <details>
   <summary>Can I be a contributor to dbt Cloud?</summary>
   <div>
-    <div>Anyone can contribute to the dbt project. And whether it's a dbt package, a plugin, dbt-core, or this documentation site, contributing to the open source code that supports the dbt ecosystem is a great way to level yourself up as a developer, and give back to the community. See <a href="https://docs.getdbt.com/community/resources/oss-expectations">Contributing</a> for details on what to expect when contributing to the dbt open source software (OSS). </div>
+    <div>Anyone can contribute to the dbt project. And whether it's a dbt package, a plugin, dbt-core, or this documentation site, contributing to the open-source code that supports the dbt ecosystem is a great way to level yourself up as a developer, and give back to the community. See <a href="https://docs.getdbt.com/community/resources/oss-expectations">Contributing</a> for details on what to expect when contributing to the dbt open source software (OSS). </div>
   </div>
 </details>
 <details>

From 8ab0f8c615baa4ad9a0d5902e1decf08b68b3817 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 12:24:52 +0100
Subject: [PATCH 319/703] prep for idev1.2

---
 .../dbt-cloud-ide/develop-in-the-cloud.md     |  4 +-
 website/docs/reference/dbt-commands.md        | 39 ++++++++++---------
 2 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index fd7eefdb4c3..d29f263686f 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -74,8 +74,8 @@ To stay informed on IDE updates, read [dbt Cloud IDE release notes](/tags/ide),
 There are three start-up states when using or launching the Cloud IDE:
 
 - **Creation start &mdash;** This is the state where you are starting the IDE for the first time. You can also view this as a *cold start* (see below), and you can expect this state to take longer because the git repository is being cloned.
-- **Cold start &mdash;** This is the process of starting a new develop session, which will be available for you for three hours. The environment automatically turns off three hours after the last activity with the rpc server. This includes compile, preview, or any dbt invocation, however, it *does not* include editing and saving a file.
--** Hot start &mdash;** This is the state of resuming an existing or active develop session within three hours of the last activity.
+- **Cold start &mdash;** This is the process of starting a new develop session, which will be available for you for three hours. The environment automatically turns off three hours after the last activity. This includes compile, preview, or any dbt invocation, however, it *does not* include editing and saving a file.
+- **Hot start &mdash;** This is the state of resuming an existing or active develop session within three hours of the last activity.
 
 ### Work retention
 
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 22cac459f4b..ff9fea4cc00 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -15,24 +15,27 @@ The following sections outline the commands supported by dbt and their relevant
 
 Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [CLI](/docs/core/about-the-cli). Use the `dbt` prefix. For example, to run the `test` command, type `dbt test`.
 
-- [build](/reference/commands/build): build and test all selected resources (models, seeds, snapshots, tests)
-- [clean](/reference/commands/clean): deletes artifacts present in the dbt project
-- [clone](/reference/commands/clone): clone selected models from the specified state (requires dbt 1.6 or higher)
-- [compile](/reference/commands/compile): compiles (but does not run) the models in a project
-- [debug](/reference/commands/debug): debugs dbt connections and projects
-- [deps](/reference/commands/deps): downloads dependencies for a project
-- [docs](/reference/commands/cmd-docs) : generates documentation for a project
-- [list](/reference/commands/list): lists resources defined in a dbt project
-- [parse](/reference/commands/parse): parses a project and writes detailed timing info
-- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
-- [run](/reference/commands/run): runs the models in a project
-- [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
-- [seed](/reference/commands/seed): loads CSV files into the database
-- [show](/reference/commands/show): preview table rows post-transformation
-- [snapshot](/reference/commands/snapshot): executes "snapshot" jobs defined in a project
-- [source](/reference/commands/source): provides tools for working with source data (including validating that sources are "fresh")
-- [test](/reference/commands/test): executes tests defined in a project
-- [init](/reference/commands/init): initializes a new dbt project (CLI only)
+| Command | Description | Version |
+| ------- | ----------- | ------- |
+| [build](/reference/commands/build) | Build and test all selected resources (models, seeds, snapshots, tests) | All [supported versions](/docs/dbt-versions/core) |
+| [clean](/reference/commands/clean) | Deletes artifacts present in the dbt project | All [supported versions](/docs/dbt-versions/core) |
+| [clone](/reference/commands/clone) | Clone selected models from the specified state | Requires [dbt v1.6 or higher](/docs/dbt-versions/core)  |
+| [compile](/reference/commands/compile) | Compiles (but does not run) the models in a project | All [supported versions](/docs/dbt-versions/core) |
+| [debug](/reference/commands/debug) | Debugs dbt connections and projects  | All [supported versions](/docs/dbt-versions/core) |
+| [deps](/reference/commands/deps) | Downloads dependencies for a project  | All [supported versions](/docs/dbt-versions/core) |
+| [docs](/reference/commands/cmd-docs) | Generates documentation for a project | All [supported versions](/docs/dbt-versions/core) |
+| [list](/reference/commands/list) | Lists resources defined in a dbt project | All [supported versions](/docs/dbt-versions/core) |
+| [parse](/reference/commands/parse) | Parses a project and writes detailed timing info | All [supported versions](/docs/dbt-versions/core) |
+| [retry](/reference/commands/retry) | Retry the last run `dbt` command from the point of failure | Requires [dbt v1.6 or higher](/docs/dbt-versions/core) |
+| [run](/reference/commands/run) | Runs the models in a project | All [supported versions](/docs/dbt-versions/core) |
+| [run-operation](/reference/commands/run-operation) | Invoke a macro, including running arbitrary maintenance SQL against<br />  the database | All [supported versions](/docs/dbt-versions/core) |
+| [seed](/reference/commands/seed) | Loads CSV files into the database  | All [supported versions](/docs/dbt-versions/core) |
+| [show](/reference/commands/show) | Preview table rows post-transformation  | All [supported versions](/docs/dbt-versions/core) |
+| [snapshot](/reference/commands/snapshot) | Executes "snapshot" jobs defined in a project  | All [supported versions](/docs/dbt-versions/core) |
+| [source](/reference/commands/source) | Provides tools for working with source data (including validating that<br /> sources are "fresh")  | All [supported versions](/docs/dbt-versions/core) |
+| [test](/reference/commands/test) | Executes tests defined in a project  | All [supported versions](/docs/dbt-versions/core) |
+| [init](/reference/commands/init) | Initializes a new dbt project (CLI only)  | All [supported versions](/docs/dbt-versions/core) |
+
 
 </VersionBlock>
 

From 01c7afa4125f431888b04432a3f5a8c81400f7ed Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 12:50:20 +0100
Subject: [PATCH 320/703] add it and readonly license

---
 .../cloud/manage-access/cloud-seats-and-users.md   |  3 ++-
 website/docs/docs/deploy/job-notifications.md      | 14 ++++++++++----
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md b/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md
index baa92b5a98f..cc1ee86b380 100644
--- a/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md
+++ b/website/docs/docs/cloud/manage-access/cloud-seats-and-users.md
@@ -15,12 +15,13 @@ The user's assigned license determines the specific capabilities they can access
 
 | Functionality | Developer User | Read-Only Users | IT Users* |
 | ------------- | -------------- | --------------- | -------- |
-| Use the Developer IDE | ✅ | ❌ | ❌ |
+| Use the dbt Cloud IDE | ✅ | ❌ | ❌ |
 | Use Jobs | ✅ | ❌ | ❌ |
 | Manage Account | ✅ | ❌ | ✅ |
 | API Access | ✅ | ❌ | ❌ |
 | Use [Source Freshness](/docs/deploy/source-freshness) | ✅ | ✅ | ❌ |
 | Use [Docs](/docs/collaborate/build-and-view-your-docs) | ✅ | ✅ | ❌ |
+| Receive [Job notifications](/docs/deploy/job-notifications) |  ✅ |  ✅ |  ✅ | 
 *Available on Enterprise and Team plans only and doesn't count toward seat usage.
 
 ## Licenses
diff --git a/website/docs/docs/deploy/job-notifications.md b/website/docs/docs/deploy/job-notifications.md
index c240ca12183..63af4866f1d 100644
--- a/website/docs/docs/deploy/job-notifications.md
+++ b/website/docs/docs/deploy/job-notifications.md
@@ -9,13 +9,19 @@ Setting up notifications in dbt Cloud will allow you to receive alerts via Email
 
 ### Email
 
-There are two options for setting up email notifications. As a **user**, you can set up email notifications for yourself under your Profile. As an **admin**, you can set up notifications on behalf of your team members.
+These are the following options for setting up email notifications:
 
-1. Click the gear in the top right and select **Notification settings**.
+- As a **user** &mdash; You can set up email notifications for yourself under your Profile. 
+- As an **admin** &mdash; You can set up notifications on behalf of your team members. Refer to [Users and licenses](/docs/cloud/manage-access/seats-and-users) for info on license types eligible for email notifications.
 
-2. **As a user:** Select **Edit** and select the type of Notification (Succeeds, Fails, or Is Cancelled) for each Job for which you would like to be notified, or
+To set up job notifications, follow these steps:
 
-    **As an admin:**  Select one or more users you'd like to set notifications for. If you only see your own name, then you might not have admin privileges. Select **Edit** and select the type of Notification (Succeeds, Fails, or Is Cancelled) for each Job for which they will be notified.
+1. Click the gear menu in the top right corner and select **Notification Settings**.
+
+    - **As a user:** Select **Edit** and choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want to receive notifications for.
+
+    - **As an admin:**  Use the dropdown to select one or more users you'd like to set notifications for under **Configure notifications for**. If you only see your own name, then you might not have admin privileges. 
+        * Select **Edit** and choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want them to receive notifications for.
 
 3. Click **Save**.
     <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/email-notifications.png" title="Configuring Email Notifications"/>

From 8df3611389f82a93245738a6d68c1a00cae494c6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 13:07:29 +0100
Subject: [PATCH 321/703] clarify steps

---
 website/docs/docs/deploy/job-notifications.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/deploy/job-notifications.md b/website/docs/docs/deploy/job-notifications.md
index 63af4866f1d..f7281c90202 100644
--- a/website/docs/docs/deploy/job-notifications.md
+++ b/website/docs/docs/deploy/job-notifications.md
@@ -18,10 +18,11 @@ To set up job notifications, follow these steps:
 
 1. Click the gear menu in the top right corner and select **Notification Settings**.
 
-    - **As a user:** Select **Edit** and choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want to receive notifications for.
+2. Select **Edit** to begin editing the **Email Notifications** settings.
+    - **As a user:** Choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want to receive notifications for.
 
-    - **As an admin:**  Use the dropdown to select one or more users you'd like to set notifications for under **Configure notifications for**. If you only see your own name, then you might not have admin privileges. 
-        * Select **Edit** and choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want them to receive notifications for.
+    - **As an admin:**  Under **Configure notifications for**, use the dropdown to select one or more users you'd like to set notifications for. If you only see your own name, then you might not have admin privileges. <br /><br />
+    Choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want them to receive notifications for.
 
 3. Click **Save**.
     <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/email-notifications.png" title="Configuring Email Notifications"/>

From b4c63c8e611a6baf2128dffa1fd7d6e11e853c24 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 13:10:33 +0100
Subject: [PATCH 322/703] fix image size

---
 website/docs/docs/deploy/job-notifications.md        | 3 ++-
 website/snippets/slack-notifications-config-steps.md | 8 ++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/deploy/job-notifications.md b/website/docs/docs/deploy/job-notifications.md
index f7281c90202..72725a1e460 100644
--- a/website/docs/docs/deploy/job-notifications.md
+++ b/website/docs/docs/deploy/job-notifications.md
@@ -25,7 +25,8 @@ To set up job notifications, follow these steps:
     Choose the Notification type (Succeeds, Fails, or Is Cancelled) for each Job you want them to receive notifications for.
 
 3. Click **Save**.
-    <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/email-notifications.png" title="Configuring Email Notifications"/>
+
+    <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/email-notifications.png" width="75%" title="Configuring Email Notifications"/>
 
 ### Slack
 
diff --git a/website/snippets/slack-notifications-config-steps.md b/website/snippets/slack-notifications-config-steps.md
index e643d4c5644..da63f7afcc9 100644
--- a/website/snippets/slack-notifications-config-steps.md
+++ b/website/snippets/slack-notifications-config-steps.md
@@ -9,17 +9,17 @@ If there have been changes to the user roles and you need to move ownership, ple
 
 1. Click the gear in the top right and select **Profile**.
 2. Click **Integrations**  to the left.
-    <Lightbox src="/img/docs/dbt-cloud/Navigate-to-integrations.png" title="Navigate to integrations"/>
+    <Lightbox src="/img/docs/dbt-cloud/Navigate-to-integrations.png" width="75%" title="Navigate to integrations"/>
 3. Click **Link your Slack profile**
-   <Lightbox src="/img/docs/dbt-cloud/Link-your-Slack-Profile.png" title="Link your Slack profile"/>
+   <Lightbox src="/img/docs/dbt-cloud/Link-your-Slack-Profile.png" width="75%" title="Link your Slack profile"/>
 4. Allow dbt Labs to access the Slack workspace. If you are a member of multiple, you can select the appropriate workspace from the dropdown menu in the top right corner.
-   <Lightbox src="/img/docs/dbt-cloud/Allow-dbt-to-access-slack.png" title="Allow dbt access to Slack"/>
+   <Lightbox src="/img/docs/dbt-cloud/Allow-dbt-to-access-slack.png" width="75%" title="Allow dbt access to Slack"/>
 
 ### Configure the notifications
 
 1. Click the gear in the top right and select **Account Settings**.
 2. Click **Slack Notifications** to the left and click **Edit** to the right.
-   <Lightbox src="/img/docs/dbt-cloud/Navigate-to-notifications.png" title="Navigate to notifications"/>
+   <Lightbox src="/img/docs/dbt-cloud/Navigate-to-notifications.png" width="75%" title="Navigate to notifications"/>
 3. You can find the Slack notification settings at the bottom of the page.
 
 ### Disabling the Slack integration

From 6f8ce473f7ce21337cc307b904b6752d1fbd9211 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 26 Jul 2023 13:50:48 +0100
Subject: [PATCH 323/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md

---
 website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index d29f263686f..e4106c0aca6 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -6,7 +6,7 @@ sidebar_label: Develop in the IDE
 tags: [IDE]
 ---
 
-The dbt Cloud integrated development environment (IDE) is a single interface for building, testing, running, and version-controlling dbt projects from your browser. With the Cloud IDE, you can compile dbt code into SQL and run it against your database directly. The IDE leverages the dbt-server, making the IDE faster and more reliable.
+The dbt Cloud integrated development environment (IDE) is a single interface for building, testing, running, and version-controlling dbt projects from your browser. With the Cloud IDE, you can compile dbt code into SQL and run it against your database directly. 
 
 ## Prerequisites
 

From 24736f3c4308f8df4a8f7d976da1ec151019e0bc Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 26 Jul 2023 19:24:39 +0100
Subject: [PATCH 324/703] tweak sl guide

---
 .../sl-partner-integration-guide.md           | 68 ++++++++-----------
 1 file changed, 27 insertions(+), 41 deletions(-)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index b431091ffe6..1a7f5ef74a2 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -13,7 +13,7 @@ import NewChanges from '/snippets/_new-sl-changes.md';
 To fit your tool within the world of the Semantic Layer, dbt Labs offers some best practice recommendations for how to expose metrics and allow users to interact with them seamlessly. 
 
 :::note
-This is an evolving guide that is meant to provide recommendations based on our experience. If you have any feedback, we'd love to hear it (email?)!
+This is an evolving guide that is meant to provide recommendations based on our experience. If you have any feedback, we'd love to hear it!
 :::
 
 
@@ -30,14 +30,9 @@ To build a dbt Semantic Layer integration:
 
 ### Connection parameters
 
-The dbt Semantic Layer authenticates with:
-- `environmentId`, 
-- `SERVICE_TOKEN`, 
-- `host`
+The dbt Semantic Layer authenticates with `environmentId`, `SERVICE_TOKEN`, and `host`.
 
-This applies to the dbt Semantic Layer APIs, which all currently use different host names. 
-
-We recommend you provide users with separate input fields with these components (which dbt Cloud provides). 
+This applies to the dbt Semantic Layer APIs, which all currently use different host names. We recommend you provide users with separate input fields with these components (which dbt Cloud provides). 
 
 For [JDBC](/docs/dbt-cloud-apis/sl-jdbc), you can construct the JDBC URL from these inputs. Or, you could request the full URL string.
 
@@ -46,43 +41,35 @@ For [JDBC](/docs/dbt-cloud-apis/sl-jdbc), you can construct the JDBC URL from th
 
 Best practices for exposing metrics is summarized into five themes:
 
-- [Governance](#governance-and-traceability) &mdash;
-- [Discoverability](#discoverability) &mdash;
-- [Organization](#organization) &mdash;
-- [Context and interpretation](#context-and-interpretation) &mdash;
-- [Query flexibility](#query-flexibility) &mdash;
+- [Governance](#governance-and-traceability) &mdash; Recommendations on how to establish guardrails for governed data work.
+- [Discoverability](#discoverability) &mdash; Recommendations on how to make user-friendly data interactions.
+- [Organization](#organization) &mdash; Organize metrics and dimensions for all audiences.
+- [Query flexibility](#query-flexibility) &mdash; Allow users to query either one metric alone without dimensions or multiple metrics with dimensions.
+- [Context and interpretation](#context-and-interpretation) &mdash; Contextualize metrics for better analysis; expose definitions, metadata, lineage, and freshness.
 
 ### Governance and traceability
 
 When working with more governed data, it's essential to establish clear guardrails. Here are some recommendations:
 
-**Aggregations control** <br />
-Users shouldn't generally be allowed to modify aggregations unless they are performing post-processing calculations on data from the Semantic Layer (such as year over year analysis).
-
-**Time series alignment and using metric_time**<br />
-Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes. 
-
-When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). 
+- **Aggregations control** &mdash; Users shouldn't generally be allowed to modify aggregations unless they are performing post-processing calculations on data from the Semantic Layer (such as year over year analysis).
 
-As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the other time dimensions is clarifying so that people do not confuse how metrics should be plotted. 
+- **Time series alignment and using metric_time** &mdash; Make sure users view metrics across the correct time series. When displaying metric graphs, using a non-default time aggregation dimension might lead to misleading interpretations. While users can still group by other time dimensions, they should be careful not to create trend lines with incorrect time axes.<br /><br />When looking at one or multiple metrics, users should use `metric_time` as the main time dimension to guarantee they are looking at the right time series for the metric(s). <br /><br /> As such, when building an application, we recommend exposing `metric_time` as a separate, "special" time dimension on its own. This dimension is always going to align with all metrics and be common across them. Other time dimensions can still be looked at and grouped by, but having a clear delineation between the `metric_time` dimension and the other time dimensions is clarifying so that people do not confuse how metrics should be plotted. <br /><br /> Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
 
-Also, when a user requests a time granularity change for the main time series, the query that your application runs should use `metric_time` as this will always give you the correct slice. Note that when looking at a single metric, the primary time dimension and `metric_time` are equivalent. 
+- **Units consistency** &mdash; If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
 
-**Units consistency**<br />
-If units are supported, it's vital to avoid plotting data incorrectly with different units. Ensuring consistency in unit representation will prevent confusion and misinterpretation of the data.
-
-**Traceability of metric and dimension changes** <br />
-When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
+- **Traceability of metric and dimension changes** &mdash; When users change names of metrics and dimensions for reports, it's crucial to have a traceability mechanism in place to link back to the original source metric name.
 
 
 ### Discoverability 
 
 - Consider treating [metrics](/docs/build/metrics-overview) as first-class objects rather than measures. Metrics offer a higher-level and more contextual way to interact with data, reducing the burden on end-users to manually aggregate data.
+
 - Easy metric interactions: Provide users with an intuitive approach to:
     * Search for Metrics &mdash; Users should be able to easily search and find relevant metrics. Metrics can serve as the starting point to lead users into exploring dimensions.
     * Search for Dimensions &mdash; Users should be able to query metrics with associated dimensions, allowing them to gain deeper insights into the data.
     * Filter by Dimension Values &mdash; Expose and enable users to filter metrics based on dimension values, encouraging data analysis and exploration.
     * Filter additional metadata &mdash; Allow users to filter metrics based on other available metadata, such as metric type and default time granularity.
+
 - Suggested Metrics: Ideally, the system should intelligently suggest relevant metrics to users based on their team's activities. This approach encourages user exposure, facilitates learning, and supports collaboration among team members.
 
 By implementing these recommendations, the data interaction process becomes more user-friendly, empowering users to gain valuable insights without the need for extensive data manipulation.
@@ -91,27 +78,23 @@ By implementing these recommendations, the data interaction process becomes more
 
 We recommend organizing metrics and dimensions in ways that a non-technical user can understand the data model, without needing much context:
 
-**Organizing Dimensions** 
-
-To help non-technical users understand the data model better, we recommend organizing dimensions based on the entity they originated from. For example, consider dimensions like `user__country` and `product__category`.  
-
-You can create groups by extracting `user` and `product` and then nest the respective dimensions under each group. This way, dimensions align with the entity or semantic model they belong to and makes them them more user-friendly and accessible.
-
-**Organizing Metrics** 
+- **Organizing Dimensions** &mdash; To help non-technical users understand the data model better, we recommend organizing dimensions based on the entity they originated from. For example, consider dimensions like `user__country` and `product__category`.<br /><br />  You can create groups by extracting `user` and `product` and then nest the respective dimensions under each group. This way, dimensions align with the entity or semantic model they belong to and makes them them more user-friendly and accessible.
 
-The goal is to organize metrics into a hierarchy in our configurations, instead of presenting them in a long list. 
-
-This hierarchy helps you organize metrics based on a specific criteria, such as business unit or team. By providing this structured organization, users can find and navigate metrics more efficiently, enhancing their overall data analysis experience.
+- **Organizing Metrics** &mdash; The goal is to organize metrics into a hierarchy in our configurations, instead of presenting them in a long list.<br /><br /> This hierarchy helps you organize metrics based on a specific criteria, such as business unit or team. By providing this structured organization, users can find and navigate metrics more efficiently, enhancing their overall data analysis experience.
 
 ### Query flexibility
 
-Allow users to query either one metric alone without dimensions or multiple metrics with dimensions
+Allow users to query either one metric alone without dimensions or multiple metrics with dimensions.
+
+- Allow toggling between metrics / dimensions seamlessly.
 
-- Allow toggling between metrics / dimensions seamlessly
 - Be clear on exposing what dimensions are queryable with what metrics and hide things that don’t apply, and vice versa.
+
 - Only expose time granularities (monthly, daily, yearly) that match the available metrics. 
   * For example, if a dbt model and its resulting semantic model have a monthly granularity, make sure querying data with a 'daily' granularity isn't available to the user. Our APIs have functionality that will help you surface the correct granularities
+
 - We recommend that time granularity is treated as a general time dimension-specific concept and that it can be applied to more than just the primary aggregation (or `metric_time`). Consider a situation where a user wants to look at `sales` over time by `customer signup month`; in this situation, having the ability able to apply granularities to both time dimensions is crucial. Note: initially, as a starting point, it makes sense to only support `metric_time` or the primary time dimension, but we recommend expanding that as your solution evolves. 
+
 - You should allow users to filter on date ranges and expose a calendar and nice presets for filtering these. 
   * For example: last 30 days, last week etc.
 
@@ -119,11 +102,14 @@ Allow users to query either one metric alone without dimensions or multiple metr
 
 For better analysis, it's best to have the context of the metrics close to where the analysis is happening. We recommend the following:
 
-- Expose business definitions of the metrics as well as logical definitions
-- Expose additional metadata from the Semantic layer (measures, type parameters)
+- Expose business definitions of the metrics as well as logical definitions.
+
+- Expose additional metadata from the Semantic layer (measures, type parameters).
+
 - Use the [Discovery API](/docs/dbt-cloud-apis/discovery-api) to enhance the metric and build confidence in its accuracy:
   * Check if the metric is fresh and when it was last updated.
   * Include lineage information to understand the metric's origin.
+
 - Allow for creating other metadata that’s useful for the metric. We can provide some of this information in our configuration (Display name, Default Granularity for View, Default Time range), but there may be other metadata that your tool wants to provide to make the metric richer.
 
 ### Example stages of an integration

From cd0b4b10e416766aefb9fb4bbeccf0fbfcf90b95 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:10:59 +0100
Subject: [PATCH 325/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 7dde6f32572..f810d0fee33 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -1,6 +1,8 @@
 
 <VersionBlock firstVersion="1.6">
 
+To use the Semantic Layer, you must:
+
 - Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform

From 13f87bbbcd6c7668499cf247ffc946f16d0b4c4a Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:11:21 +0100
Subject: [PATCH 326/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index f810d0fee33..cb95a970775 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -6,7 +6,7 @@ To use the Semantic Layer, you must:
 - Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
-- A successful run in the environment where your Semantic Layer is configured
+-  Create a successful run in the environment where you configure the Semantic Layer. <br/>
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 

From a29545faee6927681ace9dc98dbb928650336d69 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:11:34 +0100
Subject: [PATCH 327/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index cb95a970775..e4d5b28bde6 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -11,7 +11,7 @@ To use the Semantic Layer, you must:
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
   * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need to have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
-- Have an understanding of [MetricFlow's](/docs/build/about-metricflow) key concepts, which powers the revamped dbt Semantic Layer.
+- Understand [MetricFlow's](/docs/build/about-metricflow) key concepts, which powers the revamped dbt Semantic Layer.
 
 
 </VersionBlock>

From 7982c6b50f4e05923fa492ca28eded71f2e65cd6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:13:17 +0100
Subject: [PATCH 328/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index e4d5b28bde6..88a8e66620a 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -10,7 +10,7 @@ To use the Semantic Layer, you must:
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you'll need to have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
+**Note:** Developer accounts can only query data manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you must have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
 - Understand [MetricFlow's](/docs/build/about-metricflow) key concepts, which powers the revamped dbt Semantic Layer.
 
 

From c378b506516694af8d1751c30fb88133e12e2eff Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:13:35 +0100
Subject: [PATCH 329/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 88a8e66620a..359a4aa7551 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -9,7 +9,7 @@ To use the Semantic Layer, you must:
 -  Create a successful run in the environment where you configure the Semantic Layer. <br/>
   * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
-- Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions 
+- Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions. <br /> 
 **Note:** Developer accounts can only query data manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you must have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />
 - Understand [MetricFlow's](/docs/build/about-metricflow) key concepts, which powers the revamped dbt Semantic Layer.
 

From 6a81e23ea29a40a9674c04a1e7098eda81f31d91 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:13:54 +0100
Subject: [PATCH 330/703] Update website/snippets/_v2-sl-prerequisites.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 359a4aa7551..2ee9c8257ea 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -7,7 +7,7 @@ To use the Semantic Layer, you must:
 - Have both your production and development environments running dbt version 1.6 or higher 
 - Use Snowflake, BigQuery, Databricks, and Redshift data platform
 -  Create a successful run in the environment where you configure the Semantic Layer. <br/>
-  * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
+  **Note:** Semantic Layer currently supports Deployment environment. (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions. <br /> 
 **Note:** Developer accounts can only query data manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you must have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />

From c837cc46050d6370528f7d4d9ef0173dbc41eb15 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:15:11 +0100
Subject: [PATCH 331/703] Update website/snippets/_legacy-sl-callout.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_legacy-sl-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index 622e8290181..c3a261510e3 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,4 +1,4 @@
-:::tip Upgrade to access the new dbt Semantic Layer
+:::important Upgrade to access the new dbt Semantic Layer
 
 The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing a new API, and making it more efficient to define/query metrics. This means the legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
 

From a368a90573b725cb81ca622ec431f1d6f1cc2817 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:15:40 +0100
Subject: [PATCH 332/703] Update website/snippets/_legacy-sl-callout.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_legacy-sl-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index c3a261510e3..84c91895f89 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -1,6 +1,6 @@
 :::important Upgrade to access the new dbt Semantic Layer
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing a new API, and making it more efficient to define/query metrics. This means the legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
+The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing a new API, and making it more efficient to define and query metrics. The legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
 
 **Who does this affect?** Anyone who uses the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 

From c71d133cf0eb2cdf51f3b82af125ced7661ab47d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:17:00 +0100
Subject: [PATCH 333/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index e36622c1910..398957b32a7 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -14,7 +14,7 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 <NewSLChanges />
 
 
-The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies the process of defining and using critical business metrics, like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing you to define metrics on top of existing models and automatically handles data joins. 
+The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies the process of defining and using critical business metrics, like `revenue` in the modeling layer (your dbt project). By centralizing metric definitions, data teams can ensure consistent self-service access to these metrics in downstream data tools and applications. The dbt Semantic Layer eliminates duplicate coding by allowing data teams to define metrics on top of existing models and automatically handles data joins. 
 
 Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 

From b6df059fd625320ba9b2eb5519aa58752df849be Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:17:50 +0100
Subject: [PATCH 334/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 398957b32a7..45cd421d8bf 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -18,7 +18,7 @@ The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), s
 
 Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 
-To read more about why you need a universal Semantic Layer, read this [blog post](https://www.getdbt.com/blog/universal-semantic-layer/).
+To learn more about why you need a universal Semantic Layer, refer to the [Why we need a universal semantic layer](https://www.getdbt.com/blog/universal-semantic-layer/)  blog post.
 
 ## Explore the dbt Semantic Layer
 <!-- this partial lives here: https://github.com/dbt-labs/docs.getdbt.com/website/snippets/_sl-plan-info. Use it on diff pages and to tailor the message depending which instance can access the SL and what product lifecycle we're in. -->

From 7013d7641d6b5e24e86b43f887bec48ea091a39c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:18:26 +0100
Subject: [PATCH 335/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 45cd421d8bf..56d5b3f073d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -43,7 +43,7 @@ instance="hosted in North America"
 
 <Card
     title="Set up the dbt Semantic Layer"
-    body="Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
+    body="Set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
     link="/docs/use-dbt-semantic-layer/setup-sl"
     icon="dbt-bit"/>
 

From 5d455397ac62a3d27bfe59c314b2aeb0b49cc875 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:18:47 +0100
Subject: [PATCH 336/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 6d388c01e44..ce2d9fb38ac 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -1,7 +1,7 @@
 ---
 title: "Semantic manifest"
 id: sl-manifest
-description: "Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."
+description: "Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your dbt Semantic Layer."
 tags: [Semantic Layer, APIs]
 sidebar_label: "Semantic manifest"
 ---

From f2c28cb40040404e21cb0df10bc4fa6b42ee6c50 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:19:18 +0100
Subject: [PATCH 337/703] Update website/snippets/_new-sl-setup.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_new-sl-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 6a01fd8bc86..9147202c664 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -16,7 +16,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-initial-page.jpg" width="60%" title="Semantic Layer section in the Project Details page"/>
 
 4. In the **Set Up Semantic Layer Configuration** page, enter the credentials you want the Semantic Layer to use specific to your data platform. 
-    * Note: We recommend using a less privileged set of credentials because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
+**Note:** We recommend credentials have the least privileges required because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
 
 5. Select the deployment environment you want for the Semantic Layer and click **Save**.
 

From 0b5fc95b6e89c043ac002fe3b1fe451445a4cb24 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:28:06 +0100
Subject: [PATCH 338/703] Update website/snippets/_sl-plan-info.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_sl-plan-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_sl-plan-info.md b/website/snippets/_sl-plan-info.md
index 23d259a346b..b5cc2ea0e4e 100644
--- a/website/snippets/_sl-plan-info.md
+++ b/website/snippets/_sl-plan-info.md
@@ -1 +1 @@
-<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, the {props.product} is accessible to <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span> 
+<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, you can access {props.product} on <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span> 

From cf0c4c7f0330c81dd6138f76b2a2eafc2ca4da80 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:28:28 +0100
Subject: [PATCH 339/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index b052ba14eda..725b5513bc6 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -10,7 +10,7 @@ meta:
 
 This getting started page recommends a workflow to help you create your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you prefer using a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
 
-Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
+To fully experience the power of a universal dbt Semantic Layer, take the following steps:
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
 - [Define metrics](#define-metrics) with MetricFlow

From b65dbd5ee4656fb7cb0091be44c43b0252b48703 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:28:38 +0100
Subject: [PATCH 340/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 725b5513bc6..d55f27f7023 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -17,7 +17,7 @@ To fully experience the power of a universal dbt Semantic Layer, take the follow
 - [Test and query metrics](#test-and-query-metrics) with MetricFlow
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#set-up-dbt-semantic-layer) in dbt Cloud
-- [Connect and query API](#connect-and-query-api) with dbt Cloud
+- [Connect to and query the API](#connect-and-query-api) with dbt Cloud
 
 ## Prerequisites
 

From 87883aa75c24ed65019ebc1936fc1952c4337dca Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:34:27 +0100
Subject: [PATCH 341/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index d55f27f7023..e14183ed53a 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -48,7 +48,7 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
  
 ## Create a semantic model
 
-This part of the guide will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
+The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
 
 We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuilding it step by step in this example. 
 

From 24e27ccdf08e9a5fb1278dd02eab9bbdfb253a8c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:34:48 +0100
Subject: [PATCH 342/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index e14183ed53a..45af1771d51 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -50,7 +50,7 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 
 The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
 
-We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the yaml extension so it's ignored during parsing. We'll be rebuilding it step by step in this example. 
+It's highly recommended you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the .yaml extension so it's ignored during parsing. You'll be rebuilding it step by step in this example. 
 
 If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
 

From 67c51b52d841e55d890f3efa7b460e8fb790b3b4 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:34:56 +0100
Subject: [PATCH 343/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 45af1771d51..208cb6e493e 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -56,7 +56,7 @@ If you're following the guide in your own project, pick a model that you want to
 
 1. Create a new semantic model file, such as `orders.yaml`. 
 
-We recommending creating semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name, appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now `default_agg_time_dimension` is the only supported default. 
+It's best practice to create semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name and appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now, `default_agg_time_dimension` is the only supported default. 
 
 ```yaml
 semantic_models:

From c68c15b01fc4e2fc29cbe223f3e99314fee4d318 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:35:08 +0100
Subject: [PATCH 344/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 208cb6e493e..e17648b11e1 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -158,7 +158,7 @@ group by metric_time_day, country  -- dimensions
 
 Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](https://docs.getdbt.com/docs/build/metrics-overview) before getting started. 
 
-1. You can define metrics in the same YAML files as your semantic models, or create a new file. If you want to create your metrics in a new file, we recommend creating another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example by data source or business line. 
+1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line. 
 
 2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 

From f6d3de7a3322ac4878beb7c5e2655984e9b0ee2d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:35:23 +0100
Subject: [PATCH 345/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index e17648b11e1..bc1eec739fb 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -160,7 +160,7 @@ Now that you've created your first semantic model, it's time to define your firs
 
 1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line. 
 
-2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the `example_metrics` file, or removing the .yaml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
+2. The example metric you'll create is a simple metric that refers directly to the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, delete the `example_metrics` file or remove the .yaml extension so it's ignored during parsing. You'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 
 ```yaml
 metrics:

From 14396a2a89f64ffa845c48a9913ef01c0b0f2244 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:35:36 +0100
Subject: [PATCH 346/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index bc1eec739fb..591202405b4 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -172,7 +172,7 @@ metrics:
       measure: order_total
 ```
  
-3. Save your code and validate your configs (we'll do this in the next step of the guide 😀) before committing them to your repository.
+3. Save your code, and in the next section, you'll validate your configs before committing them to your repository.
 
 To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
 

From a1c5274d1759ae28e005eb283be0f3acca71bade Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:35:48 +0100
Subject: [PATCH 347/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 591202405b4..8f7c645d345 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -182,7 +182,7 @@ MetricFlow requires a time spine for certain metric types and join resolution pa
 
 ## Test and query metrics
 
-This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on how to install it and a reference for the CLI commands.
+This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
 
 :::tip
 - dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternative, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.

From 2f1e145d46b5e29edac5e22514746d43bd9d3791 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:35:57 +0100
Subject: [PATCH 348/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 8f7c645d345..c6522099838 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -185,7 +185,7 @@ MetricFlow requires a time spine for certain metric types and join resolution pa
 This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
 
 :::tip
-- dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternative, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
+- dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternatively, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
 
 - dbt Core or Developer plan &mdash; Users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
 :::

From ecbf1cb6d528fa1ea51b433829d87a681dc8ce8f Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:36:31 +0100
Subject: [PATCH 349/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index c6522099838..63d27aaf16a 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -205,7 +205,7 @@ MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To
 
 Before you begin, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
 
-Once you’ve defined metrics in your dbt project, you can perform a job run in your dbt Cloud deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
+Once you’ve defined metrics in your dbt project, you can perform a job run in your dbt Cloud deployment environment to materialize your metrics. Only the deployment environment is supported for the dbt Semantic Layer at this moment. 
 
 1. Go to **Deploy** in the navigation header
 2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.

From d40c69b2c240455b7aecc83a55eaae4fe502c8c8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:36:45 +0100
Subject: [PATCH 350/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 63d27aaf16a..0b8bb735e33 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -196,7 +196,7 @@ MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
-3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. i.e `mf query --metrics order_total --group-by metric_time`
+3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. For example, `mf query --metrics order_total --group-by metric_time`
 4. Verify that the metric values are what you expect. To further understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
 5. Run `mf validate-configs` to run validation on your semantic models and metrics. 
 6. Commit and merge the code changes that contain the metric definitions.

From 67a40d3a2f031a763e2704e1f6a736913f807a00 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:38:43 +0100
Subject: [PATCH 351/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 56d5b3f073d..03ffed32c09 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -75,7 +75,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 <LegacyInfo />
 
-The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
+The dbt Semantic Layer allows your data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
 
 The result? You have less duplicate coding for data teams and more consistency for data consumers. 
 

From c05e96486952c5338da486334c20cb42fe0c54c1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:39:32 +0100
Subject: [PATCH 352/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 03ffed32c09..c7729c1c60e 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -77,7 +77,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 The dbt Semantic Layer allows your data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
 
-The result? You have less duplicate coding for data teams and more consistency for data consumers. 
+Resulting in less duplicate coding for data teams and more consistency for data consumers. 
 
 The dbt Semantic Layer has four main parts:
 

From 6d7c832ccdf3e34b21ed275ab7f0451197e5a7fb Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:41:19 +0100
Subject: [PATCH 353/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index ce2d9fb38ac..470382fac0c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -14,7 +14,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 </VersionBlock>
 
-dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the "Semantic Manifest" (`semantic_manifest.json`), which MetricFlow requires to build and run metric queries properly for the Semantic Layer. This artifact contains comprehensive information about your Semantic Layer. It is an internal file, that acts as the integration point with MetricFlow. 
+dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the _Semantic Manifest_ (`semantic_manifest.json`), which MetricFlow requires to build and run metric queries properly for the dbt Semantic Layer. This artifact contains comprehensive information about your dbt Semantic Layer. It is an internal file that acts as the integration point with MetricFlow. 
 
 By using the semantic manifest produced by dbt Core, MetricFlow will instantiate a data flow plan and generate SQL from Semantic Layer query requests. You can it as a valuable reference to understand the structure and details of your data models.
 

From 60910ecaeb20cd45fdb700781734fb007a5fdc4f Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:41:48 +0100
Subject: [PATCH 354/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 470382fac0c..10b2747acc1 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -16,7 +16,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the _Semantic Manifest_ (`semantic_manifest.json`), which MetricFlow requires to build and run metric queries properly for the dbt Semantic Layer. This artifact contains comprehensive information about your dbt Semantic Layer. It is an internal file that acts as the integration point with MetricFlow. 
 
-By using the semantic manifest produced by dbt Core, MetricFlow will instantiate a data flow plan and generate SQL from Semantic Layer query requests. You can it as a valuable reference to understand the structure and details of your data models.
+By using the semantic manifest produced by dbt Core, MetricFlow will instantiate a data flow plan and generate SQL from Semantic Layer query requests. It's a valuable reference that you can use to understand the structure and details of your data models.
 
 Similar to the [`manifest.json` file](/reference/artifacts/manifest-json), the `semantic_manifest.json` also lives in the `/target` directory of your dbt project. This is where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
 

From 01ae2a247f2db5bbe6ddbfdb4e924ee8f2eb4be3 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:42:06 +0100
Subject: [PATCH 355/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 10b2747acc1..106e228a003 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -31,7 +31,7 @@ QUESTIONS:
 - HOW DOES IT RELATED TO THE [JSON SCHEMA FILE](https://schemas.getdbt.com/)?
 
 <details>
-<summary><code>target/semantic_manifest.json</code> file </summary>
+<summary>Example <code>target/semantic_manifest.json</code> file </summary>
 
 ```json
 {

From d000eca056e8b5a861dfb7d17a9080d8f5fa59d1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:43:34 +0100
Subject: [PATCH 356/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index c7729c1c60e..f01f91149ce 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -79,7 +79,7 @@ The dbt Semantic Layer allows your data teams to centrally define essential busi
 
 Resulting in less duplicate coding for data teams and more consistency for data consumers. 
 
-The dbt Semantic Layer has four main parts:
+The dbt Semantic Layer has these main parts:
 
 - Define your metrics in version-controlled dbt project code using [MetricFlow](/docs/build/about-metricflow) 
 	* dbt_metrics is now deprecated

From 98e489c435a9e712ffebed0f9b20bed284716d46 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:44:52 +0100
Subject: [PATCH 357/703] Update website/snippets/_new-sl-setup.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_new-sl-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 9147202c664..aecbde86777 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -23,7 +23,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="Set Up your Semantic Layer configuration."/>
 
 6. You should see connection information that allows you to connect to downstream tools. 
-    * If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
+**Note:** If your tool supports JDBC, make sure to save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 
 7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
 

From 0b1e9657e6c3d594d008539eb853e22a3a5e50c9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:45:19 +0100
Subject: [PATCH 358/703] Update website/snippets/_new-sl-setup.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_new-sl-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index aecbde86777..47705f50d32 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -25,7 +25,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 6. You should see connection information that allows you to connect to downstream tools. 
 **Note:** If your tool supports JDBC, make sure to save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 
-7. Next, go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+7. Return to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
 
 8. Save and copy your environment ID, service token, and host for inputting into a downstream tool
 

From 909cfa23a3a1210eebc644753eea7a980dc0fdad Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:45:46 +0100
Subject: [PATCH 359/703] Update website/snippets/_new-sl-setup.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_new-sl-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 47705f50d32..45d1cdccef9 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -27,7 +27,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 7. Return to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
 
-8. Save and copy your environment ID, service token, and host for inputting into a downstream tool
+8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools.
 
 9. Great job, you've configured the Semantic Layer 🎉! 
 

From 63614309063fda7fba0afd6daee01b285b602d22 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:46:07 +0100
Subject: [PATCH 360/703] Update website/snippets/_new-sl-setup.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_new-sl-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 45d1cdccef9..693e2b80fe6 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -29,5 +29,5 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools.
 
-9. Great job, you've configured the Semantic Layer 🎉! 
+Great job, you've configured the Semantic Layer 🎉! 
 

From f024c3b6797ed4af66d6381e905be014cbc5d541 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:48:40 +0100
Subject: [PATCH 361/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index f01f91149ce..87de4dedf2e 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -83,7 +83,7 @@ The dbt Semantic Layer has these main parts:
 
 - Define your metrics in version-controlled dbt project code using [MetricFlow](/docs/build/about-metricflow) 
 	* dbt_metrics is now deprecated
-- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
+- Import your metric definitions using the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
 - Query your metric data via the dbt Proxy Server
 - Explore and analyze dbt metrics in downstream tools
 

From c12015230d23519ccfe9247fe1749322b8d7279a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 09:50:59 +0100
Subject: [PATCH 362/703] nits

---
 website/docs/docs/build/sl-getting-started.md             | 8 ++++----
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md        | 3 +--
 .../docs/docs/use-dbt-semantic-layer/sl-architecture.md   | 5 +++++
 website/snippets/_sl-plan-info.md                         | 2 +-
 4 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index b052ba14eda..ccb66697d89 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -10,7 +10,7 @@ meta:
 
 This getting started page recommends a workflow to help you create your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you prefer using a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
 
-Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
+To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/). These are the following steps you'll take:
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
 - [Define metrics](#define-metrics) with MetricFlow
@@ -22,11 +22,11 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 ## Prerequisites
 
 - Have an understanding of key concepts in [MetricFlow](/docs/build/about-metricflow), which powers the revamped dbt Semantic Layer.
-- Have both your production and development environments running dbt version 1.6 or higher 
+- Have both your production and development environments running dbt version 1.6 or higher. Refer to [upgrade in dbt Cloud](/docs/dbt-versions/upgrade-core-in-cloud) for more info.
 - Use Snowflake, BigQuery, Databricks, or Redshift data platform
 - A successful run in the environment where your Semantic Layer is configured
 - To query with dbt Cloud:
-  * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
+  * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America (Other regions coming soon)
   * Set up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions.
 - To query locally:
   * dbt Core or Developer accounts must manually install the [MetricFlow CLI](/docs/build/metricflow-cli) to test or query their metrics. After installing the package, make sure you run at least one model. <br />
@@ -207,7 +207,7 @@ Before you begin, you must have a dbt Cloud Team or Enterprise [multi-tenant](/d
 
 Once you’ve defined metrics in your dbt project, you can perform a job run in your dbt Cloud deployment environment to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer at this moment. 
 
-1. Go to **Deploy** in the navigation header
+1. Go to **Deploy** in the menu bar
 2. Select **Jobs** to re-run the job with the most recent code in the deployment environment.
 3. Your metric should appear as a red node in the dbt Cloud IDE and dbt directed acyclic graphs (DAG).
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index e36622c1910..b01313d16f5 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -1,7 +1,7 @@
 ---
 title: "dbt Semantic Layer"
 id: dbt-sl
-description: "Introducing the improved dbt Semantic Layer, which allows data teams to centrally define and query metrics."
+description: "Learn how the dbt Semantic Layer enables data teams to centrally define and query metrics."
 sidebar_label: "About the dbt Semantic Layer"
 tags: [Semantic Layer]
 hide_table_of_contents: true
@@ -31,7 +31,6 @@ product="dbt Semantic Layer"
 plan="dbt Cloud Team and Enterprise"
 instance="hosted in North America"
 />
-<br /><br />
 
 <div className="grid--3-col">
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index da634a87b73..b03023b3ae2 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -6,6 +6,11 @@ sidebar_label: "Architecture"
 tags: [Semantic Layer]
 ---
 
+import NewSLChanges from '/snippets/_new-sl-changes.md';
+
+<NewSLChanges />
+
+
 <VersionBlock firstVersion="1.6">
 
 The dbt Semantic Layer allows you to define metrics and use various interfaces to query them. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). 
diff --git a/website/snippets/_sl-plan-info.md b/website/snippets/_sl-plan-info.md
index 23d259a346b..3a6607e3552 100644
--- a/website/snippets/_sl-plan-info.md
+++ b/website/snippets/_sl-plan-info.md
@@ -1 +1 @@
-<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, the {props.product} is accessible to <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span> 
+<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, the {props.product} is accessible to <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use the MetricFlow CLI to test metrics locally, but can't dynamically query them with integrated tools.</span> 

From 1203c5364f33484ddfda56b459f21696cdc621a2 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:52:35 +0100
Subject: [PATCH 363/703] Update website/docs/docs/build/sl-getting-started.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 0b8bb735e33..ad2a8fcd66c 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,7 +8,7 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
-This getting started page recommends a workflow to help you create your first metrics. This guide uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template). If you prefer using a sandbox project to follow the guide, you can clone the repo from GitHub. Otherwise, you can create semantic models and metrics for your own dbt project. 
+This getting started page presents a sample workflow to help you create your first metrics. It uses the [Jaffle shop example](https://github.com/dbt-labs/jaffle-sl-template) as the project data source and is available for you to use. If you prefer, you can create semantic models and metrics for your own dbt project. 
 
 To fully experience the power of a universal dbt Semantic Layer, take the following steps:
 

From fec7c9e8240827060363c02f838bd588ae2fb8e5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:55:48 +0100
Subject: [PATCH 364/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index acbbc43c3fb..5ed08ebefbc 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -83,7 +83,7 @@ The dbt Semantic Layer has these main parts:
 - Define your metrics in version-controlled dbt project code using [MetricFlow](/docs/build/about-metricflow) 
 	* dbt_metrics is now deprecated
 - Import your metric definitions using the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
-- Query your metric data via the dbt Proxy Server
+- Query your metric data with the dbt Proxy Server
 - Explore and analyze dbt metrics in downstream tools
 
 ### What makes the dbt Semantic Layer different?

From cae760a4a107a4cc1b187f3b289e81a28d834654 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:56:04 +0100
Subject: [PATCH 365/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 5ed08ebefbc..5e7c18013dd 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -88,7 +88,7 @@ The dbt Semantic Layer has these main parts:
 
 ### What makes the dbt Semantic Layer different?
 
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
+The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, your data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
 
 
 ## Prerequisites

From 16dcf4818327f095cab954ab5d3421c6ac3a5346 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:56:19 +0100
Subject: [PATCH 366/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 5e7c18013dd..85f15259166 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -92,7 +92,6 @@ The dbt Semantic Layer reduces code duplication and inconsistency regarding your
 
 
 ## Prerequisites
-To use the dbt Semantic Layer, you’ll need to meet the following:
 
 <Snippet path="sl-prerequisites" />
 

From ef5273415c8f03f5a8b90a5ea3829bf7156481d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:56:44 +0100
Subject: [PATCH 367/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 85f15259166..8524e3e02b5 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -115,7 +115,7 @@ An important business metric should be:
 - Well-defined (the definition is agreed upon throughout the entire organization)
 - Time-bound (able to be compared across time)
 
-A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
+A great example of this is **revenue**. It can be aggregated on multiple levels (weekly, monthly, and so on) and is key for the broader business to understand.
 
 - ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
 - ❌ `1-off experimental metric`

From 21be43fa2cb5ea6e1fada6084fb6920369a597d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:57:07 +0100
Subject: [PATCH 368/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 8524e3e02b5..07fd5091e23 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -123,7 +123,7 @@ A great example of this is **revenue**. It can be aggregated on multiple levels
 
 ### Design and define metrics
 
-You can design and define your metrics in a `.yml` files nested under a metrics key in your dbt project, review the following documents: <br />
+You can design and define your metrics in `.yml` files nested under a metrics key in your dbt project. For more information, refer to these docs: <br />
 
 - [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics, and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
 - [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started

From 60c74ed27516287b4826b1437d02f59cfe88dc4f Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:57:30 +0100
Subject: [PATCH 369/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 07fd5091e23..1004e17e218 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -140,7 +140,7 @@ You can design and define your metrics in `.yml` files nested under a metrics ke
 <details>
   <summary>How are you storing my data?</summary>
   <div>
-    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
+    <div>The dbt Semantic Layer doesn't store, cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it's never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers to your connecting data tool.</div>
   </div>
 </details>
 <details>

From 0dc028b8ff5ffdd1aa36f225fceedb027efbe2d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:57:47 +0100
Subject: [PATCH 370/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 1004e17e218..249aa445c20 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -146,7 +146,7 @@ You can design and define your metrics in `.yml` files nested under a metrics ke
 <details>
   <summary>Is the dbt Semantic Layer open source?</summary>
   <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open sources. <br></br><br></br>
+    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open source. <br></br><br></br>
       
 During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
 

From 49449488e4d35e8d1b8104c6e7c41c87fa576414 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:58:31 +0100
Subject: [PATCH 371/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 249aa445c20..69cdd99dd35 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -148,7 +148,7 @@ You can design and define your metrics in `.yml` files nested under a metrics ke
   <div>
     <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL-licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open source. <br></br><br></br>
       
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
+During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers &mdash; Developer, Team, and Enterprise.<br></br><br></br>
 
 <ul>
 <li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>

From 468c1e29d1f00d25548a3e916b045d73f2eaec00 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:58:53 +0100
Subject: [PATCH 372/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 69cdd99dd35..c7073d7d783 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -151,7 +151,7 @@ You can design and define your metrics in `.yml` files nested under a metrics ke
 During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers &mdash; Developer, Team, and Enterprise.<br></br><br></br>
 
 <ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
+<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. Using the dbt Semantic Layer integrations requires a dbt Cloud account.
 <li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
 <li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
     </ul></div> </div>

From 18668b707b19e83babd465b079877e2941ac8b47 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:59:11 +0100
Subject: [PATCH 373/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index c7073d7d783..29a0709ca9b 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -159,7 +159,7 @@ During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers &md
 <details>
     <summary>Is there a dbt Semantic Layer discussion hub?</summary>
   <div>
-    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
+    <div>Yes, absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
     </div>
   </div>
 </details>

From f2ca38e88764ba0edc0c57740a4b8684cd382e2a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 10:28:06 +0100
Subject: [PATCH 374/703] update versions

---
 .../docs/docs/use-dbt-semantic-layer/dbt-sl.md  |  6 +-----
 website/snippets/_new-sl-setup.md               |  2 +-
 website/snippets/_v2-sl-prerequisites.md        | 17 +----------------
 3 files changed, 3 insertions(+), 22 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 29a0709ca9b..18cd61b6e12 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -150,11 +150,7 @@ You can design and define your metrics in `.yml` files nested under a metrics ke
       
 During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers &mdash; Developer, Team, and Enterprise.<br></br><br></br>
 
-<ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. Using the dbt Semantic Layer integrations requires a dbt Cloud account.
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
-    </ul></div> </div>
+ </div> </div>
 </details>
 <details>
     <summary>Is there a dbt Semantic Layer discussion hub?</summary>
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 693e2b80fe6..9b6ac963d62 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -20,7 +20,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 5. Select the deployment environment you want for the Semantic Layer and click **Save**.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="Set Up your Semantic Layer configuration."/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="To set up your Semantic Layer configuration, enter your credentials and select the deployment environment. This example selects a deployment environment named 'Production.'"/>
 
 6. You should see connection information that allows you to connect to downstream tools. 
 **Note:** If your tool supports JDBC, make sure to save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 2ee9c8257ea..95d563c9359 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -17,7 +17,7 @@ To use the Semantic Layer, you must:
 </VersionBlock>
 
 
-<VersionBlock lastVersion="1.5">
+<VersionBlock firstVersion="1.3" lastVersion="1.5" >
 
 - Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses">hosted</a> in North America <br />
 - Have both your production and development environments running dbt version 1.3 or higher <br />
@@ -26,20 +26,6 @@ To use the Semantic Layer, you must:
   * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
 - Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
   * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
-
-</VersionBlock>
-
-<VersionBlock firstVersion="1.3" lastVersion="1.3" >
-
-- Have a multi-tenant dbt Cloud instance, <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses">hosted</a> in North America <br />
-- Have both your production and development environments running dbt version 1.3 or higher <br />
-- Use Snowflake data platform <br />
-- Install the <a href="https://hub.getdbt.com/dbt-labs/metrics/latest/">dbt metrics package</a> version <code>>=1.3.0, <1.4.0</code> in your dbt project <br />
-  * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
-- Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
-  * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
 
 </VersionBlock>
 
@@ -52,6 +38,5 @@ To use the Semantic Layer, you must:
   * **Note** &mdash; After installing the dbt metrics package and updating the `packages.yml` file, make sure you run at least one model.
 - Set up the <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/discovery-api">Discovery API</a> in the integrated tool to import metric definitions 
   * Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API <br />
-- Recommended - Review the <a href="https://docs.getdbt.com/docs/build/metrics">dbt metrics page</a> and <a href="https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer">Understanding the components of the dbt Semantic Layer</a> blog <br />
 
 </VersionBlock>

From a7e1a4e36856d1df6f50389649d23b96a4f27ce5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 10:29:17 +0100
Subject: [PATCH 375/703] Update package-lock.json

---
 website/package-lock.json | 88771 ++++++++++++++++++------------------
 1 file changed, 44385 insertions(+), 44386 deletions(-)

diff --git a/website/package-lock.json b/website/package-lock.json
index 65c5f6ba759..9bb80cad9f9 100644
--- a/website/package-lock.json
+++ b/website/package-lock.json
@@ -1,44386 +1,44385 @@
-{
-  "name": "website",
-  "version": "0.0.0",
-  "lockfileVersion": 2,
-  "requires": true,
-  "packages": {
-    "": {
-      "version": "0.0.0",
-      "dependencies": {
-        "@docusaurus/core": "2.3.1",
-        "@docusaurus/plugin-ideal-image": "2.3.1",
-        "@docusaurus/preset-classic": "2.3.1",
-        "@docusaurus/theme-search-algolia": "2.3.1",
-        "@mdx-js/react": "^1.6.21",
-        "@monaco-editor/react": "^4.4.6",
-        "@stoplight/elements": "^7.7.17",
-        "@svgr/webpack": "^6.0.0",
-        "axios": "^0.27.2",
-        "build": "^0.1.4",
-        "classnames": "^2.3.1",
-        "clsx": "^1.1.1",
-        "color": "^3.1.2",
-        "core-js": "^3.20.0",
-        "feed": "^4.2.2",
-        "file-loader": "^6.2.0",
-        "fs": "0.0.2",
-        "gray-matter": "^4.0.3",
-        "hast-util-is-element": "^1.1.0",
-        "js-yaml": "^4.1.0",
-        "mobx": "^6.3.9",
-        "node-polyfill-webpack-plugin": "^1.1.4",
-        "papaparse": "^5.3.2",
-        "prism-react-renderer": "^1.2.1",
-        "query-string": "^8.1.0",
-        "raw-loader": "^4.0.2",
-        "react": "^17.0.1",
-        "react-dom": "^17.0.1",
-        "react-full-screen": "^1.1.1",
-        "react-is": "^18.1.0",
-        "react-tooltip": "^4.2.21",
-        "redoc": "^2.0.0-rc.57",
-        "rehype-katex": "^5.0.0",
-        "remark-math": "^3.0.1",
-        "sanitize-html": "^2.8.0",
-        "slugify": "^1.6.1",
-        "styled-components": "5.3.3",
-        "swiper": "^8.4.3",
-        "url-loader": "^4.1.1"
-      },
-      "devDependencies": {
-        "@cypress/react": "^7.0.2",
-        "@cypress/webpack-dev-server": "^3.2.3",
-        "@docusaurus/eslint-plugin": "2.3.1",
-        "@swc/core": "^1.2.174",
-        "@swc/jest": "^0.2.20",
-        "@testing-library/jest-dom": "^5.16.2",
-        "@testing-library/react": "^12.1.3",
-        "@testing-library/user-event": "^13.5.0",
-        "@typescript-eslint/eslint-plugin": "^5.54.0",
-        "@typescript-eslint/parser": "^5.54.0",
-        "css-loader": "^3.4.2",
-        "cypress": "^12.6.0",
-        "dotenv": "^10.0.0",
-        "eslint": "^8.35.0",
-        "eslint-config-prettier": "^8.6.0",
-        "eslint-plugin-markdown": "^3.0.0",
-        "eslint-plugin-prettier": "^4.2.1",
-        "husky": "^8.0.3",
-        "jest": "^27.5.1",
-        "jest-cli": "^27.5.1",
-        "lint-staged": "^13.1.2",
-        "path-browserify": "^1.0.1",
-        "process": "^0.11.10",
-        "stream-http": "^3.2.0",
-        "style-loader": "^1.1.3",
-        "svg-inline-loader": "^0.8.2",
-        "tty-browserify": "0.0.1",
-        "webpack": "^5.75.0",
-        "webpack-dev-server": "^4.11.1"
-      },
-      "optionalDependencies": {
-        "@dbt-labs/react-dbt-dag": "0.8.7"
-      }
-    },
-    "node_modules/@adobe/css-tools": {
-      "version": "4.2.0",
-      "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.2.0.tgz",
-      "integrity": "sha512-E09FiIft46CmH5Qnjb0wsW54/YQd69LsxeKUOWawmws1XWvyFGURnAChH0mlr7YPFR1ofwvUQfcL0J3lMxXqPA==",
-      "dev": true
-    },
-    "node_modules/@algolia/autocomplete-core": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.7.4.tgz",
-      "integrity": "sha512-daoLpQ3ps/VTMRZDEBfU8ixXd+amZcNJ4QSP3IERGyzqnL5Ch8uSRFt/4G8pUvW9c3o6GA4vtVv4I4lmnkdXyg==",
-      "dependencies": {
-        "@algolia/autocomplete-shared": "1.7.4"
-      }
-    },
-    "node_modules/@algolia/autocomplete-preset-algolia": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.7.4.tgz",
-      "integrity": "sha512-s37hrvLEIfcmKY8VU9LsAXgm2yfmkdHT3DnA3SgHaY93yjZ2qL57wzb5QweVkYuEBZkT2PIREvRoLXC2sxTbpQ==",
-      "dependencies": {
-        "@algolia/autocomplete-shared": "1.7.4"
-      },
-      "peerDependencies": {
-        "@algolia/client-search": ">= 4.9.1 < 6",
-        "algoliasearch": ">= 4.9.1 < 6"
-      }
-    },
-    "node_modules/@algolia/autocomplete-shared": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.7.4.tgz",
-      "integrity": "sha512-2VGCk7I9tA9Ge73Km99+Qg87w0wzW4tgUruvWAn/gfey1ZXgmxZtyIRBebk35R1O8TbK77wujVtCnpsGpRy1kg=="
-    },
-    "node_modules/@algolia/cache-browser-local-storage": {
-      "version": "4.16.0",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.16.0.tgz",
-      "integrity": "sha512-jVrk0YB3tjOhD5/lhBtYCVCeLjZmVpf2kdi4puApofytf/R0scjWz0GdozlW4HhU+Prxmt/c9ge4QFjtv5OAzQ==",
-      "dependencies": {
-        "@algolia/cache-common": "4.16.0"
-      }
-    },
-    "node_modules/@algolia/cache-common": {
-      "version": "4.16.0",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-common/-/cache-common-4.16.0.tgz",
-      "integrity": "sha512-4iHjkSYQYw46pITrNQgXXhvUmcekI8INz1m+SzmqLX8jexSSy4Ky4zfGhZzhhhLHXUP3+x/PK/c0qPjxEvRwKQ=="
-    },
-    "node_modules/@algolia/cache-in-memory": {
-      "version": "4.16.0",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.16.0.tgz",
-      "integrity": "sha512-p7RYykvA6Ip6QENxrh99nOD77otVh1sJRivcgcVpnjoZb5sIN3t33eUY1DpB9QSBizcrW+qk19rNkdnZ43a+PQ==",
-      "dependencies": {
-        "@algolia/cache-common": "4.16.0"
-      }
-    },
-    "node_modules/@algolia/client-account": {
-      "version": "4.16.0",
-      "resolved": "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.16.0.tgz",
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+}

From 40971bf81a37dde2c8d19bdcb9ad3a9eff8d5756 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 10:35:45 +0100
Subject: [PATCH 376/703] fix spacing

---
 website/snippets/_legacy-sl-callout.md | 2 +-
 website/snippets/_sl-plan-info.md      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_legacy-sl-callout.md b/website/snippets/_legacy-sl-callout.md
index 84c91895f89..f45c6b68af3 100644
--- a/website/snippets/_legacy-sl-callout.md
+++ b/website/snippets/_legacy-sl-callout.md
@@ -2,7 +2,7 @@
 
 The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), improving governance, introducing a new API, and making it more efficient to define and query metrics. The legacy Semantic Layer, available in dbt v1.5 or lower, is no longer supported and won't receive any code fixes.
 
-**Who does this affect?** Anyone who uses the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
+**Who does this affect?** Anyone who uses the legacy Semantic Layer. The new Semantic Layer is available to [Team or Enterprise](https://www.getdbt.com/pricing/) multi-tenant dbt Cloud plans [hosted in North America](/docs/cloud/about-cloud/regions-ip-addresses) (more regions coming soon). You must be on dbt v1.6 or higher to access it. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.
 
 **What’s changed?** The dbt_metrics package has been [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new framework for defining metrics in dbt. This means dbt_metrics is no longer supported after dbt v1.5 and won't receive any code fixes.
 
diff --git a/website/snippets/_sl-plan-info.md b/website/snippets/_sl-plan-info.md
index b5cc2ea0e4e..f1a721c90fc 100644
--- a/website/snippets/_sl-plan-info.md
+++ b/website/snippets/_sl-plan-info.md
@@ -1 +1 @@
-<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, you can access {props.product} on <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span> 
+<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, you can access {props.product} on <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span><br /><br />

From 0a93f4c21be246fcec8d015d67e27abc17734b6d Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 12:09:05 +0100
Subject: [PATCH 377/703] update data

---
 website/docs/docs/build/sl-getting-started.md   | 17 +++++++++++++++++
 .../06-July-2023/sl-revamp-beta.md              |  4 ++++
 .../use-dbt-semantic-layer/quickstart-sl.md     |  3 +--
 3 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index c56fa7b9a5e..0e6c9c87d04 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -230,6 +230,23 @@ To connect and query your metrics using the dbt Semantic Layer and its API:
 
 Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
+## FAQs
+
+If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
+    
+<details>
+  <summary>How do I migrate from the legacy Semantic Layer to the new one?</summary>
+  <div>
+    <div>If you're using the legacy Semantic Layer, we highly recommend you <a href="https://docs.getdbt.com/docs/dbt-versions/upgrade-core-in-cloud">upgrade your dbt version </a> to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated <a href="https://docs.getdbt.com/guides/migration/sl-migration"> migration guide</a> for more info.</div>
+  </div>
+</details>
+<details>
+<summary>How are you storing my data?</summary>
+User data passes through the Semantic Layer on its way back from the warehouse. dbt Labs ensures security by authenticating through the customer's data warehouse. Currently, we don't cache data for the long term, but it might temporarily stay in the system for up to 10 minutes, usually less. In the future, we'll introduce a caching feature that allows us to cache data on our infrastructure for up to 24 hours.
+</details>
+<details>
+<summary>Is the dbt Semantic Layer open source?</summary>
+The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 
 ## Related docs
 
diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index c29468199a8..9831f012d53 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -16,6 +16,10 @@ dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/do
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
+:::important
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+:::
+
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="75%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Enhanced dbt Semantic Layer
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index d780cbb44fe..e4f9810b58d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -182,12 +182,11 @@ If you're encountering some issues when defining your metrics or setting up the
 </details>
 <details>
 <summary>How are you storing my data?</summary>
-The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.
+User data passes through the Semantic Layer on its way back from the warehouse. dbt Labs ensures security by authenticating through the customer's data warehouse. Currently, we don't cache data for the long term, but it might temporarily stay in the system for up to 10 minutes, usually less. In the future, we'll introduce a caching feature that allows us to cache data on our infrastructure for up to 24 hours.
 </details>
 <details>
 <summary>Is the dbt Semantic Layer open source?</summary>
 The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
-
    
 ## Next steps
 

From 727c7f9813d43e0c2ed2b0691ca6122f2119bde6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 12:11:21 +0100
Subject: [PATCH 378/703] fix callout section

---
 .../release-notes/06-July-2023/sl-revamp-beta.md          | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
index 9831f012d53..a3123237074 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
@@ -7,6 +7,10 @@ date: 2023-07-31
 sidebar_position: 9
 ---
 
+:::important
+If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+:::
+
 dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), now available in [public beta](#public-beta). It aims to bring the best of modeling and semantics to downstream applications by introducing:
 
 - [MetricFlow](/docs/build/about-metricflow) is a framework for constructing performant and legible SQL from an all new set of semantic constructs which include semantic models, entities, and metrics.
@@ -16,10 +20,6 @@ dbt Labs are thrilled to announce the re-release of the [dbt Semantic Layer](/do
 
 With semantics at its core, the dbt Semantic Layer marks a crucial milestone towards a new era of centralized logic and data applications.
 
-:::important
-If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. To migrate to the new Semantic Layer, refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
-:::
-
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="75%" title="The universal dbt Semantic Layer connecting to integration tools."/>
 
 ## Enhanced dbt Semantic Layer

From d3b83d17335371db28cd0f261f3b6b65c318b4e5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 16:38:28 +0100
Subject: [PATCH 379/703] add callout and clarify versions

---
 website/docs/docs/collaborate/govern/model-versions.md | 4 ++++
 website/docs/reference/project-configs/version.md      | 5 +++++
 website/docs/reference/resource-properties/versions.md | 4 ++++
 website/snippets/_version-callout.md                   | 7 +++++++
 4 files changed, 20 insertions(+)
 create mode 100644 website/snippets/_version-callout.md

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index 12599d0b65f..0648bc3b8d6 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -3,8 +3,12 @@ title: "Model versions"
 id: model-versions
 sidebar_label: "Model versions"
 description: "Version models to help with lifecycle management"
+keyword: governance, model version, model versioning, dbt model versioning
 ---
 
+import VersionsCallout from '/snippets/_version-callout.md;'
+
+<VersionsCallout />
 
 :::info New functionality
 This functionality is new in v1.5 — if you have thoughts, participate in [the discussion on GitHub](https://github.com/dbt-labs/dbt-core/discussions/6736)!
diff --git a/website/docs/reference/project-configs/version.md b/website/docs/reference/project-configs/version.md
index 4c128727445..f153877f22b 100644
--- a/website/docs/reference/project-configs/version.md
+++ b/website/docs/reference/project-configs/version.md
@@ -1,8 +1,13 @@
 ---
 datatype: version
 required: True
+keyword: project version, project versioning, dbt project versioning
 ---
 
+import VersionsCallout from '/snippets/_version-callout.md;'
+
+<VersionsCallout />
+
 <VersionBlock firstVersion="1.5">
 
 dbt projects have two distinct types of the `version` tags. This field has a different meaning depending on its location.
diff --git a/website/docs/reference/resource-properties/versions.md b/website/docs/reference/resource-properties/versions.md
index 7e107ff31e3..be2145e0652 100644
--- a/website/docs/reference/resource-properties/versions.md
+++ b/website/docs/reference/resource-properties/versions.md
@@ -2,8 +2,12 @@
 resource_types: [models]
 datatype: list
 required: no
+keyword: governance, model version, model versioning, dbt model versioning
 ---
 
+import VersionsCallout from '/snippets/_version-callout.md;'
+
+<VersionsCallout />
 
 <File name='models/<schema>.yml'>
 
diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
new file mode 100644
index 00000000000..e5c21a7050d
--- /dev/null
+++ b/website/snippets/_version-callout.md
@@ -0,0 +1,7 @@
+
+::: important
+
+Take note that a version tag (or `version`) is different to [`versions`](/reference/resource-properties/versions_) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt (not required for dbt v1.5 and higher). 
+
+Model versions and `versions` are _features_ that enable better governance and data model management by allowing you to track changes and updates to models over time.  Model versions and `versions` are available in dbt v1.5 or higher.
+:::

From 164ddd458284461c89199b12e75cd2538e0e93e2 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 16:42:04 +0100
Subject: [PATCH 380/703] nits

---
 website/docs/docs/collaborate/govern/model-versions.md | 2 +-
 website/docs/reference/project-configs/version.md      | 2 +-
 website/docs/reference/resource-properties/versions.md | 2 +-
 website/snippets/_version-callout.md                   | 4 ++--
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index 0648bc3b8d6..c2956d74c7e 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -6,7 +6,7 @@ description: "Version models to help with lifecycle management"
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
-import VersionsCallout from '/snippets/_version-callout.md;'
+import VersionsCallout from '/snippets/_version-callout.md';
 
 <VersionsCallout />
 
diff --git a/website/docs/reference/project-configs/version.md b/website/docs/reference/project-configs/version.md
index f153877f22b..1c947412fcd 100644
--- a/website/docs/reference/project-configs/version.md
+++ b/website/docs/reference/project-configs/version.md
@@ -4,7 +4,7 @@ required: True
 keyword: project version, project versioning, dbt project versioning
 ---
 
-import VersionsCallout from '/snippets/_version-callout.md;'
+import VersionsCallout from '/snippets/_version-callout.md';
 
 <VersionsCallout />
 
diff --git a/website/docs/reference/resource-properties/versions.md b/website/docs/reference/resource-properties/versions.md
index be2145e0652..3b8aa7dd1b3 100644
--- a/website/docs/reference/resource-properties/versions.md
+++ b/website/docs/reference/resource-properties/versions.md
@@ -5,7 +5,7 @@ required: no
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
-import VersionsCallout from '/snippets/_version-callout.md;'
+import VersionsCallout from '/snippets/_version-callout.md';
 
 <VersionsCallout />
 
diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index e5c21a7050d..9652986f19f 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,7 +1,7 @@
 
-::: important
+:::important
 
-Take note that a version tag (or `version`) is different to [`versions`](/reference/resource-properties/versions_) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt (not required for dbt v1.5 and higher). 
+Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions_) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
 
 Model versions and `versions` are _features_ that enable better governance and data model management by allowing you to track changes and updates to models over time.  Model versions and `versions` are available in dbt v1.5 or higher.
 :::

From b8f2a080b436a6b427761dd7ece98cae01ad9f99 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:48:26 +0100
Subject: [PATCH 381/703] Update website/snippets/_version-callout.md

---
 website/snippets/_version-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 9652986f19f..7fba6752505 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,7 +1,7 @@
 
 :::important
 
-Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions_) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
+Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
 
 Model versions and `versions` are _features_ that enable better governance and data model management by allowing you to track changes and updates to models over time.  Model versions and `versions` are available in dbt v1.5 or higher.
 :::

From c5b9bcde876b27f60d49a978df9f2cde9d58cf94 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:51:39 +0100
Subject: [PATCH 382/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 106e228a003..a143c878dce 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -25,7 +25,12 @@ Similar to the [`manifest.json` file](/reference/artifacts/manifest-json), the `
 The `semantic_manifest.json` is produced whenever your dbt project is parsed. The easiest way to generate the file yourself is to run `dbt parse`. Since `dbt run`, `dbt build`, and `dbt compile` all parse your dbt project, these commands will generate a semantic manifest as well. 
 
 
-QUESTIONS:
+## Top level keys
+
+Top-level keys for the semantic manifest are:
+-  `semantic_models` &mdash; Starting points of data with entities, dimensions, and measures, and correspond to models in your dbt project. 
+-  `metrics` &mdash; Functions combining measures, constraints, and so on to define quantitative indicators.
+- `project_configuration` &mdash; Contains information around your project configurations 
 - WHAT IS IT PRODUCED BY?
 - WHAT ARE THE TOP LEVEL KEYS?
 - HOW DOES IT RELATED TO THE [JSON SCHEMA FILE](https://schemas.getdbt.com/)?

From 9575b9d04c5f705be2b9fb886cb725ce3328733a Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:52:13 +0100
Subject: [PATCH 383/703] Update sl-manifest.md

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index a143c878dce..adfe65c79bc 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -31,9 +31,6 @@ Top-level keys for the semantic manifest are:
 -  `semantic_models` &mdash; Starting points of data with entities, dimensions, and measures, and correspond to models in your dbt project. 
 -  `metrics` &mdash; Functions combining measures, constraints, and so on to define quantitative indicators.
 - `project_configuration` &mdash; Contains information around your project configurations 
-- WHAT IS IT PRODUCED BY?
-- WHAT ARE THE TOP LEVEL KEYS?
-- HOW DOES IT RELATED TO THE [JSON SCHEMA FILE](https://schemas.getdbt.com/)?
 
 <details>
 <summary>Example <code>target/semantic_manifest.json</code> file </summary>

From cb72aad55be7ad2e7d8e5a46ea96e499bc6356d3 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:52:34 +0100
Subject: [PATCH 384/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 7a5488a92ea..18184d6e670 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -12,7 +12,7 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 <NewSLChanges />
 
-With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
+With the dbt Semantic Layer, you can centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
 
 ## Prerequisites
 

From bfefcc0abdb4685e5be1c2fededd286e9c4a2839 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:52:48 +0100
Subject: [PATCH 385/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 18184d6e670..10cac28958f 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -16,7 +16,6 @@ With the dbt Semantic Layer, you can centrally define business metrics, reduce c
 
 ## Prerequisites
 
-Before you set up the dbt Semantic Layer, make sure you meet the following:
 
 import SetUp from '/snippets/_v2-sl-prerequisites.md';
 

From e0d3ff05a14c672313b74a994b3d4dad6c924a3c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:52:59 +0100
Subject: [PATCH 386/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 10cac28958f..f67dac7e980 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -46,7 +46,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 
 <LegacyInfo />
 
-With the dbt Semantic Layer, you'll be able to centrally define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
+With the dbt Semantic Layer, you can define business metrics, reduce code duplication and inconsistency, create self-service in downstream tools, and more. Configure the dbt Semantic Layer in dbt Cloud to connect with your integrated partner tool. 
 
 ## Prerequisites
 

From 633e6a4da6650a050e648a98cfabf71eaa426737 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:53:08 +0100
Subject: [PATCH 387/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index f67dac7e980..c5711304fba 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -50,7 +50,6 @@ With the dbt Semantic Layer, you can define business metrics, reduce code duplic
 
 ## Prerequisites
 
-Before you set up the dbt Semantic Layer, make sure you meet the following:
 
 <Snippet path="sl-prerequisites" />
 

From dd4335c90a2a46d77eb9785526ebcd7e41e73731 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:53:15 +0100
Subject: [PATCH 388/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index c5711304fba..0aaa301cb9c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -56,7 +56,7 @@ With the dbt Semantic Layer, you can define business metrics, reduce code duplic
 ## Set up dbt Semantic Layer
 
 :::tip
-If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
+If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the latest dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 
 :::
 

From fbb1fa684589cdccbb7d22398dfcd314d93a7fab Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:53:29 +0100
Subject: [PATCH 389/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 0aaa301cb9c..0f034b1547d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -60,7 +60,6 @@ If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you
 
 :::
 
-Before you continue with the following steps, you **must** have a multi-tenant dbt Cloud account hosted in North America. 
  * Team and Enterprise accounts can set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated partner tool to import metric definitions. 
  * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
 

From ae098f39cedfea375512dbd922909256b45aef6b Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:53:40 +0100
Subject: [PATCH 390/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 0f034b1547d..b1354d7f274 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -69,7 +69,7 @@ You can set up the dbt Semantic Layer in dbt Cloud at the environment level by f
 2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 
 3. Assign permissions to service account tokens depending on the integration tool you choose. You can review the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
 4. Go to **Deploy** and then **Environment**, and select your **Deployment** environment.
-5. Click on **Settings** on the top right side of the page.
+5. Click **Settings** on the top right side of the page.
 6. Click **Edit** on the top right side of the page.
 7. Select dbt version 1.2 or higher.
 8. Toggle the Semantic Layer **On**.

From 02645ada9e3207399defd89dc0a5c9874b1d7cad Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:55:06 +0100
Subject: [PATCH 391/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index b1354d7f274..c652146866d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -79,7 +79,7 @@ You can set up the dbt Semantic Layer in dbt Cloud at the environment level by f
 
 :::info📌 
 
-Note  - It is _not_ recommended that you use your dbt Cloud credentials due to elevated permissions. Instead, you can use your specific integration tool permissions.
+It is _not_ recommended that you use your dbt Cloud credentials due to elevated permissions. Instead, you can use your specific integration tool permissions.
 
 :::
 

From 7a2a689c79f566154ca1db1b94705860f7d8ecf3 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:55:15 +0100
Subject: [PATCH 392/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index c652146866d..b5dd00b025c 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -1,7 +1,7 @@
 ---
 title: "Set up the dbt Semantic Layer"
 id: setup-sl
-description: "Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive nagivation."
+description: "Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive navigation."
 sidebar_label: "Set up your Semantic Layer"
 tags: [Semantic Layer]
 ---

From 36f1dc8996fd058a7da327addaabe79003e70ebb Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:55:24 +0100
Subject: [PATCH 393/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index b5dd00b025c..8761c39b746 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -85,7 +85,7 @@ It is _not_ recommended that you use your dbt Cloud credentials due to elevated
 
 12. Set up the [Discovery API](/docs/dbt-cloud-apis/discovery-api) (Team and Enterprise accounts only) in the integrated partner tool to import the metric definitions. The [integrated partner tool](https://www.getdbt.com/product/semantic-layer-integrations) will treat the dbt Server as another data source (like a data platform). This requires:
 
-- The account ID, environment ID, and job ID (visible in the job URL)
+- The account ID, environment ID, and job ID (which is visible in the job URL)
 - An [API service token](/docs/dbt-cloud-apis/service-tokens) with job admin and metadata permissions
 - Add the items above to the relevant fields in your integration tool
 

From 299b4ad1e3dc4467d0155128221b2c444997d702 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:55:51 +0100
Subject: [PATCH 394/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 8761c39b746..43317a46bcb 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -63,7 +63,6 @@ If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you
  * Team and Enterprise accounts can set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated partner tool to import metric definitions. 
  * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
 
-You can set up the dbt Semantic Layer in dbt Cloud at the environment level by following these steps:
 
 1. Login to your dbt Cloud account
 2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 

From a8a517b394c1f9cb87dcdcb1e03b10b6cedc0060 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:56:05 +0100
Subject: [PATCH 395/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 43317a46bcb..ae8efe351ce 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -64,7 +64,7 @@ If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you
  * Developer accounts can query the Proxy Server using SQL but won't be able to browse dbt metrics in external tools, which requires access to the Discovery API.
 
 
-1. Login to your dbt Cloud account
+1. Log in to your dbt Cloud account.
 2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 
 3. Assign permissions to service account tokens depending on the integration tool you choose. You can review the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
 4. Go to **Deploy** and then **Environment**, and select your **Deployment** environment.

From 9ada74213119f0aec59e95fc42043b15d9767e41 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:56:20 +0100
Subject: [PATCH 396/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index ae8efe351ce..40bcbf17b2b 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -66,7 +66,7 @@ If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you
 
 1. Log in to your dbt Cloud account.
 2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 
-3. Assign permissions to service account tokens depending on the integration tool you choose. You can review the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
+3. Assign permissions to service account tokens depending on the integration tool you choose. Refer to the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
 4. Go to **Deploy** and then **Environment**, and select your **Deployment** environment.
 5. Click **Settings** on the top right side of the page.
 6. Click **Edit** on the top right side of the page.

From 479d023dab84455e8a74e67d7c0e189a8b8d869e Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 16:56:36 +0100
Subject: [PATCH 397/703] Update
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/setup-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
index 40bcbf17b2b..a2395d367e7 100644
--- a/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/setup-sl.md
@@ -67,7 +67,7 @@ If you're using the legacy Semantic Layer, dbt Labs strongly recommends that you
 1. Log in to your dbt Cloud account.
 2. Go to **Account Settings**, and then **Service Tokens** to create a new [service account API token](/docs/dbt-cloud-apis/service-tokens). Save your token somewhere safe. 
 3. Assign permissions to service account tokens depending on the integration tool you choose. Refer to the [integration partner documentation](https://www.getdbt.com/product/semantic-layer-integrations) to determine the permission sets you need to assign.
-4. Go to **Deploy** and then **Environment**, and select your **Deployment** environment.
+4. Go to **Deploy** > **Environments**, and select your **Deployment** environment.
 5. Click **Settings** on the top right side of the page.
 6. Click **Edit** on the top right side of the page.
 7. Select dbt version 1.2 or higher.

From 6e3b470aea631c71672630217ff82df0ca090049 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 17:12:33 +0100
Subject: [PATCH 398/703] Update model-versions.md

---
 website/docs/docs/collaborate/govern/model-versions.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index c2956d74c7e..e21f963057f 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -6,14 +6,15 @@ description: "Version models to help with lifecycle management"
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
-import VersionsCallout from '/snippets/_version-callout.md';
-
-<VersionsCallout />
-
 :::info New functionality
 This functionality is new in v1.5 — if you have thoughts, participate in [the discussion on GitHub](https://github.com/dbt-labs/dbt-core/discussions/6736)!
 :::
 
+
+import VersionsCallout from '/snippets/_version-callout.md';
+
+<VersionsCallout />
+
 Versioning APIs is a hard problem in software engineering. The root of the challenge is that the producers and consumers of an API have competing incentives:
 - Producers of an API need the ability to modify its logic and structure. There is a real cost to maintaining legacy endpoints forever, but losing the trust of downstream users is far costlier.
 - Consumers of an API need to trust in its stability: their queries will keep working, and won't break without warning. Although migrating to a newer API version incurs an expense, an unplanned migration is far costlier.

From 6d44f645346a403673ec03274f7c1b3153c93c86 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 27 Jul 2023 17:13:20 +0100
Subject: [PATCH 399/703] Update _version-callout.md

---
 website/snippets/_version-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 7fba6752505..5dc168abeef 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,5 +1,5 @@
 
-:::important
+:::tip
 
 Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
 

From f96ff78f5fbf97c52a0bb3d84e45559326c9deb6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 27 Jul 2023 17:20:19 +0100
Subject: [PATCH 400/703] add info header

---
 website/snippets/_version-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 5dc168abeef..40afbcfa8bf 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,5 +1,5 @@
 
-:::tip
+:::info Model versioning is different to a version tag
 
 Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
 

From b4b4d4fcf8eccbad43f9aa7fdb0bb69afc1bd00d Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:51:23 -0700
Subject: [PATCH 401/703] Update
 website/docs/docs/dbt-cloud-apis/sl-manifest.md

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index adfe65c79bc..b6a9b73b08b 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -83,6 +83,7 @@ Top-level keys for the semantic manifest are:
     }
 }
 ```
+
 </details>
 
 ## Related docs

From 6514bb778a273d71b31c088f91ad7f3aea296f49 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 27 Jul 2023 10:05:08 -0700
Subject: [PATCH 402/703] adding closing brackets

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index b6a9b73b08b..0034e61e6ab 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -81,6 +81,8 @@ Top-level keys for the semantic manifest are:
         "metadata": null,
         "dsi_package_version": {}
     }
+}
+    ]
 }
 ```
 

From a12dc40119981ed27623f3652fd01438e9922a7a Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 27 Jul 2023 10:52:38 -0700
Subject: [PATCH 403/703] adding valid json

---
 website/docs/docs/dbt-cloud-apis/sl-manifest.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-manifest.md b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
index 0034e61e6ab..47304accea3 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-manifest.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-manifest.md
@@ -57,7 +57,11 @@ Top-level keys for the semantic manifest are:
             "description": "metric description",
             "type": "metric type",
             "type_params": {
-                "measure": {"measures for the metric"},
+                "measure": {
+                    "name": "name for measure",
+                    "filter": "filter for measure",
+                    "alias": "alias for measure"
+                },
                 "numerator": null,
                 "denominator": null,
                 "expr": null,

From e1dbabfb4559fdf916f1b4445a5e698efae1f140 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Thu, 27 Jul 2023 15:32:31 -0400
Subject: [PATCH 404/703] More updates and changing to new style

---
 .../manage-access/enterprise-permissions.md      |  9 ++++-----
 ...table.md => _enterprise-permissions-table.md} | 16 ++++++++--------
 2 files changed, 12 insertions(+), 13 deletions(-)
 rename website/snippets/{enterprise-permissions-table.md => _enterprise-permissions-table.md} (94%)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index 6d1a7cfda17..cb338b3dc39 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -5,6 +5,7 @@ description: "Permission sets for Enterprise plans."
 hide_table_of_contents: true #For the sake of the tables on this page
 ---
 
+import Permissions from '/snippets/_enterprise-permissions-table.md';
 import SetUpPages from '/snippets/_available-enterprise-only.md';
 
 <SetUpPages features={'/snippets/_available-enterprise-only.md'}/>
@@ -14,13 +15,11 @@ help manage access controls within a dbt Cloud account. See the docs on [access
 control](/docs/cloud/manage-access/about-user-access) for more information on Role-Based access
 control (RBAC).
 
-## Permission Sets
+## Roles and permissions
 
-The following permission sets are available for assignment in dbt Cloud Enterprise accounts. They
-can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group
-can be associated with more than one permission set.
+The following roles and permission sets are available for assignment in dbt Cloud Enterprise accounts. They can be granted to dbt Cloud groups which are then in turn granted to users. A dbt Cloud group can be associated with more than one role and permission set. Roles with more access take precedence. 
 
-<Snippet path="enterprise-permissions-table" />
+<Permissions feature={'/snippets/_enterprise-permissions-table.md'} />
 
 ## Diagram of the Permission Sets
 
diff --git a/website/snippets/enterprise-permissions-table.md b/website/snippets/_enterprise-permissions-table.md
similarity index 94%
rename from website/snippets/enterprise-permissions-table.md
rename to website/snippets/_enterprise-permissions-table.md
index 3fec61264ac..413a669f104 100644
--- a/website/snippets/enterprise-permissions-table.md
+++ b/website/snippets/_enterprise-permissions-table.md
@@ -9,10 +9,10 @@ Permissions:
 * Account-level permissions &mdash; Permissions related to management of the dbt Cloud account. For example, billing and account settings.
 * Project-level permissions &mdash; Permissions related to the projects in dbt Cloud. For example, repos and access to the IDE. 
 
-## Account roles
+### Account roles
 Account roles are primarily designed for managing the dbt Cloud account. Permissions to alter the account settings (for example, generating service tokens, inviting users, configuring SSO). They may also have access to project-level permissions as well. The **Account Admin** role is the highest level of access that can be assigned.  
 
-### Account permissions
+#### Account permissions for account roles
 
 | Account-level permission| Account Admin | Billing admin | Project creator | Security admin | Viewer | 
 |:-------------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
@@ -28,13 +28,13 @@ Account roles are primarily designed for managing the dbt Cloud account. Permiss
 | Service tokens          |     W         |               |                 |       R        |        |
 | Webhooks                |     W         |               |                 |                |        |
 
-### Project permissions
+#### Project permissions for account roles
 
 |Project-level permission | Account Admin | Billing admin | Project creator | Security admin | Viewer | 
 |:-------------------------|:-------------:|:-------------:|:---------------:|:--------------:|:------:| 
 | Connections             |       W       |               |       W         |                |   R    |
 | Credentials             |       W       |               |       W         |                |   R    |
-| Custom env variables    |       W       |               |       W         |                |   R    |
+| Custom env. variables    |       W       |               |       W         |                |   R    |
 | dbt adapters            |       W       |               |       W         |                |   R    |
 | Develop (IDE)           |       W       |               |       W         |                |        |
 | Environments            |       W       |               |       W         |                |   R    |
@@ -50,11 +50,11 @@ Account roles are primarily designed for managing the dbt Cloud account. Permiss
 | Semantic Layer Config   |    W          |               |       W         |                |   R    |
 
 
-## Project role permissions
+### Project role permissions
 
 The project roles are intended for users who will be working within the projects in various capacities. They'll primarily have access to project-level permissions such as repos and the IDE, but may also have some account-level permisions.
 
-### Account permissions
+#### Account permissions for project roles
 
 | Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
@@ -68,13 +68,13 @@ The project roles are intended for users who will be working within the projects
 | Service tokens           |       |         |                |           |           |           |             |          |                |             |            |        |
 | Webhooks                 |   W   |         |                |    W      |           |           |             |          |                |             |            |    W   |
 
-### Project permissions
+#### Project permissions for project roles
 
 |Project-level permission  | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
 | Connections              |   W   |    R    |       W        |     R     |     R     |     R     |             |          |                |     R       |     R      |        |
 | Credentials              |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
-| Custom env variables     |  W    |    W    |       W        |     W     |     W     |     W     |      R      |          |                |     R       |     W      |        |
+| Custom env. variables     |  W    |    W    |       W        |     W     |     W     |     W     |      R      |          |                |     R       |     W      |        |
 | dbt adapters             |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
 | Develop (IDE)            |   W   |    W    |                |     W     |           |           |             |          |                |             |            |        |
 | Environments             |   W   |    R    |       R        |     R     |     R     |     W     |      R      |          |                |     R       |     R      |        |

From d53c1e19c59248583c4e8389344c7912ce40fac6 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 27 Jul 2023 15:31:16 -0700
Subject: [PATCH 405/703] Update
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/use-dbt-semantic-layer/dbt-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 18cd61b6e12..97fc1700634 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -18,7 +18,7 @@ The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), s
 
 Moving metric definitions out of the BI layer and into the modeling layer allows data teams to feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. 
 
-To learn more about why you need a universal Semantic Layer, refer to the [Why we need a universal semantic layer](https://www.getdbt.com/blog/universal-semantic-layer/)  blog post.
+Refer to the [Why we need a universal semantic layer](https://www.getdbt.com/blog/universal-semantic-layer/)  blog post to learn more.
 
 ## Explore the dbt Semantic Layer
 <!-- this partial lives here: https://github.com/dbt-labs/docs.getdbt.com/website/snippets/_sl-plan-info. Use it on diff pages and to tailor the message depending which instance can access the SL and what product lifecycle we're in. -->

From 70edfad1a7467bb503b4e7d9ee223f0bf4b94d94 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 27 Jul 2023 15:35:48 -0700
Subject: [PATCH 406/703] Fix typo (#3827)

## What are you changing in this pull request and why?

Fix `constraint` to `filter`

---------

Co-authored-by: winnie <91998347+gwenwindflower@users.noreply.github.com>
---
 website/docs/docs/build/metrics-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index a7f0d1d3d21..9f04cab1b82 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -15,7 +15,7 @@ The keys for metrics definitions are:
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
 | `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Optional |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
-| `constraint` | For any type of metric, you may optionally include a constraint string, which applies a dimensional filter when computing the metric. You may think of this as your WHERE clause.   | Required |
+| `filter` | For any type of metric, you may optionally include a filter string, which applies a dimensional filter when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |
 
 

From 6ade349541bb4a4e13ee0ab46756522eb02007be Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Thu, 27 Jul 2023 18:55:28 -0400
Subject: [PATCH 407/703] Update
 website/snippets/_enterprise-permissions-table.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_enterprise-permissions-table.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_enterprise-permissions-table.md b/website/snippets/_enterprise-permissions-table.md
index 413a669f104..e5c248d6bfa 100644
--- a/website/snippets/_enterprise-permissions-table.md
+++ b/website/snippets/_enterprise-permissions-table.md
@@ -10,7 +10,7 @@ Permissions:
 * Project-level permissions &mdash; Permissions related to the projects in dbt Cloud. For example, repos and access to the IDE. 
 
 ### Account roles
-Account roles are primarily designed for managing the dbt Cloud account. Permissions to alter the account settings (for example, generating service tokens, inviting users, configuring SSO). They may also have access to project-level permissions as well. The **Account Admin** role is the highest level of access that can be assigned.  
+Account roles enable you to manage the dbt Cloud account and manage the account settings (for example, generating service tokens, inviting users, configuring SSO). They also provide project-level permissions. The **Account Admin** role is the highest level of access you can assign.  
 
 #### Account permissions for account roles
 

From a18b67d065dad2bdd39c812e400899cc9fcc0287 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Thu, 27 Jul 2023 18:55:40 -0400
Subject: [PATCH 408/703] Update
 website/snippets/_enterprise-permissions-table.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/snippets/_enterprise-permissions-table.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_enterprise-permissions-table.md b/website/snippets/_enterprise-permissions-table.md
index e5c248d6bfa..75ced50dc2b 100644
--- a/website/snippets/_enterprise-permissions-table.md
+++ b/website/snippets/_enterprise-permissions-table.md
@@ -52,7 +52,7 @@ Account roles enable you to manage the dbt Cloud account and manage the account
 
 ### Project role permissions
 
-The project roles are intended for users who will be working within the projects in various capacities. They'll primarily have access to project-level permissions such as repos and the IDE, but may also have some account-level permisions.
+The project roles enable you to work within the projects in various capacities. They primarily provide access to project-level permissions such as repos and the IDE, but may also provide some account-level permissions.
 
 #### Account permissions for project roles
 

From 0b4adf1ffbd16f0253e39c876973eb5ad8600b86 Mon Sep 17 00:00:00 2001
From: Ha Pam <phamvanhan68@gmail.com>
Date: Fri, 28 Jul 2023 10:15:49 +0700
Subject: [PATCH 409/703] Update typo in 4-dbt-cloud-job-on-pr.md

---
 .../custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md b/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
index 8a6f8965b87..6d814ffcac6 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
+++ b/website/docs/guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr.md
@@ -89,7 +89,7 @@ As noted above, when the PR job runs it will create a new schema based on the PR
 
 Add this as a macro to your project. It takes 2 arguments that lets you control which schema get dropped:
   - `age_in_days`: The number of days since the schema was last altered before it should be dropped (default 10 days)
-  - `databse_to_clean`: The name of the database to remove schemas from
+  - `database_to_clean`: The name of the database to remove schemas from
   
 ```sql
 {# 
@@ -128,4 +128,4 @@ Add this as a macro to your project. It takes 2 arguments that lets you control
 
 This macro goes into a dbt Cloud job that is run on a schedule. The command will look like this (text below for copy/paste):
 ![dbt Cloud job showing the run operation command for the cleanup macro](/img/guides/orchestration/custom-cicd-pipelines/dbt-macro-cleanup-pr.png)
-`dbt run-operation pr_schema_cleanup --args "{ 'database_to_clean': 'development','age_in_days':15}"`
\ No newline at end of file
+`dbt run-operation pr_schema_cleanup --args "{ 'database_to_clean': 'development','age_in_days':15}"`

From ca23f66f48a176d4456770d3399e797f40fd1650 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Fri, 28 Jul 2023 09:05:56 -0700
Subject: [PATCH 410/703] Fix quickstart example

---
 website/snippets/quickstarts/change-way-model-materialized.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/quickstarts/change-way-model-materialized.md b/website/snippets/quickstarts/change-way-model-materialized.md
index 08d1b5ca0d8..2a21e561f07 100644
--- a/website/snippets/quickstarts/change-way-model-materialized.md
+++ b/website/snippets/quickstarts/change-way-model-materialized.md
@@ -1,6 +1,6 @@
 One of the most powerful features of dbt is that you can change the way a model is materialized in your warehouse, simply by changing a configuration value.  You can change things between tables and views by changing a keyword rather than writing the data definition language (DDL) to do this behind the scenes.
 
-By default, everything gets created as a view. You can override that by materializing everything in jaffle_shop as a table.  Everything in the example project will still be materialized as a view.
+By default, everything gets created as a view. You can override that by materializing everything in jaffle_shop as a table. Everything in the example project will also be materialized as a table.
 
 1. Edit your `dbt_project.yml` file.
     - Update your project `name` to:
@@ -20,7 +20,7 @@ By default, everything gets created as a view. You can override that by material
         jaffle_shop:
           +materialized: table
           example:
-            +materialized: view
+            +materialized: table
       ```
 
       </File>

From 681b658e648e013c6e484cab99dff9119df7ab09 Mon Sep 17 00:00:00 2001
From: Cameron Afzal <cameron.afzal@dbtlabs.com>
Date: Fri, 28 Jul 2023 09:16:57 -0700
Subject: [PATCH 411/703] Update docs for prod environment setting (#3815)

## What are you changing in this pull request and why?
Add new default production environment language and guidance

Default prod environment is a new setting for deployment environments.

Setting it is required to use new features like dbt Explorer and
cross-project refs (Mesh/multi-project) which require a single source of
truth for production metadata per dbt Cloud project.

We need to educate customers so they set this, especially once these
features are broadly available (currently all invite-only beta, feature
flagged in launchdarkly)

---------

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/dbt-cloud-environments.md        |  4 ++--
 website/docs/docs/deploy/deploy-environments.md    | 14 +++++++++++---
 .../environment-setup/1-env-guide-overview.md      |  2 +-
 .../2-one-deployment-environment.md                |  7 +++++--
 .../3-many-deployment-environments.md              |  7 +++++--
 website/snippets/_explorer-beta-banner.md          |  3 +++
 website/snippets/_explorer-beta-note.md            |  1 +
 7 files changed, 28 insertions(+), 10 deletions(-)
 create mode 100644 website/snippets/_explorer-beta-banner.md
 create mode 100644 website/snippets/_explorer-beta-note.md

diff --git a/website/docs/docs/dbt-cloud-environments.md b/website/docs/docs/dbt-cloud-environments.md
index 5eccf3e7400..f61ec5ef72b 100644
--- a/website/docs/docs/dbt-cloud-environments.md
+++ b/website/docs/docs/dbt-cloud-environments.md
@@ -42,6 +42,6 @@ To use the IDE, each developer will need to set up [personal development credent
 
 ## Deployment environment
 
-Deployment environments in dbt Cloud are crucial for executing scheduled jobs. A dbt Cloud project can have multiple deployment environments, allowing for flexibility and customization. 
+Deployment environments in dbt Cloud are necessary to execute scheduled jobs and use other features. A dbt Cloud project can have multiple deployment environments, allowing for flexibility and customization. However, a dbt Cloud project can only have one deployment environment that represents the production source of truth. 
 
-To learn more about dbt Cloud deployments and how to configure deployment environments, visit the [Deployment environments](/docs/deploy/deploy-environments) page. For our best practices guide, read [dbt Cloud environment best practices](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview) for more info. 
+To learn more about dbt Cloud deployment environments and how to configure them, visit the [Deployment environments](/docs/deploy/deploy-environments) page. For our best practices guide, read [dbt Cloud environment best practices](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview) for more info. 
diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md
index da54b918436..553a7d2fcdd 100644
--- a/website/docs/docs/deploy/deploy-environments.md
+++ b/website/docs/docs/deploy/deploy-environments.md
@@ -4,7 +4,7 @@ id: "deploy-environments"
 description: "Learn about dbt Cloud's deployment environment to seamlessly schedule jobs or enable CI."
 ---
 
-Deployment environments in dbt Cloud are crucial for deploying dbt jobs. To execute dbt, environments determine the settings used during job runs, including:
+Deployment environments in dbt Cloud are crucial for deploying dbt jobs in production and using features or integrations that depend on dbt metadata or results. To execute dbt, environments determine the settings used during job runs, including:
 
 - The version of dbt Core that will be used to run your project
 - The warehouse connection information (including the target database/schema settings)
@@ -15,8 +15,8 @@ A dbt Cloud project can have multiple deployment environments, providing you the
 :::tip Learn how to manage dbt Cloud environments
 To learn different approaches to managing dbt Cloud environments and recommendations for your organization's unique needs, read [dbt Cloud environment best practices](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview).
 ::: 
-
-This page will go over the different types of environments and how to intuitively configure your deployment environment in dbt Cloud. 
+ 
+This page reviews the different types of environments and how to configure your deployment environment in dbt Cloud. 
 
 import CloudEnvInfo from '/snippets/_cloud-environments-info.md';
 
@@ -28,6 +28,14 @@ To create a new dbt Cloud development environment, navigate to **Deploy** -> **E
 
 <Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/create-deploy-env.jpg" width="85%" title="Navigate to Deploy ->  Environments to create a deployment environment" />
 
+### Set as production environment (Beta)
+
+import ExpBeta from '/snippets/_explorer-beta-banner.md';  
+
+<ExpBeta/>
+
+In dbt Cloud, each project can have one designated deployment environment, which serves as its production environment. This production environment is _essential_ for using features like dbt Explorer and cross-project references. It acts as the source of truth for the project's production state in dbt Cloud.
+
 ### Semantic Layer
 
 For Semantic Layer-eligible customers, the next section of environment settings is the Semantic Layer configurations. [The Semantic Layer setup guide](/docs/use-dbt-semantic-layer/setup-dbt-semantic-layer) has the most up-to-date setup instructions!
diff --git a/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md b/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
index 17811b14ca3..844c895af98 100644
--- a/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
+++ b/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
@@ -39,7 +39,7 @@ Environments define the way that dbt will execute your code, including:
   - **Development** — the environment settings in which you work in the IDE on a development branch.
   - **Deployment** — the environment settings in which a dbt Cloud job runs.
 
-In this guide, we’re going to focus on **deployment environments**, which determine how your project is executed when a **dbt Cloud job executes**.
+In this guide, we’re going to focus on **deployment environments**, which determine how your project is executed when a **dbt Cloud job executes**. When using both approaches, make sure to designate one environment as "Production." This will allow you to use features such as dbt Explorer and cross-project references. Refer to [Set product environment](/docs/deploy/deploy-environments#set-as-production-environment-beta) for details.
 
 Depending on your git workflow and testing strategy, you'll be choosing between one deployment environment or many deployment environments. We provide a high-level overview of how these two deployment strategies work here, but use each section of this guide to get a deep-dive into how these setups differ. 
 
diff --git a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md b/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
index d7d64eda548..89bb05e7c75 100644
--- a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
+++ b/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
@@ -5,13 +5,16 @@ description: Learn how to configure a single deployment environment setup in dbt
 displayText: "dbt Cloud environment best practices"
 hoverSnippet: Learn how to configure a single deployment environment setup in dbt Cloud.
 ---
-
+import ExpNote from '/snippets/_explorer-beta-note.md';
 
 
 ## What this looks like
 
 1. You have a **single *development* environment** where dbt users can access the dbt Cloud IDE and make changes to their code on feature branches created off of your default branch in your repository (most often the `main` branch).
-2. You have a **single *deployment* environment** (let’s call it “Production”) where your scheduled jobs run referencing the `main` branch.
+2. You have a **single *deployment* environment** (let’s call it “Production”) where your scheduled jobs run referencing the `main` branch. <br />
+ 
+ <ExpNote/>
+
 3. You also have a [**Slim CI job**](/docs/deploy/continuous-integration) that kicks off anytime you open a PR to merge a feature branch into `main`. This Slim CI job can run in your dbt “Production” environment.
 
 :::info
diff --git a/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md b/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
index cf9f6954ca7..cb882d4ac1b 100644
--- a/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
+++ b/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
@@ -5,13 +5,16 @@ description: Learn how to configure a many deployment environment setup in dbt C
 displayText: "dbt Cloud environment best practices"
 hoverSnippet: Learn how to configure a many deployment environment setup in dbt Cloud.
 ---
-
+import ExpNote from '/snippets/_explorer-beta-note.md';
 
 ## What this looks like
 
 1. You have a **single *development* environment** where dbt users can access the dbt Cloud IDE and make changes to their code. However, you’ll want to update the **[custom branch settings](faqs/Environments/custom-branch-settings)** to ensure that developers create feature branches off of the a non-production branch. For this example, we’ll refer to this as the `qa` branch.
 2. You have a **QA deployment environment**, running scheduled jobs from the `qa` branch that deploys your dbt project to a pre-production warehouse location.
-3. You have a **Production deployment environment,** running scheduled jobs from the `main` branch that deploys your dbt project to your production warehouse location.
+3. You have a **Production deployment environment,** running scheduled jobs from the `main` branch that deploys your dbt project to your production warehouse location. <br />
+
+ <ExpNote/>
+ 
 4. You have **multiple Slim CI jobs** (one in each deployment environment) to ensure changes to each branch are tested.  
 
 <Lightbox src="/img/guides/best-practices/environment-setup/many-deployments-table.png" title="Table of basic setup for many deployment environment" />
diff --git a/website/snippets/_explorer-beta-banner.md b/website/snippets/_explorer-beta-banner.md
new file mode 100644
index 00000000000..ab501c7bd0f
--- /dev/null
+++ b/website/snippets/_explorer-beta-banner.md
@@ -0,0 +1,3 @@
+:::info Beta
+This feature is related to dbt Explorer and cross-project references [beta](/docs/dbt-versions/product-lifecycles#dbt-cloud) projects and subject to change. If you are interested in getting access to the beta, please [contact us](mailto:support@getdbt.com).
+:::
diff --git a/website/snippets/_explorer-beta-note.md b/website/snippets/_explorer-beta-note.md
new file mode 100644
index 00000000000..3bdcd5bcf7a
--- /dev/null
+++ b/website/snippets/_explorer-beta-note.md
@@ -0,0 +1 @@
+**Note:** Make sure to set the environment to "Production" so you can take advantage of features like dbt Explorer and cross-project references. Refer to [Set product environment](/docs/deploy/deploy-environments#set-as-production-environment-beta) for details.

From 3163ba3dd18b7aa27361d34d06f3c7174d9bc989 Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Fri, 28 Jul 2023 11:27:15 -0500
Subject: [PATCH 412/703] Update change-way-model-materialized.md

---
 website/snippets/quickstarts/change-way-model-materialized.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/quickstarts/change-way-model-materialized.md b/website/snippets/quickstarts/change-way-model-materialized.md
index 2a21e561f07..9fff4fdfa91 100644
--- a/website/snippets/quickstarts/change-way-model-materialized.md
+++ b/website/snippets/quickstarts/change-way-model-materialized.md
@@ -1,6 +1,6 @@
 One of the most powerful features of dbt is that you can change the way a model is materialized in your warehouse, simply by changing a configuration value.  You can change things between tables and views by changing a keyword rather than writing the data definition language (DDL) to do this behind the scenes.
 
-By default, everything gets created as a view. You can override that by materializing everything in jaffle_shop as a table. Everything in the example project will also be materialized as a table.
+By default, everything gets created as a view. You can override that by materializing everything in a directory as a different materialization. Everything in the example project will now be materialized as a view, while everything above it in the root models folder will be a table.
 
 1. Edit your `dbt_project.yml` file.
     - Update your project `name` to:
@@ -20,7 +20,7 @@ By default, everything gets created as a view. You can override that by material
         jaffle_shop:
           +materialized: table
           example:
-            +materialized: table
+            +materialized: view
       ```
 
       </File>

From e575a33a842c210459ca765875d984b4092e5220 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Fri, 28 Jul 2023 10:01:05 -0700
Subject: [PATCH 413/703] Incorporate feedback

---
 website/snippets/quickstarts/change-way-model-materialized.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/quickstarts/change-way-model-materialized.md b/website/snippets/quickstarts/change-way-model-materialized.md
index 9fff4fdfa91..d8468bcf3bf 100644
--- a/website/snippets/quickstarts/change-way-model-materialized.md
+++ b/website/snippets/quickstarts/change-way-model-materialized.md
@@ -1,6 +1,6 @@
 One of the most powerful features of dbt is that you can change the way a model is materialized in your warehouse, simply by changing a configuration value.  You can change things between tables and views by changing a keyword rather than writing the data definition language (DDL) to do this behind the scenes.
 
-By default, everything gets created as a view. You can override that by materializing everything in a directory as a different materialization. Everything in the example project will now be materialized as a view, while everything above it in the root models folder will be a table.
+By default, everything gets created as a view. You can override that at the directory level so everything in that directory will materialize to a different materialization.
 
 1. Edit your `dbt_project.yml` file.
     - Update your project `name` to:
@@ -11,7 +11,7 @@ By default, everything gets created as a view. You can override that by material
       ```
 
       </File>
-    - Update your `models` config block to:
+    - Configure `jaffle_shop` so everything in it will be materialized as a table; and configure `example` so everything in it will be materialized as a view. Update your `models` config block to:
 
       <File name='dbt_project.yml'>
 

From 71b9b5cfc646ea59776c23b7fff21683676a7eb8 Mon Sep 17 00:00:00 2001
From: bethanyhipple-dbtlabs
 <108838013+bethanyhipple-dbtlabs@users.noreply.github.com>
Date: Fri, 28 Jul 2023 11:25:59 -0700
Subject: [PATCH 414/703] Update pr-template.md

updated the Bit Bucket PR template URL to include the destination variable.
---
 website/docs/docs/collaborate/git/pr-template.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/collaborate/git/pr-template.md b/website/docs/docs/collaborate/git/pr-template.md
index 83d620b7af9..ddb4948dad9 100644
--- a/website/docs/docs/collaborate/git/pr-template.md
+++ b/website/docs/docs/collaborate/git/pr-template.md
@@ -72,7 +72,7 @@ https://gitlab.com/<org>/<repo>/-/merge_requests/new?merge_request[source_branch
 
 ### BitBucket
 ```
-https://bitbucket.org/<org>/<repo>/pull-requests/new?source={{source}}
+https://bitbucket.org/<org>/<repo>/pull-requests/new?source={{source}}&dest={{destination}}
 ```
 
 ### AWS CodeCommit

From 6c8e2422cfc5695e01258b106f4519d942326b56 Mon Sep 17 00:00:00 2001
From: amada-ech <94760092+amada-ech@users.noreply.github.com>
Date: Fri, 28 Jul 2023 14:51:49 -0400
Subject: [PATCH 415/703] Update alan-cruickshank.md

---
 website/docs/community/spotlight/alan-cruickshank.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/community/spotlight/alan-cruickshank.md b/website/docs/community/spotlight/alan-cruickshank.md
index 52bfb821402..74ef95a2b61 100644
--- a/website/docs/community/spotlight/alan-cruickshank.md
+++ b/website/docs/community/spotlight/alan-cruickshank.md
@@ -36,6 +36,8 @@ I also see SQLFluff as a vehicle to drive more consistency in the way we write S
 
 For better or worse, I spend most of my day job on people and organisational things, less on how to solve individual problems, and more on how to enable and support groups of people in being able to make great decisions themselves. In some ways, if I have to touch the keyboard too much, it's a sign that I've failed in that calling. dbt itself is a tool which enables better collaboration—and the community is full of people with great ideas on how to better enable other people around us. I hope that I'm able to pass some of that knowledge and the experience of applying it in a scaleup environment back to others also treading this path.
 
+More specifically from the dbt community, if I were to pick one recommendation, it would be Emilie Schario’s talk from Coalesce 2022 on [“Data Led is Dumb”](https://www.youtube.com/watch?v=WsMHPALc8Vg&t=1s). I think should be essential watching for anyone who’s hearing “Data Led” a lot, and wants to turn that excitement into practical action.
+
 ## Anything else interesting you want to tell us?
 
 If you're not using SQLFluff on your dbt project, you probably should be: https://github.com/sqlfluff/sqlfluff

From 166638e33c627aed60c1a6b0769a1e41c9b9a43a Mon Sep 17 00:00:00 2001
From: amada-ech <94760092+amada-ech@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:38:04 -0400
Subject: [PATCH 416/703] Update jing-yu-lim.md

---
 website/docs/community/spotlight/jing-yu-lim.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/community/spotlight/jing-yu-lim.md b/website/docs/community/spotlight/jing-yu-lim.md
index 862e2a80de9..363d9a0216e 100644
--- a/website/docs/community/spotlight/jing-yu-lim.md
+++ b/website/docs/community/spotlight/jing-yu-lim.md
@@ -2,14 +2,14 @@
 id: jing-yu-lim
 title: Jing Yu Lim
 description: |
-  For ~3 years, I was a Product Analyst at Grab, a ride-hailing and food delivery app in Southeast Asia, before taking on an Analytics Engineering role in Spenmo, a B2B Fintech startup. I recently made the switch back to an Analyst role when I joined ExpressVPN, a VPN service for both consumers and businesses. I'm also one of the co-organisers of the <a href="https://www.meetup.com/singapore-dbt-meetup/" title="Singapore dbt Meetup" rel="noopener noreferrer" target="_blank">Singapore dbt Meetup</a>! 
+  For ~3 years, I was a Product Analyst at Grab, a ride-hailing and food delivery app in Southeast Asia, before taking on an Analytics Engineering role in Spenmo, a B2B Fintech startup. I joined a tech company in June 2023, but was recently impacted by a layoff. I'm also one of the co-organisers of the <a href="https://www.meetup.com/singapore-dbt-meetup/" title="Singapore dbt Meetup" rel="noopener noreferrer" target="_blank">Singapore dbt Meetup</a>! 
 
   My story with dbt started in Jan 2022, when I joined Spenmo where I taught myself dbt, mainly via <a href="https://docs.getdbt.com/docs/introduction" title="dbt's documentation">dbt's documentation</a> and <a href="https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___" title="Slack community" rel="noopener noreferrer" target="_blank">Slack community</a>. We used Snowflake as our data warehouse, and Holistics for BI. I spoke about data self-serve and Spenmo's journey with dbt at multiple meetups.
 image: /img/community/spotlight/jing-lim.jpg
 pronouns: she/her
 location: Singapore, Singapore
-jobTitle: Senior Data Analyst
-companyName: ExpressVPN
+jobTitle: I'm open to work!
+companyName: #
 organization: ""
 socialLinks:
   - name: LinkedIn

From 986e1954c9410611e3efc7b0fcce2335e602e177 Mon Sep 17 00:00:00 2001
From: amada-ech <94760092+amada-ech@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:39:12 -0400
Subject: [PATCH 417/703] Update jing-yu-lim.md

---
 website/docs/community/spotlight/jing-yu-lim.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/community/spotlight/jing-yu-lim.md b/website/docs/community/spotlight/jing-yu-lim.md
index 363d9a0216e..d176798106c 100644
--- a/website/docs/community/spotlight/jing-yu-lim.md
+++ b/website/docs/community/spotlight/jing-yu-lim.md
@@ -9,7 +9,7 @@ image: /img/community/spotlight/jing-lim.jpg
 pronouns: she/her
 location: Singapore, Singapore
 jobTitle: I'm open to work!
-companyName: #
+companyName: ""
 organization: ""
 socialLinks:
   - name: LinkedIn

From 1fc837bc0c869cf91220597b9b5fb377b000f9c6 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Fri, 28 Jul 2023 14:57:20 -0700
Subject: [PATCH 418/703] tweak examples in quickstart

---
 website/docs/docs/build/sl-getting-started.md | 100 +++++++++++++-----
 1 file changed, 72 insertions(+), 28 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 0e6c9c87d04..d7bcee80d88 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -50,11 +50,11 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 
 The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
 
-It's highly recommended you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the Jaffle shop example, delete the `orders.yaml` semantic model or delete the .yaml extension so it's ignored during parsing. You'll be rebuilding it step by step in this example. 
+We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the [Jaffle shop example]((https://github.com/dbt-labs/jaffle-sl-template)), delete the `orders.yml` config or delete the .yml extension so it's ignored during parsing. **We'll be rebuilding it step by step in this example.** 
 
 If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
 
-1. Create a new semantic model file, such as `orders.yaml`. 
+1. Create a new yml config file for the orders model, such as `orders.yml`. 
 
 It's best practice to create semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name and appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now, `default_agg_time_dimension` is the only supported default. 
 
@@ -62,12 +62,12 @@ It's best practice to create semantic models in the `/models/semantic_models` di
 semantic_models:
   #The name of the semantic model.
   - name: orders
+    defaults:
+      agg_time_dimension: ordered_at
     description: |
-      Model containing order data. The grain of the table is the order id.
+      Order fact table. This table is at the order grain with one row per order. 
     #The name of the dbt model and schema
     model: ref('orders')
-    defaults:
-      agg_time_dimension: ordered_at
   ```
 
 2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `order_id`, and so on.
@@ -88,19 +88,41 @@ semantic_models:
 3. Define your dimensions and measures. Dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
 
 ```yaml
-  #Measures. These are the aggregations on the columns in the table.
-    measures:
-          - name: order_total
-            agg: sum
-  #Dimensions, either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+    #Measures. These are the aggregations on the columns in the table.
+    measures: 
+      - name: order_total
+        description: The total revenue for each order.
+        agg: sum
+      - name: order_count
+        expr: 1
+        agg: sum
+      - name: tax_paid
+        description: The toal tax paid on each order. 
+        agg: sum
+      - name: customers_with_orders
+        description: Distinct count of customers placing orders
+        agg: count_distinct
+        expr: customer_id
+      - name: locations_with_orders
+        description: Distinct count of locations with order
+        expr: location_id
+        agg: count_distinct
+      - name: order_cost
+        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
+        agg: sum
+  #Dimensions. Either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.  
     dimensions:
-      - name: location_name
-        type: categorical
       - name: ordered_at
-        expr: cast(ordered_at as date)
         type: time
         type_params:
-          time_granularity: day
+          time_granularity: day 
+      - name: order_total_dim
+        type: categorical
+        expr: order_total
+      - name: is_food_order
+        type: categorical
+      - name: is_drink_order
+        type: categorical  
 ```
 
 Putting it all together, a complete semantic model configurations based on the order model would look like the following example:
@@ -109,13 +131,13 @@ Putting it all together, a complete semantic model configurations based on the o
 semantic_models:
   #The name of the semantic model.
   - name: orders
+    defaults:
+      agg_time_dimension: ordered_at
     description: |
-      Model containing order data. The grain of the table is the order id.
+      Order fact table. This table is at the order grain with one row per order. 
     #The name of the dbt model and schema
     model: ref('orders')
-    defaults:
-      agg_time_dimension: ordered_at
-    #Entities. These usually correspond to keys in the table.
+    #Entities. These usually corespond to keys in the table.
     entities:
       - name: order_id
         type: primary
@@ -126,18 +148,40 @@ semantic_models:
         type: foreign
         expr: customer_id
     #Measures. These are the aggregations on the columns in the table.
-    measures:
+    measures: 
       - name: order_total
+        description: The total revenue for each order.
+        agg: sum
+      - name: order_count
+        expr: 1
+        agg: sum
+      - name: tax_paid
+        description: The toal tax paid on each order. 
         agg: sum
-    #Dimensions, either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+      - name: customers_with_orders
+        description: Distinct count of customers placing orders
+        agg: count_distinct
+        expr: customer_id
+      - name: locations_with_orders
+        description: Distinct count of locations with order
+        expr: location_id
+        agg: count_distinct
+      - name: order_cost
+        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
+        agg: sum
+    #Dimensions. Either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.  
     dimensions:
-      - name: location_name
-        type: categorical
       - name: ordered_at
-        expr: cast(ordered_at as date)
         type: time
         type_params:
-          time_granularity: day
+          time_granularity: day 
+      - name: order_total_dim
+        type: categorical
+        expr: order_total
+      - name: is_food_order
+        type: categorical
+      - name: is_drink_order
+        type: categorical  
 ```
 
 :::tip
@@ -160,14 +204,14 @@ Now that you've created your first semantic model, it's time to define your firs
 
 1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line. 
 
-2. The example metric you'll create is a simple metric that refers directly to the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, delete the `example_metrics` file or remove the .yaml extension so it's ignored during parsing. You'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
+2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the original `orders.yml` file, or removing the .yml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
 
 ```yaml
 metrics:
   - name: order_total
-    description: "Sum of orders value"
+    description: Sum of total order amonunt. Includes tax + revenue.
     type: simple
-    label: "Order Total"
+    label: Order Total
     type_params:
       measure: order_total
 ```
@@ -192,7 +236,7 @@ This section will explain how you can test and query metrics locally. Before you
 
 **Query and commit your metrics using the CLI:**
 
-MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt build` before preceding to ensure the data exists in your warehouse. 
+MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before preceding to ensure the data exists in your warehouse. 
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.

From 84e0c418fa3a8a3050644d47020f89f3f2320d3a Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:04:19 -0700
Subject: [PATCH 419/703] commenting out guide links

---
 .../docs/dbt-cloud-apis/sl-api-overview.md    |  2 +-
 website/docs/guides/migration/sl-migration.md | 20 +++++++++++++------
 2 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
index 1ff2fce17be..efe54cbd833 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-api-overview.md
@@ -13,7 +13,7 @@ import LegacyInfo from '/snippets/_legacy-sl-callout.md';
 <LegacyInfo />
 
 </VersionBlock>
-
+ 
 The rapid growth of different tools in the modern data stack has helped data professionals address the diverse needs of different teams. The downside of this growth is the fragmentation of business logic across teams, tools, and workloads.
 
 The [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) allows you to define metrics in code (with [MetricFlow](/docs/build/about-metricflow)) and dynamically generate and query datasets in downstream tools based on their dbt governed assets, such as metrics and models. Integrating with the dbt Semantic Layer will help organizations that use your product make more efficient and trustworthy decisions with their data. It also helps you to avoid duplicative coding, optimize development workflow, ensure data governance, and guarantee consistency for data consumers.  
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 43facab9d50..da7d2691210 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -50,7 +50,9 @@ To help ensure a smooth transition, use our [custom-built migration tool](https:
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-* Note, if you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
+:::info
+If you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
+:::
 
 #### Test your metrics
 
@@ -86,12 +88,14 @@ Ensure that all jobs run successfully with the new version.
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
-**Link to migration guide for Hex**
+<!---
+@mirnawong1 commenting out until these are available
 
+ **Link to migration guide for Hex**
 
 
 **Link to migration guide for Mode**
-
+----->
 
 ## Create environment for the new Semantic Layer
 
@@ -128,11 +132,12 @@ To migrate to the new specification, you'll need to:
 - Upgrade your existing metrics configs to the new spec
 
 To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
-**Note**, this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
+**Note:** this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
 
 For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
 
-* Note, if you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
+
+If you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
 
 #### Test your metrics
 
@@ -152,10 +157,13 @@ Once you've thoroughly tested and verified the accuracy of your metrics, commit
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
 
-**Link to migration guide for Hex**
+<!---
+@mirnawong1 commenting out until these are available
 
+ **Link to migration guide for Hex**
 
 **Link to migration guide for Mode**
+----->
 
 
 ## Resources

From cc7ad387117b5d0c94559ea6285ea74d014c7b58 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:14:17 -0700
Subject: [PATCH 420/703] fixing build err

---
 website/docs/docs/build/sl-getting-started.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index d7bcee80d88..986b1f2e500 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,7 +8,7 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
-This getting started page presents a sample workflow to help you create your first metrics. It uses the [Jaffle shop example](https://github.com/dbt-labs/jaffle-sl-template) as the project data source and is available for you to use. If you prefer, you can create semantic models and metrics for your own dbt project. 
+This getting started page presents a sample workflow to help you create your first metrics. It uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) as the project data source and is available for you to use. If you prefer, you can create semantic models and metrics for your own dbt project.
 
 To fully experience the power of a universal dbt Semantic Layer, take the following steps:
 
@@ -97,7 +97,7 @@ semantic_models:
         expr: 1
         agg: sum
       - name: tax_paid
-        description: The toal tax paid on each order. 
+        description: The total tax paid on each order. 
         agg: sum
       - name: customers_with_orders
         description: Distinct count of customers placing orders
@@ -110,7 +110,7 @@ semantic_models:
       - name: order_cost
         description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
         agg: sum
-  #Dimensions. Either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.  
+  #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
     dimensions:
       - name: ordered_at
         type: time
@@ -156,7 +156,7 @@ semantic_models:
         expr: 1
         agg: sum
       - name: tax_paid
-        description: The toal tax paid on each order. 
+        description: The total tax paid on each order. 
         agg: sum
       - name: customers_with_orders
         description: Distinct count of customers placing orders
@@ -169,7 +169,7 @@ semantic_models:
       - name: order_cost
         description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
         agg: sum
-    #Dimensions. Either categorical or time. These add additonal context to metrics. The typical querying pattern is Metric by Dimension.  
+    #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
     dimensions:
       - name: ordered_at
         type: time
@@ -200,7 +200,7 @@ group by metric_time_day, country  -- dimensions
 
 ## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](https://docs.getdbt.com/docs/build/metrics-overview) before getting started. 
+Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](/docs/build/metrics-overview) before getting started. 
 
 1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line. 
 

From 433f4d65cdd0e31ef1401a72124a094548767a56 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Fri, 28 Jul 2023 15:16:45 -0700
Subject: [PATCH 421/703] fixing build err

---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 986b1f2e500..dbd953b2a5c 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -50,7 +50,7 @@ Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platfor
 
 The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
 
-We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the [Jaffle shop example]((https://github.com/dbt-labs/jaffle-sl-template)), delete the `orders.yml` config or delete the .yml extension so it's ignored during parsing. **We'll be rebuilding it step by step in this example.** 
+We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the [Jaffle shop example](https://github.com/dbt-labs/jaffle-sl-template), delete the `orders.yml` config or delete the .yml extension so it's ignored during parsing. **We'll be rebuilding it step by step in this example.** 
 
 If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
 

From 927709899729b735da48f4f95e3f1406bf327460 Mon Sep 17 00:00:00 2001
From: Grant Murray <grant@open.store>
Date: Mon, 31 Jul 2023 06:41:42 -0400
Subject: [PATCH 422/703] patch(constraints): fix-typo

---
 website/docs/reference/resource-properties/constraints.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index b25893729e5..51e10e028ab 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -228,7 +228,7 @@ select
 Snowflake suppports four types of constraints: `unique`, `not null`, `primary key` and `foreign key`.
 
 It is important to note that only the `not null` (and the `not null` property of `primary key`) are actually checked today.
-There rest of the constraints are purely metadata, not verified when inserting data.
+The rest of the constraints are purely metadata, not verified when inserting data.
 
 Currently, Snowflake doesn't support the `check` syntax and dbt will skip the `check` config and raise a warning message if it is set on some models in the dbt project.
 

From 750254cefaec2bbce3daee115d039e1c725c190e Mon Sep 17 00:00:00 2001
From: Grant Murray <grant@open.store>
Date: Mon, 31 Jul 2023 07:39:18 -0400
Subject: [PATCH 423/703] patch: fix-typo-in-quickstart-semantic-layer

---
 .../docs/use-dbt-semantic-layer/quickstart-semantic-layer.md    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
index af8de189fa9..aa490511004 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
@@ -8,7 +8,7 @@ sidebar_label: "Quickstart"
 :::info Coming soon
 The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
 
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
+**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new framework for defining metrics in dbt.
 
 **What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
 :::

From 71f98c98dce1beb93788d9466dface82d086d198 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Mon, 31 Jul 2023 09:20:54 -0400
Subject: [PATCH 424/703] Update discovery-querying.md

---
 website/docs/docs/dbt-cloud-apis/discovery-querying.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/discovery-querying.md b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
index 77fed109c68..9791ee5af3d 100644
--- a/website/docs/docs/dbt-cloud-apis/discovery-querying.md
+++ b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
@@ -63,8 +63,7 @@ There are several illustrative example queries on this page. For more examples,
 ## Reasonable use
 
 Discovery (GraphQL) API usage is subject to request rate and response size limits to maintain the performance and stability of the metadata platform and prevent abuse.
-- The current request rate limit is 200 requests for a given IP address within a minute. If you exceed this limit, you will receive an HTTP 429 response status.
-- Environment-level endpoints will be subject to response size limits in the future. The depth of the graph should not exceed three levels. A user can paginate up to 500 items per query.
+
 - Job-level endpoints are subject to query complexity limits. Nested nodes (like parents), code (like rawCode), and catalog columns are considered as most complex. Overly complex queries should be broken up into separate queries with only necessary fields included. dbt Labs recommends using the environment endpoint instead for most use cases to get the latest descriptive and result metadata for a dbt Cloud project.
 
 ## Retention limits

From a1d5b58de6d6bacdc0bd6e31ea2cdb2f9ddbf532 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 13:38:24 -0400
Subject: [PATCH 425/703] 1.6 GA Docs changes

---
 website/dbt-versions.js                             |  1 -
 .../migration/versions/01-upgrading-to-v1.6.md      | 10 +---------
 .../migration/versions/02-upgrading-to-v1.5.md      |  2 +-
 website/snippets/core-versions-table.md             | 13 +++++++------
 4 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 01d1bf5d128..82de0ad6333 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -2,7 +2,6 @@ exports.versions = [
   {
     version: "1.6",
     EOLDate: "2024-07-31",
-    isPrerelease: true
   },
   {
     version: "1.5",
diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index cb1e9af603d..6859e183362 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -1,16 +1,8 @@
 ---
-title: "Upgrading to v1.6 (prerelease)"
+title: "Upgrading to v1.6 (latest)"
 description: New features and changes in dbt Core v1.6
 ---
 
-:::warning Prerelease
-
-dbt Core v1.6 is available as a release candidate. [Final release is planned for July 31.](https://github.com/dbt-labs/dbt-core/issues/7990)
-
-Test it out, and [let us know](https://github.com/dbt-labs/dbt-core/issues/new/choose) if you run into any issues!
-
-:::
-
 ## Resources
 
 - [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.6.latest/CHANGELOG.md)
diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index bb793edf4c7..5283070217c 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -1,5 +1,5 @@
 ---
-title: "Upgrading to v1.5 (latest)"
+title: "Upgrading to v1.5"
 description: New features and changes in dbt Core v1.5
 ---
 
diff --git a/website/snippets/core-versions-table.md b/website/snippets/core-versions-table.md
index 6997353545b..fb2e2a5d60e 100644
--- a/website/snippets/core-versions-table.md
+++ b/website/snippets/core-versions-table.md
@@ -1,14 +1,15 @@
 ### Latest Releases
 
 | dbt Core                                                   | Initial Release | Support Level | Critical Support Until  | 
-|------------------------------------------------------------|-----------------|---------------|-------------------------|
-| [**v1.5**](/guides/migration/versions/upgrading-to-v1.5)   | Apr 27, 2023    | Active        | Apr 27, 2024            | 
-| [**v1.4**](/guides/migration/versions/upgrading-to-v1.4)   | Jan 25, 2023    | Critical      | Jan 25, 2024            | 
-| [**v1.3**](/guides/migration/versions/upgrading-to-v1.3)   | Oct 12, 2022    | Critical      | Oct 12, 2023            | 
-| [**v1.2**](/guides/migration/versions/upgrading-to-v1.2)   | Jul 26, 2022    | Critical      | Jul 26, 2023            |
+|------------------------------------------------------------|-----------------|----------------|-------------------------|
+| [**v1.6**](/guides/migration/versions/upgrading-to-v1.6)   | Jul 31, 2023    | Active         | Jul 30, 2024            |
+| [**v1.5**](/guides/migration/versions/upgrading-to-v1.5)   | Apr 27, 2023    | Critical       | Apr 27, 2024            | 
+| [**v1.4**](/guides/migration/versions/upgrading-to-v1.4)   | Jan 25, 2023    | Critical       | Jan 25, 2024            | 
+| [**v1.3**](/guides/migration/versions/upgrading-to-v1.3)   | Oct 12, 2022    | Critical       | Oct 12, 2023            | 
+| [**v1.2**](/guides/migration/versions/upgrading-to-v1.2)   | Jul 26, 2022    | End of Life* ⚠️ | Jul 26, 2023            |
 | [**v1.1**](/guides/migration/versions/upgrading-to-v1.1) ⚠️ | Apr 28, 2022    | End of Life* ⚠️ | Apr 28, 2023            | 
 | [**v1.0**](/guides/migration/versions/upgrading-to-v1.0) ⚠️ | Dec 3, 2021     | End of Life* ⚠️ | Dec 3, 2022 ⚠️           | 
-|  **v0.X** ⛔️                                                | (Various dates) | Deprecated ⛔️ | Deprecated ⛔️            | 
+|  **v0.X** ⛔️                                               | (Various dates) | Deprecated ⛔️  | Deprecated ⛔️            | 
 _*All versions of dbt Core since v1.0 are available in dbt Cloud until further notice. Versions that are EOL do not receive any fixes. For the best support, we recommend upgrading to a version released within the past 12 months._
 ### Planned future releases
 

From cf12adf724a3c7d31b2be57e6c95db66e7cf9e28 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 14:16:08 -0400
Subject: [PATCH 426/703] Update 01-upgrading-to-v1.6.md

---
 website/docs/guides/migration/versions/01-upgrading-to-v1.6.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index 6859e183362..ac3d7348ef9 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -18,7 +18,7 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 
 - dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
 - As part of the Semantic layer re-launch (in beta), the spec for `metrics` has changed significantly. Migration guide coming soon: https://github.com/dbt-labs/docs.getdbt.com/pull/3705
-- Manifest schema version is now v10, reflecting [TODO] changes
+- The manifest schema version is now v10.
 
 ### For consumers of dbt artifacts (metadata)
 

From 11072636633d4325ab6e8003086cd492b6b010ad Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Mon, 31 Jul 2023 11:21:19 -0700
Subject: [PATCH 427/703] Test 3

---
 website/docs/docs/dbt-support.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-support.md b/website/docs/docs/dbt-support.md
index a6e9262200c..f63e016b03e 100644
--- a/website/docs/docs/dbt-support.md
+++ b/website/docs/docs/dbt-support.md
@@ -5,7 +5,7 @@ id: "dbt-support"
 
 ## dbt Core support
 
-If you're developing in the command line (CLI) and have questions or need some help &mdash; reach out to the helpful dbt community through [the Community Forum](https://discourse.getdbt.com/) or [dbt Community slack](https://www.getdbt.com/community/join-the-community/).
+If you're developing on the command line (CLI) and have questions or need some help &mdash; reach out to the helpful dbt community through [the Community Forum](https://discourse.getdbt.com/) or [dbt Community slack](https://www.getdbt.com/community/join-the-community/).
 
 ## dbt Cloud support
 

From b95e35ff5c9346f0807cfcff628db0030de10297 Mon Sep 17 00:00:00 2001
From: amada-ech <94760092+amada-ech@users.noreply.github.com>
Date: Mon, 31 Jul 2023 15:59:57 -0400
Subject: [PATCH 428/703] Update jing-yu-lim.md

---
 website/docs/community/spotlight/jing-yu-lim.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/community/spotlight/jing-yu-lim.md b/website/docs/community/spotlight/jing-yu-lim.md
index d176798106c..a3d1784293f 100644
--- a/website/docs/community/spotlight/jing-yu-lim.md
+++ b/website/docs/community/spotlight/jing-yu-lim.md
@@ -2,7 +2,7 @@
 id: jing-yu-lim
 title: Jing Yu Lim
 description: |
-  For ~3 years, I was a Product Analyst at Grab, a ride-hailing and food delivery app in Southeast Asia, before taking on an Analytics Engineering role in Spenmo, a B2B Fintech startup. I joined a tech company in June 2023, but was recently impacted by a layoff. I'm also one of the co-organisers of the <a href="https://www.meetup.com/singapore-dbt-meetup/" title="Singapore dbt Meetup" rel="noopener noreferrer" target="_blank">Singapore dbt Meetup</a>! 
+  For ~3 years, I was a Product Analyst at Grab, a ride-hailing and food delivery app in Southeast Asia, before taking on an Analytics Engineering role in Spenmo, a B2B Fintech startup. I joined a tech company as an analyst in June 2023, but was recently impacted by a layoff. I'm also one of the co-organisers of the <a href="https://www.meetup.com/singapore-dbt-meetup/" title="Singapore dbt Meetup" rel="noopener noreferrer" target="_blank">Singapore dbt Meetup</a>! 
 
   My story with dbt started in Jan 2022, when I joined Spenmo where I taught myself dbt, mainly via <a href="https://docs.getdbt.com/docs/introduction" title="dbt's documentation">dbt's documentation</a> and <a href="https://www.getdbt.com/community/join-the-community/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2024_dbt-spotlight_aw&utm_content=____&utm_term=all___" title="Slack community" rel="noopener noreferrer" target="_blank">Slack community</a>. We used Snowflake as our data warehouse, and Holistics for BI. I spoke about data self-serve and Spenmo's journey with dbt at multiple meetups.
 image: /img/community/spotlight/jing-lim.jpg

From db455e3d40a8bb62f8fb6c0564d6d741bca62846 Mon Sep 17 00:00:00 2001
From: Grant Murray <grant@open.store>
Date: Mon, 31 Jul 2023 07:39:18 -0400
Subject: [PATCH 429/703] patch: fix-typo-in-quickstart-semantic-layer

---
 .../docs/use-dbt-semantic-layer/quickstart-semantic-layer.md    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
index af8de189fa9..aa490511004 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-semantic-layer.md
@@ -8,7 +8,7 @@ sidebar_label: "Quickstart"
 :::info Coming soon
 The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
 
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
+**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new framework for defining metrics in dbt.
 
 **What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
 :::

From 4d00e3b5a523f612d80a40eefa951660cc6f10aa Mon Sep 17 00:00:00 2001
From: Grant Murray <grant@open.store>
Date: Mon, 31 Jul 2023 06:41:42 -0400
Subject: [PATCH 430/703] patch(constraints): fix-typo

---
 website/docs/reference/resource-properties/constraints.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index b25893729e5..51e10e028ab 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -228,7 +228,7 @@ select
 Snowflake suppports four types of constraints: `unique`, `not null`, `primary key` and `foreign key`.
 
 It is important to note that only the `not null` (and the `not null` property of `primary key`) are actually checked today.
-There rest of the constraints are purely metadata, not verified when inserting data.
+The rest of the constraints are purely metadata, not verified when inserting data.
 
 Currently, Snowflake doesn't support the `check` syntax and dbt will skip the `check` config and raise a warning message if it is set on some models in the dbt project.
 

From 2f0dfac65a5ab6f6caa7555438d7ec0da6338b3f Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 13:38:24 -0400
Subject: [PATCH 431/703] 1.6 GA Docs changes

---
 website/dbt-versions.js                             |  1 -
 .../migration/versions/01-upgrading-to-v1.6.md      | 10 +---------
 .../migration/versions/02-upgrading-to-v1.5.md      |  2 +-
 website/snippets/core-versions-table.md             | 13 +++++++------
 4 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/website/dbt-versions.js b/website/dbt-versions.js
index 01d1bf5d128..82de0ad6333 100644
--- a/website/dbt-versions.js
+++ b/website/dbt-versions.js
@@ -2,7 +2,6 @@ exports.versions = [
   {
     version: "1.6",
     EOLDate: "2024-07-31",
-    isPrerelease: true
   },
   {
     version: "1.5",
diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index cb1e9af603d..6859e183362 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -1,16 +1,8 @@
 ---
-title: "Upgrading to v1.6 (prerelease)"
+title: "Upgrading to v1.6 (latest)"
 description: New features and changes in dbt Core v1.6
 ---
 
-:::warning Prerelease
-
-dbt Core v1.6 is available as a release candidate. [Final release is planned for July 31.](https://github.com/dbt-labs/dbt-core/issues/7990)
-
-Test it out, and [let us know](https://github.com/dbt-labs/dbt-core/issues/new/choose) if you run into any issues!
-
-:::
-
 ## Resources
 
 - [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.6.latest/CHANGELOG.md)
diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index bb793edf4c7..5283070217c 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -1,5 +1,5 @@
 ---
-title: "Upgrading to v1.5 (latest)"
+title: "Upgrading to v1.5"
 description: New features and changes in dbt Core v1.5
 ---
 
diff --git a/website/snippets/core-versions-table.md b/website/snippets/core-versions-table.md
index 6997353545b..fb2e2a5d60e 100644
--- a/website/snippets/core-versions-table.md
+++ b/website/snippets/core-versions-table.md
@@ -1,14 +1,15 @@
 ### Latest Releases
 
 | dbt Core                                                   | Initial Release | Support Level | Critical Support Until  | 
-|------------------------------------------------------------|-----------------|---------------|-------------------------|
-| [**v1.5**](/guides/migration/versions/upgrading-to-v1.5)   | Apr 27, 2023    | Active        | Apr 27, 2024            | 
-| [**v1.4**](/guides/migration/versions/upgrading-to-v1.4)   | Jan 25, 2023    | Critical      | Jan 25, 2024            | 
-| [**v1.3**](/guides/migration/versions/upgrading-to-v1.3)   | Oct 12, 2022    | Critical      | Oct 12, 2023            | 
-| [**v1.2**](/guides/migration/versions/upgrading-to-v1.2)   | Jul 26, 2022    | Critical      | Jul 26, 2023            |
+|------------------------------------------------------------|-----------------|----------------|-------------------------|
+| [**v1.6**](/guides/migration/versions/upgrading-to-v1.6)   | Jul 31, 2023    | Active         | Jul 30, 2024            |
+| [**v1.5**](/guides/migration/versions/upgrading-to-v1.5)   | Apr 27, 2023    | Critical       | Apr 27, 2024            | 
+| [**v1.4**](/guides/migration/versions/upgrading-to-v1.4)   | Jan 25, 2023    | Critical       | Jan 25, 2024            | 
+| [**v1.3**](/guides/migration/versions/upgrading-to-v1.3)   | Oct 12, 2022    | Critical       | Oct 12, 2023            | 
+| [**v1.2**](/guides/migration/versions/upgrading-to-v1.2)   | Jul 26, 2022    | End of Life* ⚠️ | Jul 26, 2023            |
 | [**v1.1**](/guides/migration/versions/upgrading-to-v1.1) ⚠️ | Apr 28, 2022    | End of Life* ⚠️ | Apr 28, 2023            | 
 | [**v1.0**](/guides/migration/versions/upgrading-to-v1.0) ⚠️ | Dec 3, 2021     | End of Life* ⚠️ | Dec 3, 2022 ⚠️           | 
-|  **v0.X** ⛔️                                                | (Various dates) | Deprecated ⛔️ | Deprecated ⛔️            | 
+|  **v0.X** ⛔️                                               | (Various dates) | Deprecated ⛔️  | Deprecated ⛔️            | 
 _*All versions of dbt Core since v1.0 are available in dbt Cloud until further notice. Versions that are EOL do not receive any fixes. For the best support, we recommend upgrading to a version released within the past 12 months._
 ### Planned future releases
 

From 6c504495afeb31d5cb4719aef4862f02d6c50e73 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 14:16:08 -0400
Subject: [PATCH 432/703] Update 01-upgrading-to-v1.6.md

---
 website/docs/guides/migration/versions/01-upgrading-to-v1.6.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index 6859e183362..ac3d7348ef9 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -18,7 +18,7 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 
 - dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
 - As part of the Semantic layer re-launch (in beta), the spec for `metrics` has changed significantly. Migration guide coming soon: https://github.com/dbt-labs/docs.getdbt.com/pull/3705
-- Manifest schema version is now v10, reflecting [TODO] changes
+- The manifest schema version is now v10.
 
 ### For consumers of dbt artifacts (metadata)
 

From f9edcb1657e1a5a613f6fa090c6147edb5a5f48a Mon Sep 17 00:00:00 2001
From: Jason Karlavige <jkarlavige@gmail.com>
Date: Mon, 31 Jul 2023 16:31:22 -0400
Subject: [PATCH 433/703] update featured spotlight member

---
 website/docusaurus.config.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js
index af285961145..24030624290 100644
--- a/website/docusaurus.config.js
+++ b/website/docusaurus.config.js
@@ -79,7 +79,7 @@ var siteSettings = {
     announcementBarLink: "https://www.getdbt.com/resources/dbt-cloud-demos-with-experts/?utm_medium=event&utm_source=docs&utm_campaign=q1-2024_cloud-demos-with-experts_awareness",
     // Set community spotlight member on homepage
     // This is the ID for a specific file under docs/community/spotlight
-    communitySpotlightMember: "david-effiong",
+    communitySpotlightMember: "faith-lierheimer",
     prism: {
       theme: (() => {
         var theme = require("prism-react-renderer/themes/nightOwl");

From 6711cdfcdd343d67e634e9f71b2f06ccf776cea1 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 17:20:02 -0400
Subject: [PATCH 434/703] Update
 website/docs/reference/resource-configs/postgres-configs.md

---
 website/docs/reference/resource-configs/postgres-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index 2e603ba8111..d7348d9a4f3 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -153,7 +153,7 @@ models:
 Below are current limitations that we hope to address in a future release.
 #### Changing materialization to and from "materialized_view"
 
-Swapping an already materialized model to be a materialized view and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again. Normally, re-running with the `--full-refresh` flag would resolve this, but not in this case.
+Swapping an already materialized model to a materialized view and vice versa. The workaround is manually dropping the existing materialization in the data warehouse before calling `dbt run` again. Normally, re-running with the `--full-refresh` flag would resolve this, but not in this case.
 
 To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="materialized_view"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
 

From 335e5346f657502a815153e3d6183d33051cb08e Mon Sep 17 00:00:00 2001
From: Grace Goheen <grace.goheen@dbtlabs.com>
Date: Mon, 31 Jul 2023 14:54:36 -0700
Subject: [PATCH 435/703] remove retry from cloud supported commands

---
 website/docs/reference/commands/retry.md | 6 ++++++
 website/docs/reference/dbt-commands.md   | 1 -
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/retry.md b/website/docs/reference/commands/retry.md
index 0c010ede2c1..692b2f6b744 100644
--- a/website/docs/reference/commands/retry.md
+++ b/website/docs/reference/commands/retry.md
@@ -4,6 +4,12 @@ sidebar_label: "retry"
 id: "retry"
 ---
 
+:::warning dbt Cloud support coming soon
+
+Native support for `dbt retry` is coming to dbt Cloud soon.
+
+:::
+
 `dbt retry` re-executes the last `dbt` command from the node point of failure. If the previously executed `dbt` command was successful, `retry` will finish as `no operation`. 
 
 Retry works with the following commands:
diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 5b37f13a3fb..1a45ea47a3d 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -24,7 +24,6 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project
-- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
 - [run](/reference/commands/run): runs the models in a project
 - [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
 - [seed](/reference/commands/seed): loads CSV files into the database

From ec0accd8001f2fc7331e2ca7df33f86999c17534 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Mon, 31 Jul 2023 15:19:17 -0700
Subject: [PATCH 436/703] Update
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md

remove extra underscore
---
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index aad0883f014..bfb27f3420c 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -57,7 +57,7 @@ models:
     config: ...
 ```
 
-Some options that could previously be specified _after_ a sub-command can now only be specified _before__. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
+Some options that could previously be specified _after_ a sub-command can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
 
 | --cache-selected-only | --debug | --deprecated-print | --enable-legacy-logger| --fail-fast |
 |--------------|--------------|-----------------|-----------|---------------------------|

From e19dba6f4681bdbefd52cb0935d473ca3bd02748 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 31 Jul 2023 20:10:54 -0400
Subject: [PATCH 437/703] Updating table headers

---
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index bfb27f3420c..ebcf74bc5a7 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -59,8 +59,9 @@ models:
 
 Some options that could previously be specified _after_ a sub-command can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
 
-| --cache-selected-only | --debug | --deprecated-print | --enable-legacy-logger| --fail-fast |
+|              |              |                 |           |                           |
 |--------------|--------------|-----------------|-----------|---------------------------|
+| --cache-selected-only | --debug | --deprecated-print | --enable-legacy-logger| --fail-fast |
 | --log-cache-events  | --log-format | --log-level | --log-path | --macro-debugging |
 | --partial-parse      | --populate-cache | --print      | --printer-width | --quiet |
 | --record-timing-info | --send-anonymous-usage-stats | --single-threaded | --static-parser | --use-colors |

From 6cb3dbeefcdd794623087dcb5443dfb525544cfc Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:03:51 -0400
Subject: [PATCH 438/703] Update
 website/docs/reference/resource-configs/postgres-configs.md

---
 website/docs/reference/resource-configs/postgres-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index d7348d9a4f3..a8d5af2301e 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -155,7 +155,7 @@ Below are current limitations that we hope to address in a future release.
 
 Swapping an already materialized model to a materialized view and vice versa. The workaround is manually dropping the existing materialization in the data warehouse before calling `dbt run` again. Normally, re-running with the `--full-refresh` flag would resolve this, but not in this case.
 
-To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="materialized_view"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
+For example, assume the model below, `my_model`, has already been materialized to the underlying data platform via `dbt run`. If a user changes the model's config to `materialized="materialized_view"`, they will get an error. The solution is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
 
 <File name='my_model.sql'>
 

From ab88c681a1079e320a2fcda09e9773714739878d Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:05:25 -0400
Subject: [PATCH 439/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 0991afc21db..8f874e67cdd 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -138,7 +138,7 @@ models:
 
 ### Limitations
 
-Below are current limitations that we hope to address in a future release.
+We hope to address the following limitations in a future release.
 #### Changing materialization from "materialized_view" to table or view
 
 Swapping a materialized view to be a table or view is not supported. The workaround is to manually drop the existing materialized view in the data warehouse before calling `dbt run` again.

From 106b9a8157a68e396ac4185df15801de7e9fcb0d Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:05:33 -0400
Subject: [PATCH 440/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 8f874e67cdd..2e1cddeaf1c 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -141,7 +141,7 @@ models:
 We hope to address the following limitations in a future release.
 #### Changing materialization from "materialized_view" to table or view
 
-Swapping a materialized view to be a table or view is not supported. The workaround is to manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
+Swapping a materialized view to a table or view is not supported. You must manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
 
 To illustrate, assume for a materialized view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="view"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCASE` on the data warehouse before trying the model again.
 

From cde65f0942b3036bb8c915f85feee31bc7caddd2 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:07:04 -0400
Subject: [PATCH 441/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 2e1cddeaf1c..18e4da73225 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -143,6 +143,6 @@ We hope to address the following limitations in a future release.
 
 Swapping a materialized view to a table or view is not supported. You must manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
 
-To illustrate, assume for a materialized view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="view"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCASE` on the data warehouse before trying the model again.
+For example, assume that a view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="table"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCASE` on the data warehouse before trying the model again.
 
 </VersionBlock>

From 7e6a62b4cd64c19e3c2f675ae72e5f605c45c9fd Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:08:58 -0400
Subject: [PATCH 442/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 9644a1539f5..78575f87c31 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -346,7 +346,7 @@ In the configuration format for the model SQL file:
 
 ## Dynamic Tables
 
-[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's flavor of Materialized Views.
+[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's version of Materialized Views.
 
 ### Parameters
 

From 24436a2cdb1e1f69c4f429ad1d09cc4aef24767b Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:10:17 -0400
Subject: [PATCH 443/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 78575f87c31..6c0bc69f6ab 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -399,7 +399,7 @@ models:
 
 Swapping an already materialized model to be a dynamic table and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again.
 
-To illustrate, assume for the example model below, `my_model`, that it has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="dynamic_table"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
+For example, assume for the example model below, `my_model`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="dynamic_table"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
 
 <File name='my_model.sql'>
 

From 3322d14e9b0641b325ebafd0488a37fb3c7bc26a Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:10:31 -0400
Subject: [PATCH 444/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 6c0bc69f6ab..1d6d2e8858a 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -397,7 +397,7 @@ models:
 
 #### Changing materialization to and from "dynamic_table"
 
-Swapping an already materialized model to be a dynamic table and vise versa. The workaround is the manually drop the existing materialization in the data warehouse before calling `dbt run` again.
+Swapping an already materialized model to be a dynamic table and vice versa. The workaround is manually dropping the existing materialization in the data warehouse before calling `dbt run` again.
 
 For example, assume for the example model below, `my_model`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="dynamic_table"`, they will get an error. The workaround is to execute `DROP TABLE my_model` on the data warehouse before trying the model again.
 

From 2df94ec57d3b7d14c3aba480bb459e23f16c9abd Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:11:39 -0400
Subject: [PATCH 445/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 1d6d2e8858a..2420b032b88 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -413,7 +413,6 @@ For example, assume for the example model below, `my_model`, has already been ma
 
 </File>
 
-#### Altering `target_lag` or `warehouse`
 
 Currently, changing either the `target_lag` or `warehouse` parameters for a pre-existing Dynamic Table (DT) will result in the Dynamic Table being dropped and replaced, rather than altered.
 

From 49d6eb44096316ad5e14d4c17d6f96464546bdd2 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:11:46 -0400
Subject: [PATCH 446/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 2420b032b88..b3ef3f484fc 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -414,6 +414,5 @@ For example, assume for the example model below, `my_model`, has already been ma
 </File>
 
 
-Currently, changing either the `target_lag` or `warehouse` parameters for a pre-existing Dynamic Table (DT) will result in the Dynamic Table being dropped and replaced, rather than altered.
 
 </VersionBlock>

From d84c5ff0294d19cec9fca59466436944439bd6fe Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:13:43 -0400
Subject: [PATCH 447/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index b3ef3f484fc..12851f48a69 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -352,7 +352,7 @@ In the configuration format for the model SQL file:
 
 dbt-snowflake requires the following parameters:
 
-- `TARGET_LAG`
+- `target_lag`
 - `WAREHOUSE`
 
 To learn more about each parameter and what values it can take, see 

From 11b4edb740c5341609527c0210d6479226fe541c Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:14:06 -0400
Subject: [PATCH 448/703] Apply suggestions from code review

---
 .../docs/reference/resource-configs/snowflake-configs.md    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 12851f48a69..d5526a03bcd 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -353,7 +353,7 @@ In the configuration format for the model SQL file:
 dbt-snowflake requires the following parameters:
 
 - `target_lag`
-- `WAREHOUSE`
+- `snowflake_warehouse`
 
 To learn more about each parameter and what values it can take, see 
 the Snowflake docs page: [`CREATE DYNAMIC TABLE: Parameters`](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table)
@@ -373,7 +373,7 @@ The following examples create a dynamic table:
 {{
   config(
     materialized = 'dynamic_table',
-    warehouse = 'MY_WAREHOUSE',
+    warehouse = 'snowflake_warehouse',
     target_lag = '10 minutes',
   )
 }}
@@ -387,7 +387,7 @@ The following examples create a dynamic table:
 models:
   path:
     materialized: dynamic_table
-    warehouse: MY_WAREHOUSE
+    warehouse: snowflake_warehouse
     target_lag: '10 minutes'
 ```
 

From 6e2426d7e6d5563a8208d814d0785947b9437bc5 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:15:34 -0400
Subject: [PATCH 449/703] Update
 website/docs/reference/resource-configs/snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index d5526a03bcd..a79326743a2 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -356,7 +356,7 @@ dbt-snowflake requires the following parameters:
 - `snowflake_warehouse`
 
 To learn more about each parameter and what values it can take, see 
-the Snowflake docs page: [`CREATE DYNAMIC TABLE: Parameters`](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table)
+the Snowflake docs page: [`CREATE DYNAMIC TABLE: Parameters`](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table). Note that `downstream` for `target_lag` is not supported. 
 
 ### Usage
 

From 6fc5af1516f5c964c74fea5aff16ae678796080e Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 1 Aug 2023 09:16:42 -0400
Subject: [PATCH 450/703] Update snowflake-configs.md

---
 website/docs/reference/resource-configs/snowflake-configs.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index a79326743a2..1e113d6a3b1 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -354,6 +354,7 @@ dbt-snowflake requires the following parameters:
 
 - `target_lag`
 - `snowflake_warehouse`
+- `on_configuration_change`
 
 To learn more about each parameter and what values it can take, see 
 the Snowflake docs page: [`CREATE DYNAMIC TABLE: Parameters`](https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table). Note that `downstream` for `target_lag` is not supported. 

From 453820924d734ba980419d88145ce7c55e85bf36 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Tue, 1 Aug 2023 10:42:12 -0600
Subject: [PATCH 451/703] add migration guide

---
 website/docs/guides/migration/sl-migration.md | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 website/docs/guides/migration/sl-migration.md

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
new file mode 100644
index 00000000000..908ef8e72fb
--- /dev/null
+++ b/website/docs/guides/migration/sl-migration.md
@@ -0,0 +1,53 @@
+---
+title: "Semantic Layer Migration"
+description: Migration guide for ugpgrading the semantic layer in v1.6
+---
+**Technical resources:**
+
+- MetricFlow [quickstart guide](https://docs.getdbt.com/docs/build/sl-getting-started)
+- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
+- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
+- [dbt_metrics deprecation post](https://docs.getdbt.com/blog/deprecating-dbt-metrics)
+
+## Intro
+
+The legacy Semantic Layer will be deprecated in H2 2023. Additionally, the `dbt_metrics` package will not be support in dbt 1.6 and above. If you are using `dbt_metrics` you will need to upgrade your configurations before upgrading to 1.6. This guide walks through the migration process for users of the legacy semantic layer who want to use to the new Semantic Layer. The estimated migration time is 2 weeks. 
+
+### Step 1: **Migrate Metric configs to the new spec.**
+
+The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. It is highly recommended that you read the [MetricFlow docs](https://docs.getdbt.com/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer.  We recommend completing these steps in a local dev environment instead of the IDE. To migrate your metrics configs: 
+**
+
+1. Creating new Semantic Model configs as YAML files in your dbt project. *
+2. Upgrading the Metrics configs in your project to the new spec.* 
+3. Delete your old metrics file or remove .yml extension so they are ignored at parse time. Remove the `dbt-metrics` package form your project. Remove any macros that reference `dbt-metrics` i.e like `metrics.calculate()`. Make sure that any packages you’re using do not have references to the old metrics spec. 
+4. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`  i.e `pip install "dbt-metricflow[snowflake]` . The MetricFlow CLI is not available in the IDE. We’re working hard to support it soon 🙂.
+5. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.  MetricFlow needs this file to query metrics. You will need to re-parse your project when you make changes to your configs. 
+6. Run `mf list metrics` to view the metrics in your project
+7. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>` i.e `mf query --metrics revenue --group-by metric_time`
+8. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
+9. Push these changes to a new branch in your repo. 
+
+**To make this process easier, we have [developed a custom migration tool](https://github.com/dbt-labs/dbt-converter) that automates these steps for you.  You can find installation instructions in the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md).  Derived metrics aren’t supported in the migration tool, and will have to be migrated manualy*
+
+## Step 2: **Audit metric values after the migration**
+
+You may need to audit metric values during the migration to ensure that the historical values of key business metrics are the same. To audit you metric values follow these steps:
+
+1. In the CLI, query the metric(s) and dimensions you want to test and add the `--explain` option. For examples `mf query --metrics orders,revenue --group-by metric_time__month,customer_type --explain`
+2. Use the SQL MetricFlow generates to create a temporary model in your project i.e `tmp_orders_revenue audit.sql`. You will use the model to compare to your legacy metrics.
+3. If you haven’t already done so, create a model using metrics.calculate() for the metrics you want to compare i.e 
+
+```bash
+select * 
+from {{ metrics.calculate(  
+[metric('orders)',
+metric('revenue)'],
+    grain='week',
+    dimensions=['metric_time', 'customer_type'],
+) }}
+
+```
+
+1. Run [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
+

From d75c304ba30ebaf86f0dbd677cad6fd656cab008 Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Tue, 1 Aug 2023 11:42:59 -0500
Subject: [PATCH 452/703] Semantic Layer Guide (#3823)

## What are you changing in this pull request and why?
This adds a Guide for Semantic Layer Best Practices
---
 .../semantic-layer-1-intro.md                 |  33 ++
 .../semantic-layer-2-setup.md                 |  44 +++
 .../semantic-layer-3-build-semantic-models.md | 291 ++++++++++++++++++
 .../semantic-layer-4-build-metrics.md         |  42 +++
 .../semantic-layer-5-refactor-a-mart.md       | 243 +++++++++++++++
 .../semantic-layer-6-advanced-metrics.md      |  80 +++++
 .../semantic-layer-7-conclusion.md            |  35 +++
 website/sidebars.js                           |  18 +-
 .../semantic-layer/orders_erd.png             | Bin 0 -> 1345248 bytes
 9 files changed, 785 insertions(+), 1 deletion(-)
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
 create mode 100644 website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
 create mode 100644 website/static/img/guides/best-practices/semantic-layer/orders_erd.png

diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
new file mode 100644
index 00000000000..3df92e63f12
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
@@ -0,0 +1,33 @@
+---
+title: "Intro to the Semantic Layer"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+:::beta
+**This is a guide for a beta product.** We anticipate this guide will evolve alongside the Semantic Layer through community collaboration. We welcome discussions, ideas, issues, and contributions to refining best practices.
+:::
+
+Flying cars, hoverboards, and true self-service analytics: this is the future we were promised. The first two might still be a few years out, but real self-service analytics is here today. With dbt Cloud's Semantic Layer, you can resolve the tension between accuracy and flexibility that has hampered analytics tools for years, empowering everybody in your organization to explore a shared reality of metrics. Best of all for analytics engineers, building with these new tools will significantly [DRY](https://docs.getdbt.com/terms/dry) up and simplify your codebase. As you'll see, the deep interaction between your dbt models and the Semantic Layer make your dbt project the ideal place to craft your metrics.
+
+## Learning goals
+
+- ❓ Understand the **purpose and capabilities** of the **dbt Semantic Layer**.
+- 🧱 Familiarity with the core components of the dbt Semantic Layer — **semantic models and metrics** — and how they work together.
+- 🛠️ Hands-on **experience building** semantic models and metrics in dbt Cloud.
+- 🔁 Know how to **refactor** models into the Semantic Layer.
+- 🏅 Aware of new **best practices** to take maximum advantage of the Semantic Layer.
+
+## Guide structure overview
+
+We'll work through our learning goals via an [example project](https://github.com/dbt-labs/jaffle-sl-template), we encourage you to follow along and try the code out for yourself if you'd like on the `start-here` branch, or you can just follow along with the completed state of the codebase on the `main` branch.
+
+1. Getting **setup** with MetricFlow in your dbt project.
+2. Building your first **semantic model** and its fundamental parts: **entities, dimensions, and measures**.
+3. Building your first **metric**.
+4. **Refactoring** a mart into the Semantic Layer.
+5. Defining **advanced metrics**: `ratio` and `derived` types.
+6. Review **best practices**.
+
+If you're ready to ship your users more power with less code, let's dive in!
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
new file mode 100644
index 00000000000..ef67082e73f
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
@@ -0,0 +1,44 @@
+---
+title: "Setup the Semantic Layer"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## Getting started
+
+First, if you want to follow along, we'll need to clone the [example project](https://github.com/dbt-labs/jaffle-sl-template). You will need access to a Snowflake, BigQuery, Databricks, or Postgres warehouse for this, for the time being. The project is our classic Jaffle Shop, a simulated chain restaurant serving [jaffles](https://en.wikipedia.org/wiki/Pie_iron) and tasty beverages.
+
+```shell
+git clone git@github.com:dbt-labs/jaffle-sl-template.git
+cd path/to/project
+```
+
+Next before we start writing code, we'll need to install the MetricFlow CLI as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8 through 3.11.
+
+We'll use pip to install MetricFlow and our dbt adapter:
+
+```shell
+# activate a virtual environment for your project,
+# if you don't have a name you like to use we suggest .venv
+python -m venv [virtual environment name]
+source [virtual environment name]/bin/activate
+# install dbt and MetricFlow
+pip install dbt-metricflow[adapter name]
+# e.g. dbt-metricflow[snowflake]
+```
+
+Lastly, to get to the pre-Semantic Layer starting state, checkout the `start-here` branch.
+
+```shell
+git checkout start-here
+```
+
+For more information you can [look at the docs](/docs/build/metricflow-cli) or checkout a [Quickstart](https://docs.getdbt.com/quickstarts) to get more familiar with setting up a dbt project.
+
+## Basic commands
+
+- 💻 This package will install both `dbt` and `mf` as CLIs in our virtual environment. All the regular `dbt` commands like `run`, `build`, and `test` are available.
+- 🔍 A less common one that will come in handy with the Semantic Layer is `dbt parse`. This will parse your project and generate a **semantic manifest**, a representation of meaningful connections described by your project. This file gives MetricFlow a **state of the world from which to generate queries**.
+- 🧰 In addition to `dbt`, you'll have access to `mf` commands like `query` and `validate-configs`, which operate based on that semantic manifest. We'll dig more into all of these as we go along.
+- 🛠️ Lets start off by running a `dbt build` to get the **starting state** of our project built.
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
new file mode 100644
index 00000000000..549dbccf8dd
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
@@ -0,0 +1,291 @@
+---
+title: "Building semantic models"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## How to build a semantic model
+
+A semantic model is the Semantic Layer equivalent to a logical layer model (what historically has just been called a 'model' in dbt land). Just as configurations for models are defined on the `models:` YAML key, configurations for semantic models are housed under `semantic models:`. A key difference is that while a logical model consists of configuration and SQL or Python code, a **semantic model is defined purely via YAML**. Rather than encoding a specific dataset, a **semantic model describes relationships** that let your end users select and refine their own datasets reliably.
+
+- ⚙️ Semantic models are **comprised of three components**:
+  - 🫂 **entities**: these describe the **relationships** between various semantic models (think ids)
+  - 🪣 **dimensions**: these are the columns you want to **slice, dice, group, and filter by** (think timestamps, categories, booleans).
+  - 📏 **measures**: these are the **quantitative values you want to aggregate**
+- 📚 We define **columns as being an entity, dimension, or measure**.
+
+:::tip
+**File per model**. Given the interdependence of logical and semantic models, and semantic models and metrics, we've updated our best practice recommendation to a one YAML file per model approach if you're using the Semantic Layer. This houses everything related to a model in one place and preserves unique file names for quickly getting to the code you want.
+:::
+
+## Defining orders
+
+- 🥪 The semantic model we're going to define is _orders_.
+- 📗 We define it as a **YAML dictionary in the semantic models list**.
+- 📑 It will have a **name, entities list, dimensions list, and measures list**.
+- ⏬ We recommend defining them **in this order consistently** as a style best practice.
+
+```YAML
+semantic_models:
+  - name: orders
+    entities:
+      ...
+    dimensions:
+      ...
+    measures:
+      ...
+```
+
+- Next we'll point to the corresponding logical model by supplying a [`ref`](https://docs.getdbt.com/reference/dbt-jinja-functions/ref) in the `model:` property, and a `description` for documentation.
+
+```YAML
+semantic_models:
+  - name: orders
+    description: |
+      Model containting order data. The grain of the table is the order id.
+    model: ref('stg_orders')
+    entities:
+      ...
+    dimensions:
+      ...
+    measures:
+      ...
+```
+
+## Establishing our entities
+
+- 🫂 Entities are the **objects and concepts** in our data that _have_ dimensions and measures. You can think of them as the **nouns** of our project, the **spines** of our queries that we may want to aggregate by, or simply the **join keys**.
+- 🔀 Entities help MetricFlow understand **how various semantic models relate to one another**.
+- ⛓️ Unlike many other semantic layers, in MetricFlow **we do not need to describe joins explicitly**, instead the **relationships are implicitly described by entities**.
+- 1️⃣ Each semantic model should have **one primary entity** defined for itself, and **any number of foreign entities** for other semantic models it may join to.
+- 🫂 Entities require a **name and type**
+  - 🔑 Types available are **primary**, **foreign**, **unique** or **natural** — we'll be focused on the first two for now, but you can [read more about unique and natural keys](https://docs.getdbt.com/docs/build/entities#entity-types).
+
+### Entities in action
+
+If we look at the staging model for orders, we see that it has 3 id columns, so we'll need three entities.
+
+```SQL
+renamed as (
+
+    select
+
+        ----------  ids
+        id as order_id,
+        store_id as location_id,
+        customer as customer_id,
+
+        ---------- properties
+        (order_total / 100.0) as order_total,
+        (tax_paid / 100.0) as tax_paid,
+
+        ---------- timestamps
+        ordered_at
+
+    from source
+```
+
+- 👉 We add them with a **`name`, `type`, and optional `expr`** (expression). The expression can be any valid SQL expression on your platform.
+- 📛 If you **don't add an expression**, MetricFlow will **assume the name is equal to the column name** in the underlying logical model.
+- 👍 Our best practices pattern is to, whenever possible, provide a `name` that is the singular form of the subject or grain of the table, and use `expr` to specify the precise column name (with `_id` etc). This will let us write **more readable metrics** on top of these semantic models.
+
+```YAML
+semantic_models:
+  - name: orders
+    ...
+    entities:
+      # we use the column for the name here because order is a reserved word in SQL
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
+
+    dimensions:
+      ...
+    measures:
+      ...
+
+```
+
+## Defining our dimensions
+
+- 🧮 Dimensions are the columns that we want to **filter and group by**, **the adjectives of our project**. They come in three types:
+  - **categorical**
+  - **time**
+  - slowly changing dimensions — [these are covered in the documentation](https://docs.getdbt.com/docs/build/dimensions#scd-type-ii), and a little more complex. To focus on building your mental models of MetricFlow's fundamentals, we won't be using SCDs this guide.
+- ➕ We're **not limited to existing columns**, we can use the `expr` property to add simple computations in our dimensions.
+- 📛 Categorical dimensions are the simplest, they simply require a `name` and `type` (type being categorical). **If the `name` property matches the name of the dimension column**, that's it, you're done. If you want or need to use a `name` other than the column name, or do some filtering or computation, **you can supply an optional `expr` property** to evaluate for the dimension.
+
+### Dimensions in action
+
+- 👀 Lets look at our staging model again and see what fields we have available.
+
+```SQL
+select
+
+    ----------  ids -> entities
+    id as order_id,
+    store_id as location_id,
+    customer as customer_id,
+
+    ---------- numerics -> measures
+    (order_total / 100.0) as order_total,
+    (tax_paid / 100.0) as tax_paid,
+
+    ---------- timestamps -> dimensions
+    ordered_at
+
+from source
+```
+
+- ⏰ For now the only dimension to add is a **time dimension**.
+- 🕰️ At least one **primary time dimension** is **required** for any semantic models that **have measures**.
+- 1️⃣ We denote this with the `is_primary` property, or if there is only one time dimension supplied it is primary by default. Below we only have `ordered_at` as a timestamp so we don't need to specify anything except the maximum granularity we're bucketing to (in this case, day).
+
+```YAML
+dimensions:
+      - name: date_trunc('day', ordered_at)
+        type: time
+        type_params:
+          time_granularity: day
+```
+
+:::tip
+**Dimensional models**. You may have some models that do not contain measures, just dimensional data that enriches other facts. That's totally fine, a semantic model does not require dimensions or measures, it just needs a primary entity, and if you do have measures, a primary time dimension.
+
+We'll discuss an alternate situation, dimensional tables that have static numeric values like supply costs or tax rates but no time dimensions, later in the Guide.
+:::
+
+- 🔢 We can also **make a dimension out of numeric column** that would be typically be a measures.
+- 🪣 Using `expr` we can **create buckets of values that we label** for our dimension. We'll add one of these in for labeling 'large orders' as any order totals over $50.
+
+```YAML
+...
+dimensions:
+  - name: date_trunc('day', ordered_at)
+    type: time
+    type_params:
+      time_granularity: day
+  - name: is_large_order
+    type: categorical
+    expr: case when order_total > 50 then true else false end
+...
+```
+
+## Making our measures
+
+- 📏 Measures are the final component of a semantic model. They describe the **numeric values that we want to aggregate**.
+- 🧱 Measures form **the building blocks of metrics**, with entities and dimensions helping us combine, group, and filter those metrics correctly.
+- 🏃 You can think of them as something like the **verbs of a semantic model**.
+
+### Measures in action
+
+- 👀 Lets look at **our staging model** one last time and see what **fields we want to measure**.
+
+```SQL
+select
+
+    ----------  ids -> entities
+    id as order_id,
+    store_id as location_id,
+    customer as customer_id,
+
+    ---------- numerics -> measures
+    (order_total / 100.0) as order_total,
+    (tax_paid / 100.0) as tax_paid,
+
+    ---------- timestamps -> dimensions
+    ordered_at
+
+from source
+```
+
+- ➕ Here `order_total` and `tax paid` are the **columns we want as measures**.
+- 📝 We can describe them via the code below, specifying a **name, description, aggregation, and expression**.
+- 👍 As before MetricFlow we default to the **name being the name of a column when no expression is supplied**.
+- 🧮 [Many different aggregations](https://docs.getdbt.com/docs/build/measures#aggregation) are available to us. Here we just want sums.
+
+```YAML
+measures:
+  - name: order_total
+    description: The total amount for each order inlcuding taxes.
+    agg: sum
+  - name: tax_paid
+    description: The total tax paid on each order.
+    agg: sum
+```
+
+- 🆕 We can also **create new measures using expressions**, for instance adding a count of individual orders as below.
+
+```YAML
+  - name: order_count
+    description: The count of individual orders.
+    expr: 1
+    agg: sum
+```
+
+## Validating configs
+
+Our completed code should look like this, our first semantic model!
+
+```orders
+semantic_models:
+  - name: orders
+    defaults:
+      agg_time_dimension: ordered_at
+    description: |
+      Order fact table. This table is at the order grain with one row per order.
+
+    model: ref('stg_orders')
+
+    entities:
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
+
+    dimensions:
+      - name: ordered_at
+        type: time
+        type_params:
+          time_granularity: day
+      - name: is_large_order
+        type: categorical
+        expr: case when order_total > 50 then true else false end
+
+    measures:
+      - name: order_total
+        description: The total revenue for each order.
+        agg: sum
+      - name: order_count
+        description: The count of individual orders.
+        expr: 1
+        agg: sum
+      - name: tax_paid
+        description: The total tax paid on each order.
+        agg: sum
+```
+
+- 🦺 We can check that it's valid configuration and works with the real data our dbt project is generating by using the `mf validate-configs` command. This will:
+  1. **Parse the semantic manifest** our configuration describes out of the dbt project.
+  2. Validate the **internal semantics** of the manifest as described by our code.
+  3. Validate the **external semantics** of the manifest against your data warehouse (e.g. making sure that a column specified as a dimension exists on the proper table)
+
+## Review and next steps
+
+Let's review what the basics of semantic models, they:
+
+- 🧱 Consist off **entities, dimensions, and measures**.
+- 🫂 Describe the **semantics and relationships of objects** in the warehouse.
+- 1️⃣ Correspond to a **single logical model** in your dbt project.
+
+Next up, lets use our new semantic model to **build a metric**!
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
new file mode 100644
index 00000000000..1a572cc03e4
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
@@ -0,0 +1,42 @@
+---
+title: "Building metrics"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## How to build metrics
+
+- 💹 We'll start with one of the most important metrics for any business: **revenue**.
+- 📖 For now, our metric for revenue will be **defined as the sum of order totals excluding tax**.
+- 🆕 Let's create a file called `metrics.yml` in our marts folder for now to write our first metric in.
+
+## Defining revenue
+
+- 🔢 Metrics have four basic properties:
+  - `name:` We'll use 'revenue' to reference this metric.
+  - `description:` For documentation.
+  - `label:` The display name for the metric in downstream tools.
+  - `type:` one of `simple`, `ratio`, or `derived`.
+- 🎛️ Each type has different `type_params`.
+- 🛠️ We'll build a **simple metric** first to get the hang of it, and move on to ratio and derived metrics later.
+- 📏 Simple metrics are built on a **single measure defined as a type parameter**.
+- 🔜 Defining **measures as their own distinct component** on semantic models is critical to allowing the **flexibility of more advanced metrics**, though simple metrics act mainly as **pass-through that provide filtering** and labeling options. A `create_metric` option for measures is coming in the next version of MetricFlow to **save you writing extra code** for simple metrics that make no changes to the underlying measure.
+
+```YAML
+metrics:
+  - name: revenue
+    description: Sum of the order total.
+    label: Revenue
+    type: simple
+    type_params:
+      measure: order_total
+```
+
+## Query your metric
+
+- It's best practice any time we're updating our semantic layer code to run a `dbt parse && mf validate-configs`.
+- If everything passes, we can start querying this metric with `mf query`!
+- `mf query` is not how you would use the tool in production, that's handled by the dbt Semantic Layer's cloud features. It's available for testing results of various metric queries in development, exactly as we're using it now.
+- Try `mf query --metrics revenue --group-by metric_time__day` and see a preview of the data come back.
+- Note the structure of the above query. We select the metric(s) we want and the dimensions to group them by — we use dunders (double underscores e.g.`metric_time__[time bucket]`) to designate time dimensions or other non-unique dimensions that need a specified entity path to resolve (e.g. if you have a orders location dimension and a employee location dimension both named 'location' you would need dunders to specify `orders__location` or `employee__location`).
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
new file mode 100644
index 00000000000..cc519e1b718
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
@@ -0,0 +1,243 @@
+---
+title: "Refactor an existing mart"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## A new approach
+
+We've covered the basics, now it's time to dig in to the fun and messy part: how do we refactor an existing mart in dbt into semantic models and metrics?
+
+Let's look at the differences we can observe in how we might approach this with MetricFlow supercharging dbt versus how we work without a Semantic Layer. These differences can then inform our structure.
+
+- 🍊 In dbt, we tend to create **highly denormalized datasets** that bring **everything you want around a certain entity or process into a single table**.
+- 💜 The problem is, this **limits the dimensionality available to MetricFlow**. The more we pre-compute and 'freeze' into place, the less flexible our data is.
+- 🚰 In MetricFlow, we ideally want **highly normalized**, star schema-like data that then allows MetricFlow to shine as a **denormalization engine**.
+- ∞ Another way to think about this is that instead of moving down a list of requested priorities trying to pre-make as many combinations of our marts as possible — increasing lines of code and complexity — we can **let MetricFlow present every combination possible without specifically coding it**.
+- 🏗️ To resolve these approaches optimally, we'll need to shift some **fundamental aspects of our modeling strategy**.
+
+## Refactor steps outlined
+
+We recommend an incremental implementation process that looks something like this:
+
+1. 👉 Identify **an important output** (a revenue chart on a dashboard for example, and the mart model(s) that supplies this output.
+2. 🔍 Examine all the **entities that are components** of this mart (for instance, an orders mart may include customers, shipping, and product data).
+3. 🛠️ **Build semantic models and metrics** for all the required components.
+4. 👯 Create a **clone of the output** on top of the Semantic Layer.
+5. 💻 Audit to **ensure you get accurate outputs**.
+6. 💎 Use `mf list dimensions --metrics [metric_name]` to check that your refactoring is increasing dimensionality (flexibility).
+7. 👉 Identify **any other outputs** that point to the mart and **move them to the Semantic Layer**.
+8. ✌️ Put a **deprecation plan** in place for the mart.
+
+You would then **continue this process** on other outputs and marts moving down a list of **priorities**. Each model as you go along will be faster and easier as you'll **reuse many of the same components** that will already have been semantically modeled.
+
+## Let's make a `revenue` metric
+
+So far we've been working in new pointing at a staging model to simplify things as we build new mental models for MetricFlow. In reality, unless you're implementing MetricFlow in a green-field dbt project, you probably are going to have some refactoring to do. So let's get into that in detail.
+
+1. 📚 Per the above steps, we've identified our target, now we need to identify all the components we need, these will be all the 'import' CTEs at the top our mart. Let's look at `orders` and `order_items`, the likely models to generate revenue, we see we'll need: `orders`, `order_items`, `products`, `locations`, and `supplies`.
+2. 🗺️ We'll next make semantic models for all of these. Let's walk through a straightforward conversion first with `locations`.
+3. ⛓️ We'll want to first decide if we need to do any joining to get this into the shape we want for our semantic model. The biggest determinants of this are two factors:
+   - 📏 Does this semantic model **contain measures**?
+   - 🕥 Does this semantic model have a **primary timestamp**?
+   - 🫂 If a semantic model **has measures but no timestamp** (for example, supplies in the example project, which has static costs of supplies), you'll likely want to **sacrifice some normalization and join it on to another model** that has a primary timestamp to allow for metric aggregation.
+4. 🔄 If we _don't_ need any joins, we'll just go straight to the staging model for our semantic model's `ref`. Locations does have a `tax_rate` measure, but it also has an `ordered_at` timestamp, so we can go **straight to the staging model** here.
+5. 🥇 We specify our **primary entity** (based on `location_id`), dimensions (one categorical, `location_name`, and one **primary time dimension** `opened_at`), and lastly our measures, in this case just `average_tax_rate`.
+
+   ```YAML
+   semantic_models:
+   - name: locations
+      description: |
+      Location dimension table. The grain of the table is one row per location.
+      model: ref('stg_locations')
+      entities:
+      - name: location
+         type: primary
+         expr: location_id
+      dimensions:
+      - name: location_name
+         type: categorical
+      - name: date_trunc('day', opened_at)
+         type: time
+         type_params:
+            time_granularity: day
+      measures:
+      - name: average_tax_rate
+         description: Average tax rate.
+         expr: tax_rate
+         agg: avg
+   ```
+
+## Semantic and logical interaction
+
+Now, let's tackle a thornier situation. Products and supplies both have dimensions and measures but no time dimension. Products has a one-to-one relationship with `order_items`, enriching that table, which is itself just a mapping table of products to orders. Additionally, products have a one-to-many relationship with supplies. The high-level ERD looks like the diagram below.
+
+<Lightbox src='/img/guides/best-practices/semantic-layer/orders_erd.png' />
+
+So to calculate, for instance, the cost of ingredients and supplies for a given order, we'll need to do some joining and aggregating, but again we **lack a time dimension for products and supplies**. This is the signal to us that we'll **need to build a logical mart** and point our semantic model at that.
+
+:::tip
+**dbt 🧡 MetricFlow.** This is where integrating your semantic definitions into your dbt project really starts to pay dividends. The interaction between the logical and semantic layers is so dynamic, you either need to house them in one codebase or facilitate a lot of cross-project communication and dependency.
+:::
+
+1. 🎯 Let's aim at, to start, building a table at the `order_items` grain. We can aggregate supply costs up, map over the fields we want from products, such as price, and bring the `ordered_at` timestamp we need over from the orders table. We'll write the following code in `models/marts/order_items.sql`.
+
+   ```SQL
+   {{
+      config(
+         materialized = 'table',
+      )
+   }}
+
+   with
+
+   order_items as (
+
+      select * from {{ ref('stg_order_items') }}
+
+   ),
+
+   orders as (
+
+      select * from {{ ref('stg_orders')}}
+
+   ),
+
+   products as (
+
+      select * from {{ ref('stg_products') }}
+
+   ),
+
+   supplies as (
+
+      select * from {{ ref('stg_supplies') }}
+
+   ),
+
+   order_supplies_summary as (
+
+      select
+         product_id,
+         sum(supply_cost) as supply_cost
+
+      from supplies
+
+      group by 1
+   ),
+
+   joined as (
+
+      select
+         order_items.*,
+         products.product_price,
+         order_supplies_summary.supply_cost,
+         products.is_food_item,
+         products.is_drink_item,
+         orders.ordered_at
+
+      from order_items
+
+      left join orders on order_items.order_id  = orders.order_id
+
+      left join products on order_items.product_id = products.product_id
+
+      left join order_supplies_summary on order_items.product_id = order_supplies_summary.product_id
+
+   )
+
+   select * from joined
+   ```
+
+2. 🏗️ Now we've got a table that looks more like what we want to feed into MetricFlow. Next, we'll **build a semantic model on top of this new mart** in `models/marts/order_items.yml`. Again, we'll identify our **entities, then dimensions, then measures**.
+
+   ```YAML
+   semantic_models:
+      #The name of the semantic model.
+      - name: order_items
+         defaults:
+            agg_time_dimension: ordered_at
+         description: |
+            Items contatined in each order. The grain of the table is one row per order item.
+         model: ref('order_items')
+         entities:
+            - name: order_item
+              type: primary
+              expr: order_item_id
+            - name: order_id
+              type: foreign
+              expr: order_id
+            - name: product
+              type: foreign
+              expr: product_id
+         dimensions:
+            - name: ordered_at
+              expr: date_trunc('day', ordered_at)
+              type: time
+              type_params:
+                time_granularity: day
+            - name: is_food_item
+              type: categorical
+            - name: is_drink_item
+              type: categorical
+         measures:
+            - name: revenue
+              description: The revenue generated for each order item. Revenue is calculated as a sum of revenue associated with each product in an order.
+              agg: sum
+              expr: product_price
+            - name: food_revenue
+              description: The revenue generated for each order item. Revenue is calculated as a sum of revenue associated with each product in an order.
+              agg: sum
+              expr: case when is_food_item = 1 then product_price else 0 end
+            - name: drink_revenue
+              description: The revenue generated for each order item. Revenue is calculated as a sum of revenue associated with each product in an order.
+              agg: sum
+              expr: case when is_drink_item = 1 then product_price else 0 end
+            - name: median_revenue
+              description: The median revenue generated for each order item.
+              agg: median
+              expr: product_price
+   ```
+
+3. 📏 Finally, Let's **build a simple revenue metric** on top of our semantic model now.
+
+   ```YAML
+   metrics:
+      - name: revenue
+        description: Sum of the product revenue for each order item. Excludes tax.
+        type: simple
+        label: Revenue
+        type_params:
+          measure: revenue
+   ```
+
+## Checking our work
+
+- 🔍 We always will start our **auditing** with a `dbt parse && mf validate-configs` to **ensure our code works** before we examine its output.
+- 👯 If we're working there, we'll move to trying out an `mf query` that **replicates the logic of the output** we're trying to refactor.
+- 💸 For our example we want to **audit monthly revenue**, to do that we'd run the query below. You can [read more about the MetricFlow CLI](https://docs.getdbt.com/docs/build/metricflow-cli).
+
+### Example query
+
+```shell
+mf query --metrics revenue --group-by metric_time__month
+```
+
+### Example query results
+
+```shell
+✔ Success 🦄 - query completed after 1.02 seconds
+| METRIC_TIME__MONTH   |   REVENUE |
+|:---------------------|----------:|
+| 2016-09-01 00:00:00  |  17032.00 |
+| 2016-10-01 00:00:00  |  20684.00 |
+| 2016-11-01 00:00:00  |  26338.00 |
+| 2016-12-01 00:00:00  |  10685.00 |
+```
+
+- Try introducing some other dimensions from the semantic models into the `group-by` arguments to get a feel for this command.
+
+## An alternate approach
+
+If you **don't have capacity to refactor** some of your marts, they can **still benefit from the Semantic Layer**. The above process is about **maximizing dimensionality** for the long term. In the short term, making your **marts as-is available to MetricFlow** unlocks greatly increased functionality. For an example of this quicker approach check out the `customers` SQL and YAML files on the `main` branch. This displays a **typical denormalized dbt mart** being hooked into MetricFlow.
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
new file mode 100644
index 00000000000..cfac469ed7c
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
@@ -0,0 +1,80 @@
+---
+title: "More advanced metrics"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## More advanced metric types
+
+We're not limited to just passing measures through to our metrics, we can also _combine_ measures to model more advanced metrics.
+
+- 🍊 **Ratio** metrics are, as the name implies, about **comparing two metrics as a numerator and a denominator** to form a new metric, for instance the percentage of order items that are food items instead of drinks.
+- 🧱 **Derived** metrics are when we want to **write an expression** that calculates a metric **using multiple metrics**. A classic example here is our gross profit calculated by subtracting costs from revenue.
+- ➕ **Cumulative** metrics calculate all of a **measure over a given window**, such as the past week, or if no window is supplied, the all-time total of that measure.
+
+## Ratio metrics
+
+- 🔢 We need to establish one measure that will be our **numerator**, and one that will be our **denominator**.
+- 🥪 Let's calculate the **percentage** of our Jaffle Shop revenue that **comes from food items**.
+- 💰 We already have our denominator, revenue, but we'll want to **make a new metric for our numerator** called `food_revenue`.
+
+```YAML
+ - name: food_revenue
+    description: The revenue from food in each order.
+    label: Food Revenue
+    type: simple
+    type_params:
+      measure: revenue
+      filter: |
+        {{ Dimension('order__is_food_order') }} = true
+```
+
+- 📝 Now we can set up our ratio metric.
+
+```YAML
+- name: food_revenue_pct
+  description: The % of order revenue from food.
+  label: Food Revenue %
+  type: ratio
+  type_params:
+    numerator: food_revenue
+    denominator: revenue
+```
+
+## Derived metrics
+
+- 🆙 Now let's really have some fun. One of the most important metrics for any business is not just revenue, but _revenue growth_. Let's use a derived metric to build month-over-month revenue.
+- ⚙️ A derived metric has a couple key components:
+  - 📚 A list of metrics to build on. These can be manipulated and filtered in various way, here we'll use the `offset_window` property to lag by a month.
+  - 🧮 An expression that performs a calculation with these metrics.
+- With these parts we can assemble complex logic that would otherwise need to be 'frozen' in logical models.
+
+```YAML
+- name: revenue_growth_mom
+  description: "Percentage growth of revenue compared to 1 month ago. Excluded tax"
+  type: derived
+  label: Revenue Growth % M/M
+  type_params:
+    expr: (current_revenue - revenue_prev_month) * 100 / revenue_prev_month
+    metrics:
+      - name: revenue
+        alias: current_revenue
+      - name: revenue
+        offset_window: 1 month
+        alias: revenue_prev_month
+```
+
+## Cumulative metrics
+
+- ➕ Lastly, lets build a **cumulative metric**. In keeping with our theme of business priorities, let's continue with revenue and build an **all-time revenue metric** for any given time window.
+- 🪟 All we need to do is indicate the type is `cumulative` and not supply a `window` in the `type_params`, which indicates we want cumulative for the entire time period our end users select.
+
+```YAML
+- name: cumulative_revenue
+  description: The cumulative revenue for all orders.
+  label: Cumulative Revenue (All Time)
+  type: cumulative
+  type_params:
+    measure: revenue
+```
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
new file mode 100644
index 00000000000..915ab2d9bfe
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
@@ -0,0 +1,35 @@
+---
+title: "Best practices"
+description: Getting started with the dbt Semantic Layer
+displayText: "dbt Cloud Semantic Layer best practices"
+hoverSnippet: Learn how to get started with the dbt Semantic Layer
+---
+
+## Putting it all together
+
+- 📊 We've **created semantic models and metrics** for basic coverage of a key business area.
+- 🔁 In doing so we've **refactored a 'static' mart** into a dynamic, flexible new life in the Semantic Layer.
+- 🗺️ We encourage you to **explore the `main` branch** of the [example project repo](https://github.com/dbt-labs/jaffle-sl-template) to see even more metrics and semantic models in action within a project fully ported to the Semantic Layer.
+
+## Best practices
+
+- ✅ **Prefer normalization** when possible to allow MetricFlow to denormalize dynamically for end users.
+- ✅ Use **marts to denormalize** when needed, for instance grouping tables together into richer components, or getting measures on dimensional tables attached to a table with a time spine.
+- ✅ When source data is **well normalized** you can **build semantic models on top of staging models**.
+- ✅ **Prefer** computing values in **measures and metrics** when possible as opposed to in fixed marts.
+- ❌ **Don't directly refactor the code you have in production**, build in parallel so you can audit the Semantic Layer output and deprecate old marts gracefully.
+
+## Key commands
+
+- 🔑 Use `dbt parse && mf validate-configs` to generate a semantic manifest and ensure it works with your data.
+- 🔑 Use `mf list dimensions --metrics [metric name]` to check that you're increasing dimensionality as you progress.
+- 🔑 Use `mf query [query options]` to preview the output from your metrics as you develop.
+
+## Next steps
+
+- 🗺️ Map out a clear plan for your dbt project to **incrementally adopt the Semantic Layer**.
+- 🤗 Get involved in the community and ask questions, **help craft best practices**, and share your progress in building a dbt Semantic Layer.
+
+The dbt Semantic Layer is the biggest paradigm shift thus far in the young practice of analytics engineering. It's ready to provide value right away, but is most impactful if you move your project towards increasing normalization, and allow MetricFlow to do the denormalization for you with maximum dimensionality.
+
+We will be releasing more resources soon covering implementation of the Semantic Layer in dbt Cloud with various integrated BI tools. This is just the beginning, hopefully this guide has given you a path forward for building your data platform in this new era.
diff --git a/website/sidebars.js b/website/sidebars.js
index c09e7b784c4..f108e580695 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -869,6 +869,22 @@ const sidebarSettings = {
             "guides/best-practices/how-we-style/6-how-we-style-conclusion",
           ],
         },
+        {
+          type: "category",
+          label: "How we build our metrics",
+          link: {
+            type: "doc",
+            id: "guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro",
+          },
+          items: [
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup",
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models",
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics",
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart",
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics",
+            "guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion",
+          ],
+        },
         {
           type: "category",
           label: "Materializations best practices",
@@ -884,7 +900,7 @@ const sidebarSettings = {
             "guides/best-practices/materializations/materializations-guide-6-examining-builds",
             "guides/best-practices/materializations/materializations-guide-7-conclusion",
           ],
-        },
+        }, 
         {
           type: "category",
           label: "dbt Cloud Environment best practices",
diff --git a/website/static/img/guides/best-practices/semantic-layer/orders_erd.png b/website/static/img/guides/best-practices/semantic-layer/orders_erd.png
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literal 0
HcmV?d00001


From 39363daa74f052b59d789032c9374cf0005d6272 Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Tue, 1 Aug 2023 11:55:29 -0500
Subject: [PATCH 453/703] Fix beta tag (#3842)

## What are you changing in this pull request and why?
<!---
Describe your changes and why you're making them. If linked to an open
issue or a pull request on dbt Core, then link to them here!

To learn more about the writing conventions used in the dbt Labs docs,
see the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md).
-->

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page
---
 .../how-we-build-our-metrics/semantic-layer-1-intro.md          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
index 3df92e63f12..1d63431a73e 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
@@ -5,7 +5,7 @@ displayText: "dbt Cloud Semantic Layer best practices"
 hoverSnippet: Learn how to get started with the dbt Semantic Layer
 ---
 
-:::beta
+:::tip
 **This is a guide for a beta product.** We anticipate this guide will evolve alongside the Semantic Layer through community collaboration. We welcome discussions, ideas, issues, and contributions to refining best practices.
 :::
 

From 95846210671f3ccc7dec44cb495a47f72eaa9c2a Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 1 Aug 2023 10:23:33 -0700
Subject: [PATCH 454/703] Fold in feedback

---
 .../versions/02-upgrading-to-v1.5.md          | 50 +++++++++++++++----
 .../reference/project-configs/target-path.md  | 14 +++++-
 2 files changed, 53 insertions(+), 11 deletions(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index ebcf74bc5a7..88a908f0d1d 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -57,17 +57,49 @@ models:
     config: ...
 ```
 
-Some options that could previously be specified _after_ a sub-command can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
+Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
+
+<details>
+<summary>List of affected commands</summary>
+
+```bash
+--cache-selected-only | --no-cache-selected-only
+--debug, -d | --no-debug
+--deprecated-print | --deprecated-no-print
+--enable-legacy-logger | --no-enable-legacy-logger
+--fail-fast, -x | --no-fail-fast
+--log-cache-events | --no-log-cache-events
+--log-format
+--log-format-file
+--log-level
+--log-level-file
+--log-path
+--macro-debugging | --no-macro-debugging
+--partial-parse | --no-partial-parse
+--partial-parse-file-path
+--populate-cache | --no-populate-cache
+--print | --no-print
+--printer-width
+--quiet, -q | --no-quiet
+--record-timing-info, -r
+--send-anonymous-usage-stats | --no-send-anonymous-usage-stats
+--single-threaded | --no-single-threaded
+--static-parser | --no-static-parser
+--use-colors | --no-use-colors
+--use-colors-file | --no-use-colors-file
+--use-experimental-parser | --no-use-experimental-parser
+--version, -V, -v
+--version-check | --no-version-check
+--warn-error
+--warn-error-options
+--write-json | --no-write-json
+
+```
+
+</details>
 
-|              |              |                 |           |                           |
-|--------------|--------------|-----------------|-----------|---------------------------|
-| --cache-selected-only | --debug | --deprecated-print | --enable-legacy-logger| --fail-fast |
-| --log-cache-events  | --log-format | --log-level | --log-path | --macro-debugging |
-| --partial-parse      | --populate-cache | --print      | --printer-width | --quiet |
-| --record-timing-info | --send-anonymous-usage-stats | --single-threaded | --static-parser | --use-colors |
-| --use-experimental-parser  | --version | --version-check | --warn-error | --write-json | 
 
-Additionally, some options could be previously specified _before_ a sub-command can now only be specified _after_. Any command _not_ on the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
+Additionally, some options that could be previously specified _before_ a subcommand can now only be specified _after_. Any command _not_ on the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
 
 
 The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object; otherwise, some of your dbt commands will never finish. For example:
diff --git a/website/docs/reference/project-configs/target-path.md b/website/docs/reference/project-configs/target-path.md
index 54458efe512..fddc5a93c5e 100644
--- a/website/docs/reference/project-configs/target-path.md
+++ b/website/docs/reference/project-configs/target-path.md
@@ -48,12 +48,22 @@ The precedence order is: CLI flag > env var > `dbt_project.yml`
 </VersionBlock>
 
 ## Examples
-### Use a subdirectory named `compiled` for compiled files
+### Specify subdirectory using the project config file
 
 <File name='dbt_project.yml'>
 
 ```yml
-target-path: "compiled"
+target-path: "compiled_files"
 ```
 
 </File>
+
+<VersionBlock firstVersion="1.5">
+
+### Specify subdirectory from the command line 
+
+```bash
+dbt run --target-path compiled_files
+```
+
+</VersionBlock>
\ No newline at end of file

From 86da948c10d8327f28e91f3f9b241155b2548f53 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Tue, 1 Aug 2023 10:44:44 -0700
Subject: [PATCH 455/703] Update
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md

Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 .../docs/guides/migration/versions/02-upgrading-to-v1.5.md    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 88a908f0d1d..91e12d4a4cb 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -57,10 +57,10 @@ models:
     config: ...
 ```
 
-Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected commands are:
+Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected options are:
 
 <details>
-<summary>List of affected commands</summary>
+<summary>List of affected options</summary>
 
 ```bash
 --cache-selected-only | --no-cache-selected-only

From 7bc568d698ad4d905228b551ca61c53ec7377d6f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Tue, 1 Aug 2023 10:50:16 -0700
Subject: [PATCH 456/703] Update
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md

---
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 91e12d4a4cb..1779d18193d 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -99,7 +99,7 @@ Some options that could previously be specified _after_ a subcommand can now onl
 </details>
 
 
-Additionally, some options that could be previously specified _before_ a subcommand can now only be specified _after_. Any command _not_ on the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
+Additionally, some options that could be previously specified _before_ a subcommand can now only be specified _after_. Any command _not_ in the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
 
 
 The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object; otherwise, some of your dbt commands will never finish. For example:

From 6367dbb5a73526add08744bf626f95aefdad587a Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Tue, 1 Aug 2023 11:56:42 -0600
Subject: [PATCH 457/703] Fix broken links to "Related documentation" (#3840)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

[Preview](https://deploy-preview-3840--docs-getdbt-com.netlify.app/docs/build/custom-aliases#model-versions)

## What are you changing in this pull request and why?


[These](https://docs.getdbt.com/docs/build/custom-aliases#model-versions)
links are broken:

<img width="175" alt="image"
src="https://github.com/dbt-labs/docs.getdbt.com/assets/44704949/99b4dd69-335b-4ed9-bbd8-4d2be5c45e28">

Because they go here:
- https://docs.getdbt.com/docs/build/govern/model-versions
- https://docs.getdbt.com/docs/build/resource-properties/versions#alias

## 🎩
Click these links (which work now):

> Related documentation:
>
> - [Model
versions](https://deploy-preview-3840--docs-getdbt-com.netlify.app/docs/collaborate/govern/model-versions)
> -
[versions](https://deploy-preview-3840--docs-getdbt-com.netlify.app/reference/resource-properties/versions#alias)

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/build/custom-aliases.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/custom-aliases.md b/website/docs/docs/build/custom-aliases.md
index 589d64f8510..326434ea922 100644
--- a/website/docs/docs/build/custom-aliases.md
+++ b/website/docs/docs/build/custom-aliases.md
@@ -168,8 +168,8 @@ New in v1.5
 <VersionBlock firstVersion="1.5">
 
 **Related documentation:**
-- [Model versions](govern/model-versions)
-- [`versions`](resource-properties/versions#alias)
+- [Model versions](/docs/collaborate/govern/model-versions)
+- [`versions`](/reference/resource-properties/versions#alias)
 
 By default, dbt will create versioned models with the alias `<model_name>_v<v>`, where `<v>` is that version's unique identifier. You can customize this behavior just like for non-versioned models by configuring a custom `alias` or re-implementing the `generate_alias_name` macro.
 

From 0f28cf951ce5b08de8862c91604914073e2345d3 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 1 Aug 2023 11:10:59 -0700
Subject: [PATCH 458/703] Fold in moar feedback

---
 .../connect-data-platform/connection-profiles.md    | 13 +++++++++++++
 .../migration/versions/02-upgrading-to-v1.5.md      |  4 ++--
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/connection-profiles.md b/website/docs/docs/core/connect-data-platform/connection-profiles.md
index 8088ff1dfa7..a6ec4120433 100644
--- a/website/docs/docs/core/connect-data-platform/connection-profiles.md
+++ b/website/docs/docs/core/connect-data-platform/connection-profiles.md
@@ -177,10 +177,23 @@ There are multiple ways to direct dbt to a different location for your `profiles
 ### 1. Use the `--profiles-dir` option when executing a dbt command
 This option can be used as follows:
 
+<VersionBlock lastVersion="1.4">
+
+ ```
+$ dbt --profiles-dir path/to/directory run
+ ```
+
+ </VersionBlock>
+
+
+<VersionBlock firstVersion="1.5">
+
  ```
 $ dbt run --profiles-dir path/to/directory
  ```
 
+ </VersionBlock>
+
 If using this method, the `--profiles-dir` option needs to be provided every time you run a dbt command.
 
 ### 2. Use the `DBT_PROFILES_DIR` environment variable to change the default location
diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index 1779d18193d..f4005cd8bdc 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -57,7 +57,7 @@ models:
     config: ...
 ```
 
-Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the command, `--write-json` and `--no-write-json`, for example.  The list of affected options are:
+Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the option, `--write-json` and `--no-write-json`, for example.  The list of affected options are:
 
 <details>
 <summary>List of affected options</summary>
@@ -99,7 +99,7 @@ Some options that could previously be specified _after_ a subcommand can now onl
 </details>
 
 
-Additionally, some options that could be previously specified _before_ a subcommand can now only be specified _after_. Any command _not_ in the above list must appear _after_ the subcommand from v1.5 on. For example, `--profiles-dir`.
+Additionally, some options that could be previously specified _before_ a subcommand can now only be specified _after_. Any option _not_ in the above list must appear _after_ the subcommand from v1.5 and later. For example, `--profiles-dir`.
 
 
 The built-in [collect_freshness](https://github.com/dbt-labs/dbt-core/blob/1.5.latest/core/dbt/include/global_project/macros/adapters/freshness.sql) macro now returns the entire `response` object, instead of just the `table` result. If you're using a custom override for `collect_freshness`, make sure you're also returning the `response` object; otherwise, some of your dbt commands will never finish. For example:

From 44e69aff176989d7315df147275c3383d2a6ae16 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Tue, 1 Aug 2023 11:22:28 -0700
Subject: [PATCH 459/703] Update
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md

---
 website/docs/guides/migration/versions/02-upgrading-to-v1.5.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
index f4005cd8bdc..0c7fc7ebcad 100644
--- a/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
+++ b/website/docs/guides/migration/versions/02-upgrading-to-v1.5.md
@@ -57,7 +57,7 @@ models:
     config: ...
 ```
 
-Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the option, `--write-json` and `--no-write-json`, for example.  The list of affected options are:
+Some options that could previously be specified _after_ a subcommand can now only be specified _before_. This includes the inverse of the option, `--write-json` and `--no-write-json`, for example. The list of affected options are:
 
 <details>
 <summary>List of affected options</summary>

From 731438c1309ac5d8efe1e4f3a890254606d01230 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 1 Aug 2023 11:26:42 -0700
Subject: [PATCH 460/703] Revert one change

---
 .../connect-data-platform/connection-profiles.md    | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/website/docs/docs/core/connect-data-platform/connection-profiles.md b/website/docs/docs/core/connect-data-platform/connection-profiles.md
index a6ec4120433..8088ff1dfa7 100644
--- a/website/docs/docs/core/connect-data-platform/connection-profiles.md
+++ b/website/docs/docs/core/connect-data-platform/connection-profiles.md
@@ -177,23 +177,10 @@ There are multiple ways to direct dbt to a different location for your `profiles
 ### 1. Use the `--profiles-dir` option when executing a dbt command
 This option can be used as follows:
 
-<VersionBlock lastVersion="1.4">
-
- ```
-$ dbt --profiles-dir path/to/directory run
- ```
-
- </VersionBlock>
-
-
-<VersionBlock firstVersion="1.5">
-
  ```
 $ dbt run --profiles-dir path/to/directory
  ```
 
- </VersionBlock>
-
 If using this method, the `--profiles-dir` option needs to be provided every time you run a dbt command.
 
 ### 2. Use the `DBT_PROFILES_DIR` environment variable to change the default location

From 97b5c7369e9eb222e6825c43f8f3258fbdb76f7e Mon Sep 17 00:00:00 2001
From: Anders Swanson <anders.swanson@dbtlabs.com>
Date: Tue, 1 Aug 2023 15:24:48 -0400
Subject: [PATCH 461/703] merge only in 1.6

---
 website/docs/reference/resource-configs/postgres-configs.md | 6 ++++++
 website/docs/reference/resource-configs/redshift-configs.md | 6 ++++++
 2 files changed, 12 insertions(+)

diff --git a/website/docs/reference/resource-configs/postgres-configs.md b/website/docs/reference/resource-configs/postgres-configs.md
index a8d5af2301e..b2d1f644e90 100644
--- a/website/docs/reference/resource-configs/postgres-configs.md
+++ b/website/docs/reference/resource-configs/postgres-configs.md
@@ -8,9 +8,15 @@ id: "postgres-configs"
 
 In dbt-postgres, the following incremental materialization strategies are supported:
 
+<VersionBlock lastVersion="1.5">
+- `append` (default)
+- `delete+insert`
+</VersionBlock>
+<VersionBlock firstVersion="1.6">
 - `append` (default)
 - `merge`
 - `delete+insert`
+</VersionBlock>
 
 
 ## Performance Optimizations
diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 18e4da73225..25b5264b71c 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -14,9 +14,15 @@ To-do:
 
 In dbt-redshift, the following incremental materialization strategies are supported:
 
+<VersionBlock lastVersion="1.5">
+- `append` (default)
+- `delete+insert`
+</VersionBlock>
+<VersionBlock firstVersion="1.6">
 - `append` (default)
 - `merge`
 - `delete+insert`
+</VersionBlock>
 
 All of these strategies are inheirited via from dbt-postgres.
 

From 633c0aa40c62045dc026a3606374a1d863849e44 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Tue, 1 Aug 2023 13:55:47 -0700
Subject: [PATCH 462/703] Added page to sidebar and reviewed content

---
 website/docs/guides/migration/sl-migration.md | 88 +++++++++++--------
 website/sidebars.js                           |  1 +
 2 files changed, 52 insertions(+), 37 deletions(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 908ef8e72fb..c57611494bd 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -1,53 +1,67 @@
 ---
-title: "Semantic Layer Migration"
-description: Migration guide for ugpgrading the semantic layer in v1.6
+title: "Legacy dbt Semantic Layer migration guide"
+sidebar_label: "Legacy dbt Semantic Layer migration"
+description: "Learn how to migrate from the legacy dbt Semantic Layer to the latest one."
+tags: [Semantic Layer]
 ---
-**Technical resources:**
 
-- MetricFlow [quickstart guide](https://docs.getdbt.com/docs/build/sl-getting-started)
-- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
-- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
-- [dbt_metrics deprecation post](https://docs.getdbt.com/blog/deprecating-dbt-metrics)
+The legacy Semantic Layer will be deprecated in H2 2023. Additionally, the `dbt_metrics` package will not be supported in dbt v1.6 and later. If you are using `dbt_metrics`, you'll need to upgrade your configurations before upgrading to v1.6. This guide is for people who have the legacy dbt Semantic Layer setup and would like to migrate to the new dbt Semantic Layer. The estimated migration time is two weeks. 
+
+
+## Step 1: Migrate metric configs to the new spec
 
-## Intro
+The metrics specification in dbt core is changed in v1.6 to support the integration of MetricFlow. It's strongly recommended that you refer to [Build your metrics](/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer. 
 
-The legacy Semantic Layer will be deprecated in H2 2023. Additionally, the `dbt_metrics` package will not be support in dbt 1.6 and above. If you are using `dbt_metrics` you will need to upgrade your configurations before upgrading to 1.6. This guide walks through the migration process for users of the legacy semantic layer who want to use to the new Semantic Layer. The estimated migration time is 2 weeks. 
+dbt Labs recommends completing these steps in a local dev environment instead of the IDE: 
 
-### Step 1: **Migrate Metric configs to the new spec.**
+1. Create new Semantic Model configs as YAML files in your dbt project. *
+1. Upgrade the metrics configs in your project to the new spec. * 
+1. Delete your old metrics file or remove the `.yml` file extension so they're ignored at parse time. Remove the `dbt-metrics` package from your project. Remove any macros that reference `dbt-metrics`, like `metrics.calculate()`. Make sure that any packages you’re using don't have references to the old metrics spec. 
+1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]`. For example: 
 
-The metrics specification in dbt core has changed in v1.6 in order to support the integration of MetricFlow. It is highly recommended that you read the [MetricFlow docs](https://docs.getdbt.com/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer.  We recommend completing these steps in a local dev environment instead of the IDE. To migrate your metrics configs: 
-**
+    ```bash
+    pip install "dbt-metricflow[snowflake]
+    ```
+    The MetricFlow CLI is not available in the IDE at this time. Support for it is coming soon. 
 
-1. Creating new Semantic Model configs as YAML files in your dbt project. *
-2. Upgrading the Metrics configs in your project to the new spec.* 
-3. Delete your old metrics file or remove .yml extension so they are ignored at parse time. Remove the `dbt-metrics` package form your project. Remove any macros that reference `dbt-metrics` i.e like `metrics.calculate()`. Make sure that any packages you’re using do not have references to the old metrics spec. 
-4. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`  i.e `pip install "dbt-metricflow[snowflake]` . The MetricFlow CLI is not available in the IDE. We’re working hard to support it soon 🙂.
-5. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.  MetricFlow needs this file to query metrics. You will need to re-parse your project when you make changes to your configs. 
-6. Run `mf list metrics` to view the metrics in your project
-7. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>` i.e `mf query --metrics revenue --group-by metric_time`
-8. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
-9. Push these changes to a new branch in your repo. 
+1. Run `dbt parse`. This parses your project and creates a `semantic_manifest.json` file in your target directory. MetricFlow needs this file to query metrics. If you make changes to your configs, you will need to parse your project again. 
+1. Run `mf list metrics` to view the metrics in your project.
+1. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`. For example:
+    ```bash
+    mf query --metrics revenue --group-by metric_time
+    ```
+1. Run `mf validate-configs` to run semantic and warehouse validations. This ensures your configs are valid and the underlying objects exist in your warehouse. 
+1. Push these changes to a new branch in your repo. 
 
-**To make this process easier, we have [developed a custom migration tool](https://github.com/dbt-labs/dbt-converter) that automates these steps for you.  You can find installation instructions in the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md).  Derived metrics aren’t supported in the migration tool, and will have to be migrated manualy*
+**To make this process easier, dbt Labs provides a [custom migration tool](https://github.com/dbt-labs/dbt-converter) that automates these steps for you. You can find installation instructions in the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md). Derived metrics aren’t supported in the migration tool, and will have to be migrated manually.*
 
-## Step 2: **Audit metric values after the migration**
+## Step 2: Audit metric values after the migration
 
-You may need to audit metric values during the migration to ensure that the historical values of key business metrics are the same. To audit you metric values follow these steps:
+You might need to audit metric values during the migration to ensure that the historical values of key business metrics are the same.
 
-1. In the CLI, query the metric(s) and dimensions you want to test and add the `--explain` option. For examples `mf query --metrics orders,revenue --group-by metric_time__month,customer_type --explain`
-2. Use the SQL MetricFlow generates to create a temporary model in your project i.e `tmp_orders_revenue audit.sql`. You will use the model to compare to your legacy metrics.
-3. If you haven’t already done so, create a model using metrics.calculate() for the metrics you want to compare i.e 
+1. In the CLI, query the metric(s) and dimensions you want to test and include the `--explain` option. For example:
+    ```bash 
+    mf query --metrics orders,revenue --group-by metric_time__month,customer_type --explain
+    ``` 
+1. Use SQL MetricFlow to create a temporary model in your project, like `tmp_orders_revenue audit.sql`. You will use this temporary model to compare against your legacy metrics.
+1. If you haven’t already done so, create a model using `metrics.calculate()` for the metrics you want to compare against. For example: 
 
-```bash
-select * 
-from {{ metrics.calculate(  
-[metric('orders)',
-metric('revenue)'],
-    grain='week',
-    dimensions=['metric_time', 'customer_type'],
-) }}
+    ```bash
+    select * 
+    from {{ metrics.calculate(  
+    [metric('orders)',
+    metric('revenue)'],
+        grain='week',
+        dimensions=['metric_time', 'customer_type'],
+    ) }}
+    ```
 
-```
+1. Run the [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
 
-1. Run [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
 
+## Related docs 
+
+- [MetricFlow quickstart guide](/docs/build/sl-getting-started)
+- [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
+- [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
+- [Why we're deprecating the dbt_metrics package](/blog/deprecating-dbt-metrics) blog post
\ No newline at end of file
diff --git a/website/sidebars.js b/website/sidebars.js
index f108e580695..e358c8474ce 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -979,6 +979,7 @@ const sidebarSettings = {
       type: "category",
       label: "Migration",
       items: [
+        "guides/migration/sl-migration",
         {
           type: "category",
           label: "Versions",

From 91c84a6d8dfdf19bdd21455657da6fdad118ccf8 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Tue, 1 Aug 2023 23:39:52 -0700
Subject: [PATCH 463/703] Update sl-partner-integration-guide.md

added a quick note about how to expose Explain
---
 .../guides/dbt-ecosystem/sl-partner-integration-guide.md     | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
index 1a7f5ef74a2..decad95a516 100644
--- a/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
+++ b/website/docs/guides/dbt-ecosystem/sl-partner-integration-guide.md
@@ -112,6 +112,11 @@ For better analysis, it's best to have the context of the metrics close to where
 
 - Allow for creating other metadata that’s useful for the metric. We can provide some of this information in our configuration (Display name, Default Granularity for View, Default Time range), but there may be other metadata that your tool wants to provide to make the metric richer.
 
+### A note on transparency and using explain
+
+For transparency and additional context, we recommend you have an easy way for the user to obtain the SQL that MetricFlow generates. You can do this by appending `explain=True` to any query. This is incredibly powerful because we want to be very transparent to the user about what we're doing and do not want to be a black box. This would be mostly a power user / technical user functionality.
+
+
 ### Example stages of an integration
 
 These are recommendations on how to evolve a Semantic Layer integration and not a strict runbook.

From 8f23228b130a36ac963cc7f10421231096121e65 Mon Sep 17 00:00:00 2001
From: Grace Goheen <53586774+graciegoheen@users.noreply.github.com>
Date: Wed, 2 Aug 2023 01:45:43 -0700
Subject: [PATCH 464/703] add focus areas to 1.6 release docs (#3844)

## What are you changing in this pull request and why?
<!---
Describe your changes and why you're making them. If linked to an open
issue or a pull request on dbt Core, then link to them here!

To learn more about the writing conventions used in the dbt Labs docs,
see the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md).
-->

Added three main focus areas for v1.6 release docs

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."
---
 .../docs/guides/migration/versions/01-upgrading-to-v1.6.md   | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index ac3d7348ef9..cb2eefbea18 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -3,6 +3,11 @@ title: "Upgrading to v1.6 (latest)"
 description: New features and changes in dbt Core v1.6
 ---
 
+dbt Core v1.6 has three significant areas of focus:
+1. Next milestone of [multi-project deployments](https://github.com/dbt-labs/dbt-core/discussions/6725): improvements to contracts, groups/access, versions; and building blocks for cross-project `ref`
+1. Semantic layer re-launch: dbt Core and [MetricFlow](https://docs.getdbt.com/docs/build/about-metricflow) integration
+1. Mechanisms to support mature deployment at scale (`dbt clone` and `dbt retry`)
+
 ## Resources
 
 - [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.6.latest/CHANGELOG.md)

From 9925229f71f42aad76b324755818c62e6753e3ee Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Wed, 2 Aug 2023 16:27:15 +0200
Subject: [PATCH 465/703] Add callout to homebrew docs (#3847)

Add callout to Homebrew docs, redirecting to:
- https://github.com/dbt-labs/dbt-core/discussions/8277
---
 website/docs/docs/core/homebrew-install.md                 | 7 +++++++
 .../docs/guides/migration/versions/01-upgrading-to-v1.6.md | 1 +
 2 files changed, 8 insertions(+)

diff --git a/website/docs/docs/core/homebrew-install.md b/website/docs/docs/core/homebrew-install.md
index ab80cc1148f..2e2676c4a95 100644
--- a/website/docs/docs/core/homebrew-install.md
+++ b/website/docs/docs/core/homebrew-install.md
@@ -3,6 +3,13 @@ title: "Install with Homebrew"
 description: "You can use Homebrew to install dbt Core and adapter plugins from the command line."
 ---
 
+:::caution
+
+Starting with v1.6, dbt Labs will no longer maintain Homebrew formulae as a supported installation method for dbt-core and adapters. For more on our rationale, consult this discussion:
+- [Installing dbt Core: saying goodbye to brew and hello to "bundles"](https://github.com/dbt-labs/dbt-core/discussions/8277)
+
+:::
+
 dbt Labs maintains Homebrew formulae for the four oldest and most popular adapter plugins: Postgres, Redshift, Snowflake, and BigQuery.
 
 We recommend you use Homebrew if you meet these conditions:
diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index cb2eefbea18..22111828e24 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -24,6 +24,7 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 - dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
 - As part of the Semantic layer re-launch (in beta), the spec for `metrics` has changed significantly. Migration guide coming soon: https://github.com/dbt-labs/docs.getdbt.com/pull/3705
 - The manifest schema version is now v10.
+- dbt Labs is ending support for Homebrew installation of dbt-core and adapters. See [the discussion](https://github.com/dbt-labs/dbt-core/discussions/8277) for more details.
 
 ### For consumers of dbt artifacts (metadata)
 

From 2b5d750592ea6c24e6026f550f8b26d7b65c967d Mon Sep 17 00:00:00 2001
From: rtol5 <110069999+rtol5@users.noreply.github.com>
Date: Wed, 2 Aug 2023 13:19:46 -0700
Subject: [PATCH 466/703] Fix warehouse->snowflake_warehouse in MV
 documentation

---
 website/docs/reference/resource-configs/snowflake-configs.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 1e113d6a3b1..09ebe11f4c7 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -374,7 +374,7 @@ The following examples create a dynamic table:
 {{
   config(
     materialized = 'dynamic_table',
-    warehouse = 'snowflake_warehouse',
+    snowflake_warehouse = 'snowflake_warehouse',
     target_lag = '10 minutes',
   )
 }}
@@ -388,7 +388,7 @@ The following examples create a dynamic table:
 models:
   path:
     materialized: dynamic_table
-    warehouse: snowflake_warehouse
+    snowflake_warehouse: snowflake_warehouse
     target_lag: '10 minutes'
 ```
 

From fd72047756d754b27ed50d888c8269ee6468e6f4 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 16:42:07 -0400
Subject: [PATCH 467/703] add in new blog

---
 ...023-08-01-announcing-materialized-views.md | 256 ++++++++++++++++++
 .../streaming-pipeline.png                    | Bin 0 -> 392403 bytes
 2 files changed, 256 insertions(+)
 create mode 100644 website/blog/2023-08-01-announcing-materialized-views.md
 create mode 100644 website/static/img/blog/2023-08-01-announcing-materialized-views/streaming-pipeline.png

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
new file mode 100644
index 00000000000..ffa6d527bd6
--- /dev/null
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -0,0 +1,256 @@
+---
+title: "Introducing Materialized Views and how to use them"
+description: "In dbt v1.6, we introduce support for materialized views. In this blog post, Amy will review how to use them in your workflow"
+slug: announcing-materialized-views
+
+authors: [amy chen]
+
+tags: [analytics craft, dbt product updates]
+hide_table_of_contents: false
+
+date: 2023-08-01
+is_featured: true
+---
+
+## What's happening?
+
+The year was 2020. I was a kitten-only household, and dbt Labs was still Fishtown Analytics. A major customer I was working with, Jetblue, asked me for help running their dbt models every 2 minutes to meet a 5 minute SLA.
+
+After getting over the initial terror, we talked through the use case and soon realized there was a better option. Together with my team, I created [lambda views](https://discourse.getdbt.com/t/how-to-create-near-real-time-models-with-just-dbt-sql/1457%20?) to meet the need.
+
+Flash forward to 2023. I’m writing this as my giant dog snores next to me (don’t worry the cats have multiplied as well). Jetblue has outgrown lambda views due to performance constraints (a view can only be so performant) and we are at another milestone in dbt’s journey to support streaming. What. a. time.
+
+Today we are announcing that we now support Materialized Views in dbt. So, what does that mean?
+
+Materialized views are now an out of the box materialization in your dbt project once you upgrade to the latest version of dbt v1.6 on these following adapters:
+
+- dbt-postgres
+- dbt-redshift
+- dbt-snowflake
+- dbt-databricks
+- dbt-materialize*
+- dbt-trino*
+- dbt-bigquery**
+
+* These adapters have supported materialized views in their adapter prior 1.6.
+** dbt-bigquery support will be coming in 1.7.
+
+Just like you would materialize your sql model as  `table` or `view`  today, you can use `materialized_view` in your model configuration, dbt_project.yml, and resources.yml files. At release, python models will not be supported.
+
+
+
+For Postgres/Redshift/Databricks
+
+```sql
+
+{{
+
+config(
+
+    materialized = 'materialized_view',
+
+)
+
+}}
+
+```
+
+For Snowflake:
+
+```sql
+
+{{
+
+config(
+
+    materialized = 'dynamic_table',
+
+)
+
+}}
+
+```
+
+:::note
+We are only supporting dynamic tables on Snowflake, not Snowflake’s materialized views (for a comparison between Snowflake Dynamic Tables and Materialized Views, refer docs). Dynamic tables are better suited for continuous transformations due to functionality like the ability to join, union, and aggregate on base tables, views , and other dynamic tables. Due to those features, they are also more aligned with what other data platforms are calling Materialized Views. For the sake of simplicity, when I refer to materialized views in this blog, I mean dynamic tables in Snowflake.
+:::
+
+Now that we support materialized views: how do you fit them into your dbt workflow? It’s easy to imagine a world of unregulated computation because you didn’t put in proper guardrails and now you have materialized views running rampant unbeknownst to you in your data platform.
+
+Materialized views, just like any other materialization, fit a need and you should utilize them while taking into consideration the additional complexity they will add to your project. They are a tool in your analytics engineering toolbox, one of many.
+
+In this blog, we will go over when to pull this tool out of your toolbox, how to wield it successfully, and how to promote materialized views with governance in mind. Now this is a new functionality and I expect this to be the first of many posts to come, defining our best practices (or even redefining them). Also I will not be discussing dbt’s interactions upstream from the data platform like how to manage your Kafka topics using dbt, but would highly recommend [this post from Charlie Summers](https://docs.getdbt.com/blog/demystifying-event-streams) if that’s something you’re interested in.
+
+Additionally, if you want to get a more detailed understanding of your data platform’s support of materialized views, I recommend checking out dbt’s and your data platform’s documentation site. This blog post is intended to be a high level, platform agnostic overview to get you started.
+
+## What are Materialized Views?
+
+Starting out with, **what are materialized views (MVs)?** While specific features will vary by data platform, materialized views at their core are database objects that have stored the results of a query as a physically materialized table. What makes them distinct from a regular table is that the data in a materialized view is periodically refreshed to reflect the latest changes in the underlying table. Because they’re precomputed and the results are stored, you have faster query times when accessing them because you aren’t recomputing the data from scratch. This is great when you have low latency requirements for your data pipelines.
+
+Now you might have noticed that MVs sound a lot like incremental models, and you are not wrong! It can be worthwhile to think of materialized views as a successor of sorts to incremental models. In fact, depending on your needs and data platform of choice, you might wish to replace all of your incremental dbt models with materialized view models. By doing this, you will no longer have to manually craft specific incremental strategies, detailing how dbt should update the underlying table. Awesome, right?
+
+The tradeoff (outside of any data platform specific ones) is that you will have less fine-grained control over the incremental logic and orchestration. This is because you are handing defining the logic of what and how to update the existing table over to the data platform to perform for you.
+
+Other factors to consider when deciding on when/how to use a materialized view:
+- What are the costs associated with running the materialized view versus a batched incremental model? (this will vary depending on your data platform as some will require different compute nodes)
+- Does your data platform support joins, aggregations, and window functions on MVs if you need them?
+- What are the latency needs of your development environment? In production? (If not near real time, you can make the choice between a batch incremental model or a MV with a longer refresh schedule.)
+- How often do your upstream dependencies update? If your answer is `not frequent`, you may not need a MV.
+- How large is your dataset?(It might be cheaper to use MVs for extremely large datasets)
+- How often do you need your query refreshed? What are your downstream dependencies and their stakeholders? (If near real time is important, MVs might be the right choice).
+- Do you have real time machine learning models training or applications using your transformed dataset?
+
+### Development
+
+When we talk about using materialized views in development, the question to think about is not so much “should you execute your dbt models as materialized views in your sandbox?,” but rather “should you schedule them to refresh in your sandbox?”. For development, you do need to create them and test them out in your sandbox but how do you do this in a way that doesn’t drive up your cloud bill unnecessarily? Or keeping a post-it note on your laptop as a reminder to drop all of the running materialized views in your sandbox before you sign off? Let’s talk about it!
+
+Outside of the scheduling part, development will be pretty standard. Your pipeline is likely going to look something like this:
+
+<Lightbox src="/img/blog/2023-08-01-announcing-materialized-views/streaming-pipeline.png" title="A Real Time Pipeline" />
+
+This is assuming you have a near real time pipeline where you are pulling from a streaming data source like a Kafka Topic via an ingestion tool of your choice like Snowpipe for Streaming into your data platform. After your data is in the data platform, you will:
+
+1. Create the dbt model with the SQL transformation logic that you need.
+2. Look at the logic and answer these questions:
+    1. Does my data platform support the functionality I need in materialized views?
+    2. How often do you need the data refreshed? Do you need any flexibility in that?
+    3. How am I promoting this into production? Either you will run the transformation logic in the production environment (recommended) and create a separate object or promote the object created from development.
+
+
+Depending on your answer, this will decide if you want a materialized view in the first place (versus a view, table, or incremental model). If you have decided on a materialized view as meeting your needs, by default do not schedule a refresh. You can run manual refreshes as needed. Why’s that? In your development environment, you are likely validating three things: the dependencies, the SQL logic, and the transformation output. All of those can be tested by creating a materialized view without scheduling and running manually refreshes.
+
+Your configuration during development:
+
+For Postgres:
+
+Every time you run a `dbt run`, that will result in a manual refresh unless you set the `on_configuration_change` to `continue` which will skip running the model.
+
+```sql
+{{
+
+config(
+
+    materialized = 'materialized_view',
+
+    on_configuration_change = 'apply',
+
+)
+
+}}
+```
+
+For Redshift:
+
+```sql
+
+{{
+
+config(
+
+    materialized = 'materialized_view',
+
+    on_configuration_change = 'apply',
+
+    uto_refresh =False
+
+)
+
+}}
+```
+
+For Databricks:
+
+```sql
+{{
+
+config(
+
+materialized='materialized_view',
+
+post_hook = 'alter materialized view {{this}} add SCHEDULE CRON "0 0 0 * * ? *" AT TIME ZONE "America/Los_Angeles";'
+
+)
+
+}}
+```
+
+By default, materialized views are not refreshed on a schedule on Databricks in this materialization. To set up scheduling, you can use a post-hook to alter the MV with a cron schedule that will run in Databricks Workflows.
+
+For Snowflake:
+
+```sql
+{{
+
+config(
+
+materialized = 'dynamic_table',
+
+snowflake_warehouse = ‘<warehouse>’,
+
+target_lag = ‘<desired_lag>`,
+
+on_configuration_change = 'apply',
+
+)
+
+}}
+```
+
+Now if you do need to more fully build out your development pipeline (making sure scheduling/syncs do happen), you can schedule but make sure to drop the materialized views when you are done with them. I encourage you to invest in an operations macro that drops all MVs in the schema that you use as your sandbox and run it as needed. You could even create a dbt Cloud job to manage that. This way, you don’t have any stray MVs running in your sandbox, consuming credits unnecessarily.
+
+### Testing
+
+Now let’s dive into the second question: how do you test? In development and QA, this will look the same as any batch run tests. You can run `dbt build` or  `dbt test` and then have the tests run after execution as validation. But in production, what can you do to continually test? Your options are:
+
+- Continue to do batch testing as we wait for [materialized tests](https://github.com/dbt-labs/dbt-core/issues/6914)
+- Or overriding the –store-failures macro like what Materialize has created [here](https://materialize.com/blog/real-time-data-quality-tests-using-dbt-and-materialize/) for their adapter to materialize failed rows as a materialized view. This is not a great solution for the long term but if you have urgency to put this into production, it is an option.
+
+In order to promote materialized views into production, the process will look very much like it did with your incremental models. Using SlimCI, for new MVs, you can build them into your QA environment. For existing MVs without changes, we can skip and defer to the production objects.
+
+### Production
+
+When you feel satisfied with your development and testing, for data platforms that offer scheduling via our dbt configurations, you have two options: hardcode the refresh cadence or write in conditional logic based on the environment for the refresh cadence. I recommend using the latter.
+
+The code for having a conditional in your config block looks like this if you want to include in a macro for either the lag or other fields (warehouse, auto-refresh,etc):
+
+```sql
+
+{% macro target_lag_environment() %}
+
+{% set lag = '1 minute' if target.name == "prod" else '35 days' %}
+
+{{ return(lag) }}
+
+{% endmacro %}
+
+```
+
+```sql
+
+{{
+
+config(
+
+materialized = 'dynamic_table',
+
+snowflake_warehouse = 'transforming',
+
+target_lag = target_lag_environment(),
+
+on_configuration_change = 'apply',
+
+)
+
+}}
+
+```
+
+You will want a very long lag for development; I recommend the cadence you drop and refresh your development environment. Here I just chose my two favorite numbers.
+
+For orchestration, if your materialized views aren’t able to auto refresh, you can use dbt cloud to orchestrate your refreshes. The beauty of materialized views is that dbt will be able to provide the dependency/testing/documentation but also skip or rerun the models as configured, enabling you to version control your logic. Reasonable guardrails for the modern data stack. ✨
+
+Depending on how you orchestrate your materialized views, you can either run the testing in production as part of a scheduled job (with dbt test or dbt build).
+
+## Conclusion
+
+Well, I’m excited for everyone to remove the lines in your packages.yml that installed your experimental package (at least if you’re using it for MVs) and start to get your hands dirty. We are still new in our journey and I look forward to hearing all the things you are creating and how we can better our best practices in this.
\ No newline at end of file
diff --git a/website/static/img/blog/2023-08-01-announcing-materialized-views/streaming-pipeline.png b/website/static/img/blog/2023-08-01-announcing-materialized-views/streaming-pipeline.png
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z{F?Q#{^FM+)a?Gpj~`zzE-qH+MXXjk-u_e;(%XBPH>i_x1ZD?@I_v5r2F^gB_+>d*
zM<<}`E&rFRA1OA^e7r_SCxxS0oYXY3w8c~|BG?3&Zvx#0Z~>#TcQ=Wj17nzXxBoo*
jM=$?i<DD;j`qdKYCJ1TOUQ%hTeRAyElixf!6maE#oV|q)

literal 0
HcmV?d00001


From e745acfa931ae96d14632c92019692d8bc7e126c Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 16:48:25 -0400
Subject: [PATCH 468/703] fix author

---
 website/blog/2023-08-01-announcing-materialized-views.md | 4 ++--
 website/blog/authors.yml                                 | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index ffa6d527bd6..7fa56cc1999 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -3,9 +3,9 @@ title: "Introducing Materialized Views and how to use them"
 description: "In dbt v1.6, we introduce support for materialized views. In this blog post, Amy will review how to use them in your workflow"
 slug: announcing-materialized-views
 
-authors: [amy chen]
+authors: [amy_chen]
 
-tags: [analytics craft, dbt product updates]
+tags: [analytics craft, dbt product updates, data ecosystem]
 hide_table_of_contents: false
 
 date: 2023-08-01
diff --git a/website/blog/authors.yml b/website/blog/authors.yml
index e9b48bd02fc..2e554ffc814 100644
--- a/website/blog/authors.yml
+++ b/website/blog/authors.yml
@@ -1,6 +1,6 @@
 amy_chen:
   image_url: /img/blog/authors/achen.png
-  job_title: Senior Partner Engineer
+  job_title: Staff Partner Engineer
   links:
     - icon: fa-linkedin
       url: https://www.linkedin.com/in/yuanamychen/

From f0b9b741013b7ded69e4c8e2a9f04982c9f6fb31 Mon Sep 17 00:00:00 2001
From: Cameron Afzal <cameron.afzal@dbtlabs.com>
Date: Wed, 2 Aug 2023 13:51:13 -0700
Subject: [PATCH 469/703] Add screenshot to prod environment setup

---
 website/docs/docs/deploy/deploy-environments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md
index 553a7d2fcdd..143c56b4c2e 100644
--- a/website/docs/docs/deploy/deploy-environments.md
+++ b/website/docs/docs/deploy/deploy-environments.md
@@ -34,6 +34,8 @@ import ExpBeta from '/snippets/_explorer-beta-banner.md';
 
 <ExpBeta/>
 
+<img width="764" alt="image" src="https://github.com/dbt-labs/docs.getdbt.com/assets/38054082/39f63ab8-6b0d-4444-a659-3f34543aa96e">
+
 In dbt Cloud, each project can have one designated deployment environment, which serves as its production environment. This production environment is _essential_ for using features like dbt Explorer and cross-project references. It acts as the source of truth for the project's production state in dbt Cloud.
 
 ### Semantic Layer

From cdc759ec3fd39ed579c961c1206ac99ef155e366 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:09:42 -0400
Subject: [PATCH 470/703] fix spacing

---
 ...023-08-01-announcing-materialized-views.md | 57 +++----------------
 1 file changed, 8 insertions(+), 49 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 7fa56cc1999..78d39d4b5ce 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -14,7 +14,7 @@ is_featured: true
 
 ## What's happening?
 
-The year was 2020. I was a kitten-only household, and dbt Labs was still Fishtown Analytics. A major customer I was working with, Jetblue, asked me for help running their dbt models every 2 minutes to meet a 5 minute SLA.
+The year was 2020. I was a kitten-only household, and dbt Labs was still Fishtown Analytics. A enterprise customer I was working with, Jetblue, asked me for help running their dbt models every 2 minutes to meet a 5 minute SLA.
 
 After getting over the initial terror, we talked through the use case and soon realized there was a better option. Together with my team, I created [lambda views](https://discourse.getdbt.com/t/how-to-create-near-real-time-models-with-just-dbt-sql/1457%20?) to meet the need.
 
@@ -22,6 +22,8 @@ Flash forward to 2023. I’m writing this as my giant dog snores next to me (don
 
 Today we are announcing that we now support Materialized Views in dbt. So, what does that mean?
 
+<!--truncate-->
+
 Materialized views are now an out of the box materialization in your dbt project once you upgrade to the latest version of dbt v1.6 on these following adapters:
 
 - dbt-postgres
@@ -44,13 +46,9 @@ For Postgres/Redshift/Databricks
 ```sql
 
 {{
-
 config(
-
     materialized = 'materialized_view',
-
 )
-
 }}
 
 ```
@@ -58,17 +56,11 @@ config(
 For Snowflake:
 
 ```sql
-
 {{
-
 config(
-
     materialized = 'dynamic_table',
-
 )
-
 }}
-
 ```
 
 :::note
@@ -127,15 +119,10 @@ Every time you run a `dbt run`, that will result in a manual refresh unless you
 
 ```sql
 {{
-
 config(
-
     materialized = 'materialized_view',
-
     on_configuration_change = 'apply',
-
 )
-
 }}
 ```
 
@@ -144,15 +131,10 @@ For Redshift:
 ```sql
 
 {{
-
 config(
-
     materialized = 'materialized_view',
-
     on_configuration_change = 'apply',
-
-    uto_refresh =False
-
+    auto_refresh = False
 )
 
 }}
@@ -164,13 +146,9 @@ For Databricks:
 {{
 
 config(
-
 materialized='materialized_view',
-
 post_hook = 'alter materialized view {{this}} add SCHEDULE CRON "0 0 0 * * ? *" AT TIME ZONE "America/Los_Angeles";'
-
 )
-
 }}
 ```
 
@@ -180,19 +158,12 @@ For Snowflake:
 
 ```sql
 {{
-
 config(
-
 materialized = 'dynamic_table',
-
 snowflake_warehouse = ‘<warehouse>’,
-
 target_lag = ‘<desired_lag>`,
-
 on_configuration_change = 'apply',
-
 )
-
 }}
 ```
 
@@ -216,33 +187,21 @@ The code for having a conditional in your config block looks like this if you wa
 ```sql
 
 {% macro target_lag_environment() %}
-
 {% set lag = '1 minute' if target.name == "prod" else '35 days' %}
-
 {{ return(lag) }}
-
 {% endmacro %}
-
 ```
 
 ```sql
 
 {{
-
 config(
-
-materialized = 'dynamic_table',
-
-snowflake_warehouse = 'transforming',
-
-target_lag = target_lag_environment(),
-
-on_configuration_change = 'apply',
-
+    materialized = 'dynamic_table',
+    snowflake_warehouse = 'transforming',
+    target_lag = target_lag_environment(),
+    on_configuration_change = 'apply',
 )
-
 }}
-
 ```
 
 You will want a very long lag for development; I recommend the cadence you drop and refresh your development environment. Here I just chose my two favorite numbers.

From ee39aa983335974e2518cec26cee4087d02faab6 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:15:29 -0400
Subject: [PATCH 471/703] fix trunc

---
 website/blog/2023-08-01-announcing-materialized-views.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 78d39d4b5ce..258ae2aeab0 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -12,7 +12,7 @@ date: 2023-08-01
 is_featured: true
 ---
 
-## What's happening?
+## Introduction
 
 The year was 2020. I was a kitten-only household, and dbt Labs was still Fishtown Analytics. A enterprise customer I was working with, Jetblue, asked me for help running their dbt models every 2 minutes to meet a 5 minute SLA.
 
@@ -34,8 +34,8 @@ Materialized views are now an out of the box materialization in your dbt project
 - dbt-trino*
 - dbt-bigquery**
 
-* These adapters have supported materialized views in their adapter prior 1.6.
-** dbt-bigquery support will be coming in 1.7.
+*These adapters have supported materialized views in their adapter prior 1.6.
+**dbt-bigquery support will be coming in 1.7.
 
 Just like you would materialize your sql model as  `table` or `view`  today, you can use `materialized_view` in your model configuration, dbt_project.yml, and resources.yml files. At release, python models will not be supported.
 

From bcc8813e8e893e7de642704a73dd08b395f6f535 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:16:59 -0400
Subject: [PATCH 472/703] add a new header

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 258ae2aeab0..cf4635a33e0 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -92,6 +92,8 @@ Other factors to consider when deciding on when/how to use a materialized view:
 - How often do you need your query refreshed? What are your downstream dependencies and their stakeholders? (If near real time is important, MVs might be the right choice).
 - Do you have real time machine learning models training or applications using your transformed dataset?
 
+## Materialized Views in the dbt Workflow
+
 ### Development
 
 When we talk about using materialized views in development, the question to think about is not so much “should you execute your dbt models as materialized views in your sandbox?,” but rather “should you schedule them to refresh in your sandbox?”. For development, you do need to create them and test them out in your sandbox but how do you do this in a way that doesn’t drive up your cloud bill unnecessarily? Or keeping a post-it note on your laptop as a reminder to drop all of the running materialized views in your sandbox before you sign off? Let’s talk about it!

From 64e72d74181148786fc86fb543d71f7b5c332e9c Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:21:13 -0400
Subject: [PATCH 473/703] fix spacing

---
 .../blog/2023-08-01-announcing-materialized-views.md | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index cf4635a33e0..9cf484bb497 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -44,7 +44,6 @@ Just like you would materialize your sql model as  `table` or `view`  today, y
 For Postgres/Redshift/Databricks
 
 ```sql
-
 {{
 config(
     materialized = 'materialized_view',
@@ -54,7 +53,6 @@ config(
 ```
 
 For Snowflake:
-
 ```sql
 {{
 config(
@@ -131,14 +129,12 @@ config(
 For Redshift:
 
 ```sql
-
 {{
 config(
     materialized = 'materialized_view',
     on_configuration_change = 'apply',
     auto_refresh = False
 )
-
 }}
 ```
 
@@ -146,7 +142,6 @@ For Databricks:
 
 ```sql
 {{
-
 config(
 materialized='materialized_view',
 post_hook = 'alter materialized view {{this}} add SCHEDULE CRON "0 0 0 * * ? *" AT TIME ZONE "America/Los_Angeles";'
@@ -162,8 +157,8 @@ For Snowflake:
 {{
 config(
 materialized = 'dynamic_table',
-snowflake_warehouse = ‘<warehouse>’,
-target_lag = ‘<desired_lag>`,
+snowflake_warehouse = '<warehouse>',
+target_lag = '<desired_lag>',
 on_configuration_change = 'apply',
 )
 }}
@@ -184,7 +179,7 @@ In order to promote materialized views into production, the process will look ve
 
 When you feel satisfied with your development and testing, for data platforms that offer scheduling via our dbt configurations, you have two options: hardcode the refresh cadence or write in conditional logic based on the environment for the refresh cadence. I recommend using the latter.
 
-The code for having a conditional in your config block looks like this if you want to include in a macro for either the lag or other fields (warehouse, auto-refresh,etc):
+The code for having a conditional in your config block looks like this if you want to include in a macro for either the lag or other fields (snowflake_warehouse, auto_refresh,etc):
 
 ```sql
 
@@ -195,7 +190,6 @@ The code for having a conditional in your config block looks like this if you wa
 ```
 
 ```sql
-
 {{
 config(
     materialized = 'dynamic_table',

From 2f599b494001064e990884fbf70a4b4236378199 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:22:56 -0400
Subject: [PATCH 474/703] spaces will be the death of me

---
 website/blog/2023-08-01-announcing-materialized-views.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 9cf484bb497..8ba4a037a0c 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -182,7 +182,6 @@ When you feel satisfied with your development and testing, for data platforms th
 The code for having a conditional in your config block looks like this if you want to include in a macro for either the lag or other fields (snowflake_warehouse, auto_refresh,etc):
 
 ```sql
-
 {% macro target_lag_environment() %}
 {% set lag = '1 minute' if target.name == "prod" else '35 days' %}
 {{ return(lag) }}

From 906f00f6034656725be8189acd6adba17b48d7cc Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Wed, 2 Aug 2023 17:32:35 -0400
Subject: [PATCH 475/703] Fixing broken internal links

---
 website/docs/docs/build/jinja-macros.md                         | 2 +-
 website/docs/docs/build/python-models.md                        | 2 +-
 website/docs/docs/cloud/git/connect-github.md                   | 2 +-
 website/docs/docs/collaborate/govern/model-contracts.md         | 2 +-
 website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md  | 2 +-
 .../materializations-guide-3-configuring-materializations.md    | 2 +-
 .../materializations-guide-4-incremental-models.md              | 2 +-
 website/docs/guides/migration/tools/refactoring-legacy-sql.md   | 2 +-
 website/docs/guides/orchestration/webhooks/zapier-ms-teams.md   | 2 +-
 .../docs/guides/orchestration/webhooks/zapier-new-cloud-job.md  | 2 +-
 .../guides/orchestration/webhooks/zapier-refresh-mode-report.md | 2 +-
 .../orchestration/webhooks/zapier-refresh-tableau-workbook.md   | 2 +-
 website/docs/guides/orchestration/webhooks/zapier-slack.md      | 2 +-
 website/docs/quickstarts/snowflake-qs.md                        | 2 +-
 14 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/website/docs/docs/build/jinja-macros.md b/website/docs/docs/build/jinja-macros.md
index 538a3a5e4c6..44bc85872f5 100644
--- a/website/docs/docs/build/jinja-macros.md
+++ b/website/docs/docs/build/jinja-macros.md
@@ -126,7 +126,7 @@ from app_data.payments
 
 
 ### Using a macro from a package
-A number of useful macros have also been grouped together into [packages](docs/build/packages) — our most popular package is [dbt-utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/).
+A number of useful macros have also been grouped together into [packages](/docs/build/packages) — our most popular package is [dbt-utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/).
 
 After installing a package into your project, you can use any of the macros in your own project — make sure you qualify the macro by prefixing it with the [package name](/reference/dbt-jinja-functions/project_name):
 
diff --git a/website/docs/docs/build/python-models.md b/website/docs/docs/build/python-models.md
index 5b9222ad1c5..12825648501 100644
--- a/website/docs/docs/build/python-models.md
+++ b/website/docs/docs/build/python-models.md
@@ -146,7 +146,7 @@ with upstream_python_model as (
 
 :::caution
 
-Referencing [ephemeral](docs/build/materializations#ephemeral) models is currently not supported (see [feature request](https://github.com/dbt-labs/dbt-core/issues/7288)) 
+Referencing [ephemeral](/docs/build/materializations#ephemeral) models is currently not supported (see [feature request](https://github.com/dbt-labs/dbt-core/issues/7288)) 
 :::
 
 ## Configuring Python models
diff --git a/website/docs/docs/cloud/git/connect-github.md b/website/docs/docs/cloud/git/connect-github.md
index d5ead96d940..5d27012195d 100644
--- a/website/docs/docs/cloud/git/connect-github.md
+++ b/website/docs/docs/cloud/git/connect-github.md
@@ -56,7 +56,7 @@ If you are your GitHub organization owner, you can also configure the dbt Cloud
 
 ## Personally authenticate with GitHub
 
-Once the dbt Cloud admin has [set up a connection](docs/cloud/git/connect-github#installing-dbt-cloud-in-your-github-account) to your organization GitHub account, you need to personally authenticate, which improves the security of dbt Cloud by enabling you to log in using OAuth through GitHub.
+Once the dbt Cloud admin has [set up a connection](/docs/cloud/git/connect-github#installing-dbt-cloud-in-your-github-account) to your organization GitHub account, you need to personally authenticate, which improves the security of dbt Cloud by enabling you to log in using OAuth through GitHub.
 :::infoGitHub profile connection
 - dbt Cloud developers on the [Enterprise plan](https://www.getdbt.com/pricing/) must each connect their GitHub profiles to dbt Cloud. This is because the dbt Cloud IDE verifies every developer's read / write access for the dbt repo. 
 
diff --git a/website/docs/docs/collaborate/govern/model-contracts.md b/website/docs/docs/collaborate/govern/model-contracts.md
index 97667996194..339098adbdc 100644
--- a/website/docs/docs/collaborate/govern/model-contracts.md
+++ b/website/docs/docs/collaborate/govern/model-contracts.md
@@ -98,7 +98,7 @@ Any model meeting the criteria described above _can_ define a contract. We recom
 
 A model's contract defines the **shape** of the returned dataset. If the model's logic or input data doesn't conform to that shape, the model does not build.
 
-[Tests](docs/build/tests) are a more flexible mechanism for validating the content of your model _after_ it's built. So long as you can write the query, you can run the test. Tests are more configurable, such as with [custom severity thresholds](/reference/resource-configs/severity). They are easier to debug after finding failures, because you can query the already-built model, or [store the failing records in the data warehouse](/reference/resource-configs/store_failures).
+[Tests](/docs/build/tests) are a more flexible mechanism for validating the content of your model _after_ it's built. So long as you can write the query, you can run the test. Tests are more configurable, such as with [custom severity thresholds](/reference/resource-configs/severity). They are easier to debug after finding failures, because you can query the already-built model, or [store the failing records in the data warehouse](/reference/resource-configs/store_failures).
 
 In some cases, you can replace a test with its equivalent constraint. This has the advantage of guaranteeing the validation at build time, and it probably requires less compute (cost) in your data platform. The prerequisites for replacing a test with a constraint are:
 - Making sure that your data platform can support and enforce the constraint that you need. Most platforms only enforce `not_null`.
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
index 95962610f8d..320bebad3bf 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
@@ -107,7 +107,7 @@ To read about best practices on structuring and organizing your metrics, review
 You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
 
 - [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project
-- [dbt metrics](docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
+- [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
 - [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
 - [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
     
diff --git a/website/docs/guides/best-practices/materializations/materializations-guide-3-configuring-materializations.md b/website/docs/guides/best-practices/materializations/materializations-guide-3-configuring-materializations.md
index 2f6c04bd35d..54f4443b600 100644
--- a/website/docs/guides/best-practices/materializations/materializations-guide-3-configuring-materializations.md
+++ b/website/docs/guides/best-practices/materializations/materializations-guide-3-configuring-materializations.md
@@ -53,7 +53,7 @@ def model(dbt, session):
 </Tabs>
 
 :::info
-🐍 **Not all adapters support python yet**, check the [docs here to be sure](docs/build/python-models#specific-data-platforms) before spending time writing python models.
+🐍 **Not all adapters support python yet**, check the [docs here to be sure](/docs/build/python-models#specific-data-platforms) before spending time writing python models.
 :::
 
 - Configuring a model to materialize as a `table` is simple, and the same as a `view` for both SQL and python models.
diff --git a/website/docs/guides/best-practices/materializations/materializations-guide-4-incremental-models.md b/website/docs/guides/best-practices/materializations/materializations-guide-4-incremental-models.md
index c1a4cb3eb0e..603cbc8cda1 100644
--- a/website/docs/guides/best-practices/materializations/materializations-guide-4-incremental-models.md
+++ b/website/docs/guides/best-practices/materializations/materializations-guide-4-incremental-models.md
@@ -115,7 +115,7 @@ So we’re going to use an **if statement** to apply our cutoff filter **only wh
 
 Thankfully, we don’t have to dig into the guts of dbt to sort out each of these conditions individually.
 
-- ⚙️  dbt provides us with a **macro [`is_incremental`](docs/build/incremental-models#understanding-the-is_incremental-macro)** that checks all of these conditions for this exact use case.
+- ⚙️  dbt provides us with a **macro [`is_incremental`](/docs/build/incremental-models#understanding-the-is_incremental-macro)** that checks all of these conditions for this exact use case.
 - 🔀  By **wrapping our cutoff logic** in this macro, it will only get applied when the macro returns true for all of the above conditions.
 
 Let’s take a look at all these pieces together:
diff --git a/website/docs/guides/migration/tools/refactoring-legacy-sql.md b/website/docs/guides/migration/tools/refactoring-legacy-sql.md
index 9dd66abb495..d9acfea6dab 100644
--- a/website/docs/guides/migration/tools/refactoring-legacy-sql.md
+++ b/website/docs/guides/migration/tools/refactoring-legacy-sql.md
@@ -59,7 +59,7 @@ This allows you to call the same table in multiple places with `{{ src('my_sourc
 We start here for several reasons:
 
 #### Source freshness reporting
-Using sources unlocks the ability to run [source freshness reporting](docs/build/sources#snapshotting-source-data-freshness) to make sure your raw data isn't stale.
+Using sources unlocks the ability to run [source freshness reporting](/docs/build/sources#snapshotting-source-data-freshness) to make sure your raw data isn't stale.
 
 #### Easy dependency tracing
 If you're migrating multiple stored procedures into dbt, with sources you can see which queries depend on the same raw tables.
diff --git a/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md b/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
index aa95b999d4c..bb3f03ef0c0 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-ms-teams.md
@@ -27,7 +27,7 @@ In order to set up the integration, you should have familiarity with:
 **Note**: To receive the message, add the Zapier app to the team's channel during installation.
 
 ### 2. Create a new Zap in Zapier
-Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
+Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](/docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
 
 Press **Continue**, then copy the webhook URL. 
 
diff --git a/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md b/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
index 49b01d0db7e..0764c6c7911 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-new-cloud-job.md
@@ -16,7 +16,7 @@ In order to set up the integration, you should have familiarity with:
 ## Integration steps
 
 ### 1. Create a new Zap in Zapier
-Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
+Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](/docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
 
 Press **Continue**, then copy the webhook URL. 
 
diff --git a/website/docs/guides/orchestration/webhooks/zapier-refresh-mode-report.md b/website/docs/guides/orchestration/webhooks/zapier-refresh-mode-report.md
index 99680c432b3..f682baae8e2 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-refresh-mode-report.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-refresh-mode-report.md
@@ -22,7 +22,7 @@ In order to set up the integration, you should have familiarity with:
 ## Integration steps
 
 ### 1. Create a new Zap in Zapier
-Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
+Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](/docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
 
 Press **Continue**, then copy the webhook URL. 
 
diff --git a/website/docs/guides/orchestration/webhooks/zapier-refresh-tableau-workbook.md b/website/docs/guides/orchestration/webhooks/zapier-refresh-tableau-workbook.md
index 8751528565c..52a9ae63523 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-refresh-tableau-workbook.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-refresh-tableau-workbook.md
@@ -25,7 +25,7 @@ To set up the integration, you need to be familiar with:
 To authenticate with the Tableau API, obtain a [Personal Access Token](https://help.tableau.com/current/server/en-us/security_personal_access_tokens.htm) from your Tableau Server/Cloud instance. In addition, make sure your Tableau workbook uses data sources that allow refresh access, which is usually set when publishing.
 
 ### 2. Create a new Zap in Zapier
-To trigger an action with the delivery of a webhook in Zapier, you'll want to create a new Zap with **Webhooks by Zapier** as the Trigger and **Catch Raw Hook** as the Event.  However, if you choose not to [validate the authenticity of your webhook](docs/deploy/webhooks#validate-a-webhook), which isn't recommended, you can choose **Catch Hook** instead.  
+To trigger an action with the delivery of a webhook in Zapier, you'll want to create a new Zap with **Webhooks by Zapier** as the Trigger and **Catch Raw Hook** as the Event.  However, if you choose not to [validate the authenticity of your webhook](/docs/deploy/webhooks#validate-a-webhook), which isn't recommended, you can choose **Catch Hook** instead.  
 
 Press **Continue**, then copy the webhook URL. 
 
diff --git a/website/docs/guides/orchestration/webhooks/zapier-slack.md b/website/docs/guides/orchestration/webhooks/zapier-slack.md
index d3b0473502b..c9046ee9943 100644
--- a/website/docs/guides/orchestration/webhooks/zapier-slack.md
+++ b/website/docs/guides/orchestration/webhooks/zapier-slack.md
@@ -25,7 +25,7 @@ In order to set up the integration, you should have familiarity with:
 ## Integration steps
 
 ### 1. Create a new Zap in Zapier
-Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
+Use **Webhooks by Zapier** as the Trigger, and **Catch Raw Hook** as the Event. If you don't intend to [validate the authenticity of your webhook](/docs/deploy/webhooks#validate-a-webhook) (not recommended!) then you can choose **Catch Hook** instead. 
 
 Click **Continue**, then copy the webhook URL. 
 
diff --git a/website/docs/quickstarts/snowflake-qs.md b/website/docs/quickstarts/snowflake-qs.md
index 6d03586e611..0561ea13410 100644
--- a/website/docs/quickstarts/snowflake-qs.md
+++ b/website/docs/quickstarts/snowflake-qs.md
@@ -138,7 +138,7 @@ There are two ways to connect dbt Cloud to Snowflake. The first option is Partne
 <Tabs>
 <TabItem value="partner-connect" label="Use Partner Connect" default>
 
-Using Partner Connect allows you to create a complete dbt account with your [Snowflake connection](docs/cloud/connect-data-platform/connect-snowflake), [a managed repository](/docs/collaborate/git/managed-repository), [environments](/docs/build/custom-schemas#managing-environments), and credentials.
+Using Partner Connect allows you to create a complete dbt account with your [Snowflake connection](/docs/cloud/connect-data-platform/connect-snowflake), [a managed repository](/docs/collaborate/git/managed-repository), [environments](/docs/build/custom-schemas#managing-environments), and credentials.
 
 1. In the Snowflake UI, click on the home icon in the upper left corner. In the left sidebar, select **Admin**. Then, select **Partner Connect**. Find the dbt tile by scrolling or by searching for dbt in the search bar. Click the tile to connect to dbt.
 

From a773d5ebf521ecdfc2a2f7808e7d3bb7101eb628 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Wed, 2 Aug 2023 15:48:59 -0600
Subject: [PATCH 476/703] update dims and add primary entity example

---
 website/docs/docs/build/about-metricflow.md   |   2 +-
 website/docs/docs/build/derived-metrics.md    |   4 +-
 website/docs/docs/build/dimensions.md         |  32 ++-
 website/docs/docs/build/metrics-overview.md   |  12 +-
 website/docs/docs/build/ratio-metrics.md      |   2 +-
 website/docs/docs/build/semantic-models.md    |  12 +-
 website/docs/docs/build/simple.md             |  31 ++-
 website/docs/guides/migration/sl-migration.md | 202 ++++++------------
 8 files changed, 124 insertions(+), 173 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 38b64f492cb..154e40d515d 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -234,7 +234,7 @@ metrics:
       numerator: food_order
       denominator: active_customers
     filter: |
-      {{dimension('is_new_customer')}} = true
+      {{ Dimension('customer__is_new_customer')}} = true
 ```
 </TabItem>
 </Tabs>
diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index 010c86c2c33..acfd6935208 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -50,11 +50,11 @@ metrics:
         - name: order_total
           alias: revenue
           filter: |
-            {{dimension('is_food_order')}} = True
+            {{ Dimension('order__is_food_order') }} = True
         - name: order_cost
           alias: cost
           filter: |
-            {{dimension('is_food_order')}} = True
+            {{ Dimension('order__is_food_order') }} = True
   - name: order_total_growth_mom
     description: "Percentage growth of orders total completed to 1 month ago"
     type: derived
diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index d154141791f..fe2fa802e17 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -8,13 +8,14 @@ tags: [Metrics, Semantic Layer]
 
 Dimensions is a way to group or filter information based on categories or time. It's like a special label that helps organize and analyze data. 
 
-In a data platform, dimensions is part of a larger structure called a semantic model. It's created along with other elements like [entities](/docs/build/entities) and [measures](/docs/build/measures), and used to add more details to your data that can't be easily added up or combined.  In SQL, dimensions is typically included in the `dimensions` clause of your SQL query.
+In a data platform, dimensions is part of a larger structure called a semantic model. It's created along with other elements like [entities](/docs/build/entities) and [measures](/docs/build/measures), and used to add more details to your data that can't be easily added up or combined.  In SQL, dimensions is typically included in the `group by` clause of your SQL query.
+
 
 <!--dimensions are non-aggregatable expressions that define the level of aggregation for a metric used to define how data is sliced or grouped in a metric. Since groups can't be aggregated, they're considered to be a property of the primary or unique entity of the table.
 
 Groups are defined within semantic models, alongside entities and measures, and correspond to non-aggregatable columns in your dbt model that provides categorical or time-based context. In SQL, dimensions  is typically included in the GROUP BY clause.-->
 
-All dimensions require a `name`, `type` and in most cases, an `expr` parameter. 
+All dimensions require a `name`, `type` and in some cases, an `expr` parameter. 
 
 | Name | Parameter | Field type |
 | --- | --- | --- |
@@ -60,13 +61,28 @@ semantic_models:
       expr: case when quantity > 10 then true else false end
 ```
 
+Metricflow requires that all dimensions have a primary entity. If your data source does not have a primary entity, you will need to specfy one. 
+
+```yaml:
+semantic_model:
+  name: bookings_monthly_source
+  description: bookings_monthly_source
+  defaults:
+    agg_time_dimension: ds
+  model: ref('bookings_monthly_source')
+  measures:
+    - name: bookings_monthly
+      agg: sum
+      create_metric: true
+  primary_entity: booking_id
+  ```
+
 ## Dimensions types
 
-Dimensions have three types. This section further explains the definitions and provides examples.
+Dimensions have 2 types. This section further explains the definitions and provides examples.
 
 1. [Categorical](#categorical)
 1. [Time](#time)
-1. [Slowly changing](#scd-type-ii)
 
 ### Categorical
 
@@ -91,9 +107,9 @@ To use BigQuery as your data platform, time dimensions columns need to be in the
 
 <TabItem value="is_primary" label="is_primary">
 
-To specify the default time dimensions for a measure or metric in MetricFlow, set the `is_primary` parameter to True. If your semantic model has multiple time dimensions, the non-primary ones should have `is_primary` set to False. To assign non-primary time dimensions to a measure, use the `agg_time_dimension` parameter and refer to the time dimensions defined in the section. 
+To specify the default time dimensions for a measure or metric in MetricFlow, set the `agg_time_dimension` in the `defaults` section. To override the default and aggregation on diffrent time dimension, set the `agg_time_dimension` parameter on a measure.
 
-In the provided example, the semantic model has two-time groups, `created_at` and `deleted_at`, with `created_at` being the primary time dimension through `is_primary: True`. The `users_created` measure defaults to the primary time dimensions, while the `users_deleted` measure uses `deleted_at` as its time group. 
+In the provided example, the semantic model has two-time groups, `created_at` and `deleted_at`, with `created_at` being the default time dimension. The `users_deleted` measure overrides the default and uses `deleted_at` as its time group. 
 
 ```yaml
 dimensions: 
@@ -231,7 +247,7 @@ The following basic structure of an SCD Type II data platform table is supported
 
 **Note**: The SCD dimensions table must have `valid_to` and `valid_from` columns.
 
-This is an example of SQL code that shows how a sample metric called `num_events` is joined with versioned dimensions data (stored in a table called `scd_dimensions`) using a natural key made up of the `entity_key` and `timestamp` columns. 
+This is an example of SQL code that shows how a sample metric called `num_events` is joined with versioned dimensions data (stored in a table called `scd_dimensions`) using a primary key made up of the `entity_key` and `timestamp` columns. 
 
 
 ```sql
@@ -289,7 +305,7 @@ semantic_models:
 
     entities:
       - name: sales_person
-        type: natural 
+        type: primary 
         expr: sales_person_id
 ```
 
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 9f04cab1b82..3b377494565 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -31,7 +31,7 @@ metrics:
     configs: here for `enabled`           ## Optional
     label: The display name for your metric. This value will be shown in downstream tools. ## Required
     filter: |                             ## Optional            
-      {{  dimension('name') }} > 0 and {{ dimension(' another name') }} is not
+      {{  Dimension('entity__name') }} > 0 and {{ Dimension(' entity__another name') }} is not
       null
 ```
 
@@ -75,7 +75,7 @@ metrics:
       - name: gross_sales # these are all metrics (can be a derived metric, meaning building a derived metric with derived metrics)
       - name: cogs
       - name: users
-        filter: is_active # Optional additional constraint
+        filter: {{ Dimension('is_active')}} # Optional additional constraint
         alias: active_users # Optional alias to use in the expr
 ```
 <!-- not supported
@@ -113,7 +113,7 @@ metrics:
       numerator: cancellations_usd
       denominator: transaction_amount_usd
       filter: |     # add optional constraint string. This applies to both the numerator and denominator
-        {{ dimension('country', entity_path=['customer']) }} = 'MX'
+        {{ Dimension('customer__country') }} = 'MX'
   - name: enterprise_cancellation_rate
     owners:
       - support@getdbt.com
@@ -123,10 +123,10 @@ metrics:
     type_params:
       numerator:
         name: cancellations_usd
-        filter: tier = 'enterprise'  # constraint only applies to the numerator
+        filter: {{ Dimension('company__tier' )}} = 'enterprise'  # constraint only applies to the numerator
       denominator: transaction_amount_usd
       filter: |   #  add optional constraint string. This applies to both the numerator and denominator
-        {{ dimension('country', entity_path=['customer']) }} = 'MX'  
+        {{ Dimension('customer__country') }} = 'MX'  
 ```
 ### Simple metrics
 
@@ -144,7 +144,7 @@ metrics:
     type_params:
       measure: cancellations_usd  # Specify the measure you are creating a proxy for. 
       filter: |
-        {{dimension('value')}} > 100 and {{dimension('acquisition', entity_path=['user'])}}
+        {{ Dimension('order__value')}} > 100 and {{Dimension('user__acquisition')}}
 ```
 
 ### Further configuration 
diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index 95ce22db5b6..e206671d423 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -102,7 +102,7 @@ metrics:
     type_params:
       numerator:
         name: distinct_purchasers
-        filter: {{dimension('is_frequent_purchaser')}}
+        filter: {{Dimension('customer__is_frequent_purchaser')}}
         alias: frequent_purchasers
       denominator:
         name: distinct_purchasers
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index 9b74b5b4813..0919b5bee6e 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -21,9 +21,11 @@ Semantic models have 6 components and this page explains the definitions with so
 | [Name](#name) | Unique name for the semantic model | Required |
 | [Description](#description) | Includes important details in the description | Optional |
 | [Model](#model) | Specifies the dbt model for the semantic model using the `ref` function | Required |
+| [Defaults](#defaults) | The defaults for the model, currently only `agg_time_dimension` is supported.  | Required |
 | [Entities](#entities) | Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter | Required |
-| [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time-based` or `categorical` | Required |
+| [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time` or `categorical` | Required |
 | [Measures](#measures) | Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics | Optional |
+| [Primary Entity](#primary) | If the semantic model has no primary entity, then this property is required. | Optional |
 
 ## Semantic models components
 
@@ -33,7 +35,7 @@ semantic_models:
   - name: the_name_of_the_semantic_model      ## Required
     description: same as always               ## Optional
     model: ref('some_model')                  ## Required
-    default:                                  ## Required
+    defaults:                                 ## Required
         agg_time_dimension: dimension_name    ## Required if the model contains dimensions
     entities:                                 ## Required
        - see more information in entities
@@ -41,6 +43,7 @@ semantic_models:
        - see more information in measures section
     dimensions:                               ## Required
        - see more information in dimensions section
+    primary_entity: if the semantic model has no primary entity, then this property is required. #Optional if a primary entity exists, otherwise Required
 ```
 
 The following example displays a complete configuration and detailed descriptions of each field:
@@ -113,10 +116,15 @@ Includes important details in the description of the semantic model. This descri
 
 Specify the dbt model for the semantic model using the [`ref` function](/reference/dbt-jinja-functions/ref).
 
+### Defaults
+Defaults for the semantic model. Currently only `agg_time_dimension`. `agg_time_dimension` represents the default time dimensions for measures. This can be overriden by adding the `agg_time_dimension` key directly to a measure - see [Dimensions](/docs/build/dimensions) for examples. 
 ### Entities 
 
 To specify the [entities](/docs/build/entities) in your model, use their columns as join keys and indicate their `type` as primary, foreign, or unique keys with the type parameter.
 
+### Primary Entity
+If your data source does not have a primary entity, you need to manually specify one. Metricflow requers that all dimensions be tied to an entity.
+
 <Tabs>
 
 <TabItem value="entitytypes" value="Entity types">
diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md
index 29f70bb89e7..efc0c24ffbd 100644
--- a/website/docs/docs/build/simple.md
+++ b/website/docs/docs/build/simple.md
@@ -31,20 +31,19 @@ If you've already defined the measure using the `create_metric: true` parameter,
 ## Simple metrics example
 
 ```yaml
-``` yaml
-metrics: 
-  - name: customers
-    description: Count of customers
-    type: simple # Pointers to a measure you created in a semantic model
-    label: Count of customers
-    type_params:
-      measure: customers # The measure you're creating a proxy of.
-  - name: large_orders
-    description: "Order with order values over 20."
-    type: SIMPLE
-    label: Large Orders
-    type_params:
-      measure: orders
-    filter: | # For any metric you can optionally include a filter on dimension values
-      {{dimension('order_total_dim')}} >= 20
+  metrics: 
+    - name: customers
+      description: Count of customers
+      type: simple # Pointers to a measure you created in a semantic model
+      label: Count of customers
+      type_params:
+        measure: customers # The measure youre creating a proxy of.
+    - name: large_orders
+      description: "Order with order values over 20."
+      type: SIMPLE
+      label: Large Orders
+      type_params:
+        measure: orders
+      filter: | # For any metric you can optionally include a filter on dimension values
+        {{Dimension('customer__order_total_dim')}} >= 20
 ```
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index da7d2691210..88d55d44e18 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -1,175 +1,103 @@
 ---
 title: "Legacy dbt Semantic Layer migration guide"
-id: sl-migration
 sidebar_label: "Legacy dbt Semantic Layer migration"
-description: "This migration guide will help you migrate from the legacy dbt Semantic Layer to the new one."
+description: "Learn how to migrate from the legacy dbt Semantic Layer to the latest one."
 tags: [Semantic Layer]
 ---
 
-The dbt Semantic Layer has undergone a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. The new dbt Semantic Layer is in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and supported on dbt v1.6 or higher. It's available to all dbt Cloud plans (Developer, Team, and Enterprise) and introduces new semantic components, improved governance, enhanced efficiency, data accessibility, and new Semantic Layer APIs.
+The legacy Semantic Layer will be deprecated in H2 2023. Additionally, the `dbt_metrics` package will not be supported in dbt v1.6 and later. If you are using `dbt_metrics`, you'll need to upgrade your configurations before upgrading to v1.6. This guide is for people who have the legacy dbt Semantic Layer setup and would like to migrate to the new dbt Semantic Layer. The estimated migration time is two weeks. 
 
-This guide is for users who have the legacy dbt Semantic Layer set up and would like to migrate to the new and revamped  [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). The legacy dbt Semantic Layer is supported on dbt v1.5 or lower, uses the dbt_metrics package, and will be deprecated. 
 
-**Note**: Migrating to the new Semantic Layer will require downtime and this guide will explain how to minimize it.
+## Step 1: Migrate metric configs to the new spec
 
-## Migration components
+The metrics specification in dbt core is changed in v1.6 to support the integration of MetricFlow. It's strongly recommended that you refer to [Build your metrics](/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer. 
 
-In order to migrate from the legacy Semantic Layer to the new one, there are three migration components you'll need to complete: 
+dbt Labs recommends completing these steps in a local dev environment instead of the IDE: 
 
-- Create semantic models and migrate metric definitions
-- Setup the new dbt Semantic Layer in dbt Cloud
-- Update any applicable downstream integrations
+1. Create new Semantic Model configs as YAML files in your dbt project. *
+1. Upgrade the metrics configs in your project to the new spec. * 
+1. Delete your old metrics file or remove the `.yml` file extension so they're ignored at parse time. Remove the `dbt-metrics` package from your project. Remove any macros that reference `dbt-metrics`, like `metrics.calculate()`. Make sure that any packages you’re using don't have references to the old metrics spec. 
+1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]`. For example: 
 
-This guide assumes you have an environment with the legacy Semantic Layer enabled. There are two migration options for you to choose from, **both of which involve some downtime**:
+    ```bash
+    pip install "dbt-metricflow[snowflake]
+    ```
+    The MetricFlow CLI is not available in the IDE at this time. Support for it is coming soon. 
 
-1. [Migrating an existing environment](#migrate-an-existing-environment) over to the new dbt Semantic Layer
-2. [Creating and configure a brand new environment](#creating-a-new-environment-running-the-new-semantic-layer) for the new dbt Semantic Layer
+1. Run `dbt parse`. This parses your project and creates a `semantic_manifest.json` file in your target directory. MetricFlow needs this file to query metrics. If you make changes to your configs, you will need to parse your project again. 
+1. Run `mf list metrics` to view the metrics in your project.
+1. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`. For example:
+    ```bash
+    mf query --metrics revenue --group-by metric_time
+    ```
+1. Run `mf validate-configs` to run semantic and warehouse validations. This ensures your configs are valid and the underlying objects exist in your warehouse. 
+1. Push these changes to a new branch in your repo. 
 
+**To make this process easier, dbt Labs provides a [custom migration tool](https://github.com/dbt-labs/dbt-converter) that automates these steps for you. You can find installation instructions in the [README](https://github.com/dbt-labs/dbt-converter/blob/master/README.md). Derived metrics aren’t supported in the migration tool, and will have to be migrated manually.*
 
-## Migrate an existing environment
+## Step 2: Audit metric values after the migration
 
-Switching to th new Semantic Layer requires a complete transition. This is because the legacy Semantic Layer will no longer work once you update your metrics to the new specifications in dbt v1.6 or higher. 
+You might need to audit metric values during the migration to ensure that the historical values of key business metrics are the same.
 
-To avoid any disruptions or downtime, you _must_ set up the new Semantic Layer in your deployment environment after upgrading to the new metrics spec.
+1. In the CLI, query the metric(s) and dimensions you want to test and include the `--explain` option. For example:
+    ```bash 
+    mf query --metrics orders,revenue --group-by metric_time__month,customer_type --explain
+    ``` 
+1. Use SQL MetricFlow to create a temporary model in your project, like `tmp_orders_revenue audit.sql`. You will use this temporary model to compare against your legacy metrics.
+1. If you haven’t already done so, create a model using `metrics.calculate()` for the metrics you want to compare against. For example: 
 
-### Step 1: Migrate metrics
+    ```bash
+    select * 
+    from {{ metrics.calculate(  
+    [metric('orders)',
+    metric('revenue)'],
+        grain='week',
+        dimensions=['metric_time', 'customer_type'],
+    ) }}
+    ```
 
-Starting from dbt v1.6, the metrics specification dbt Core has been updated to support the integration of MetricFlow. This introduces two new components that are essential for building a semantic graph:
+1. Run the [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
 
-- [Semantic Models](/docs/build/semantic-models)  
-- [Metrics](/docs/build/metrics-overview)
+### Step 3: **Setup the Semantic Layer in a new environment**
 
-To migrate to the new specification, you'll need to:
+This step is only relevant to users who want the legacy and new semantic layer to run in parallel for a short time. This will let you recreate content in downstream tools like Hex and Mode with minimal downtime. If you do not need to recreate assets in these tools skip to step 5.
 
-- Create net new semantic model configs using YAML files in your dbt project
-- Upgrade your existing metrics configs to the new spec
+1. Create a new deployment environment in dbt cloud and set the dbt version to 1.6.
+2. Choose `Only run on a custom branch` and point to the branch that has the updated metric definition
+3. Set the deployment schema to a temporary migration schema i.e `tmp_sl_migration`. Optional, you can create a new database for the migration. 
+4. Create a job to parse your project i.e `dbt parse` and run it. Make sure this job succeeds,  There needs to be a succesful job in your environment in order to setup the semantic layer
+5. In Account Settings > Projects > Project details click `Configure the Semantic Layer`. Under  **********************Environment********************** select the deployment environment you created in the previous step. Save your configuration.
+6. In the Project details page, click `Generate service token` and grant it `Semantic Layer Only` and `Metadata Only` permissions. Save this token securely - you will need it to connect to the semantic layer. 
 
-To help ensure a smooth transition, use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
+At this point, both the new semantic layer and the old semantic layer will be running. The new semantic layer will be pointing at your migration branch with the updated metrics definitions. 
 
-**Note**, this doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
+## Step 4: Update connection in downstream integrations
 
-For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
+Now that your Semantic Layer is set up, you will need to update any downstream integrations that used the legacy Semantic Layer. 
 
-:::info
-If you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
-:::
+**Migration guide for Hex**
 
-#### Test your metrics
+1. Setup a new connection for the semantic layer for your account . Note your old connection will still work. This Loom walks through setting up your SL in Hex:
+https://www.loom.com/share/118ca1a28e264cadaa531cc2f3c0af81
 
-Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools. Note, this is only available to users developing metrics locally:
+2. Re-create the dashboards/reports that we’re using the legacy semantic layer. 
 
-1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
-2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
-3. Run `mf list metrics` to view the metrics in your project.
-4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
-5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
 
-If you migrate using the dbt Cloud IDE, we will perform semantic validations every time we parse or compile your project. This will ensure correct configuration of your metrics and semantic models. You will need to test querying metrics values in downstream tools. 
+NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently this connection will be pointing at the semantic layer migration environment
 
-Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the change to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. To minimize downtime, we recommend you promptly complete step 2 and set up your environment on the new Semantic Layer.
+### Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
 
-### Step 2: Switch to the new Semantic Layer
+1. Upgrade your production environment to 1.6. Note the old metrics definitions are no longer valid so your dbt jobs will not pass. 
+2. Merge your updated metrics definitions to main. **************************************************************************************************************************At this point the legacy semantic layer will no longer work.************************************************************************************************************************** 
 
-Now that you've migrated your metrics and created semantic models, it's time to set up your environment using the new Semantic Layer configuration. 
+If you created a new environment in Step 3:
 
-To upgrade your dbt Cloud environment to use the new Semantic Layer:
+1. Update your Environment in Account Settings > Project Details > Edit Semantic Layer Configuration to point to your production environment
+2. Delete your migration environment. Be sure to update your connections details in any down stream tools to account for the environment change.
 
-1. [Upgrade](/docs/dbt-versions/upgrade-core-in-cloud) the existing deployment environment for your project to v1.6 or higher.
-Ensure that all jobs run successfully with the new version.
-2. Run a dbt job in your deployment environment to set up the new Semantic Layer. You can use any dbt command, as long as it completes successfully. 
-3. To activate the new Semantic Layer, go to the **Account Settings** page and select the specific project you want to enable the Semantic Layer for.
-3. On the **Project Details** page, select **Configure Semantic Layer** 
-4. Enter the connection details for the data platform and select the environment where you want to enable the Semantic Layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
-5. After you’ve entered your credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. You can use this to connect to the Semantic Layer JDBC interface, which you will use in the next step. This interface allows you to connect and query your metrics in downstream tools.
-6. Go back to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
-7. You’re done 🎉! The new dbt Semantic Layer is now enabled for your project an you can start querying metrics.
+## Related docs 
 
-### Step 3: Update connection in downstream integrations
-
-Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
-
-<!---
-@mirnawong1 commenting out until these are available
-
- **Link to migration guide for Hex**
-
-
-**Link to migration guide for Mode**
------>
-
-## Create environment for the new Semantic Layer
-
-This approach allows you to run the legacy Semantic Layer in your existing environment, while you stage the metrics spec changes to your dbt project. 
-
-In this approach, you’ll set up a brand new deployment environment on dbt v1.6 or higher, which is a clone of your current Semantic Layer environment. When you’re ready to merge your changes, the new environment will be prepared, allowing you to safely deprecate the old Semantic Layer environment. Although some downtime may still be involved, this approach helps mitigate it.
-
-### Step 1: Setup a new environment
-
-You'll need to create a new environment for the new Semantic Layer and migrate your existing jobs to this environment:
-
-1. In dbt Cloud, select **Deploy** and then **Environments** to create a new [deployment environment](/docs/deploy/deploy-environments).
-2. Select **dbt Version 1.6** (or the latest) and enter your deployment credentials.
-    * There needs to be a successful run in your new environment to configure the  Semantic Layer. We recommend running `dbt ls`, since `dbt build` won’t succeed until you’ve updated your metrics configs.
-3. You'll need to migrate your jobs from your old deployment environment. You can do this by manually creating and configuring the new jobs in the new environment.
-4. To activate the new Semantic Layer, go to the Account Settings page and select the specific project you want to enable the Semantic Layer for.
-5. On the **Project Details** page, select **Configure Semantic Layer**.
-6. Enter the connection details for the data platform and select the environment where you want to enable the Semantic Layer. It's recommended to use a set of credentials with limited privileges, granting SELECT and CREATE TABLE permissions.
-7. After you’ve entered you’re credentials, you should see a **JDBC URL** connection string. Copy this string and save it in your password manager. This can be used to connect to the Semantic Layer JDBC.
-7. Go back to **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. Save this token for later.
-8. You’re done ✨ ! The Semantic Layer should now be enabled for your project! 
-
-The legacy Semantic Layer will still be running in your old environment so make sure you complete step two to migrate your metrics configs to the new spec. Once you’ve migrated your metrics configs, you can safely deprecate the old environment.
-
-### Step 2: Migrate metric configs to the new spec
-
-Starting from dbt v1.6, the metrics specification dbt Core has been updated to support the integration of MetricFlow. This introduces two new components that are essential for building a semantic graph:
-
-- [Semantic Models](/docs/build/semantic-models)  
-- [Metrics](/docs/build/metrics-overview)
-
-To migrate to the new specification, you'll need to:
-- Create net new semantic model configs using YAML files in your dbt project
-- Upgrade your existing metrics configs to the new spec
-
-To help ensure a smooth transition, you can use our [custom-built migration tool](https://github.com/dbt-labs/dbt-converter/blob/master/README.md) that automates this process. 
-**Note:** this tool doesn't support [derived metrics](/docs/build/derived) and you'll need to migrate them over manually. 
-
-For detailed instructions on how to configure semantic models and metrics, refer to our [getting started guide](/docs/use-dbt-semantic-layer/quickstart-sl). 
-
-
-If you're developing in metrics and semantic models in the dbt Cloud IDE, make sure to update your development environment to dbt version 1.6 or newer. This update will affect other analysts on your team who use the IDE for the same project. If they have metrics defined using dbt version 1.5 or older, it might cause errors.
-
-#### Test your metrics
-
-Once you’ve created your semantic models and created or updated your metrics, you can test your updated configs using the MetricFlow CLI tools:
-
-1. Install the CLI via `pip install "dbt-metricflow[your_adapter_name]`.
-2. Run `dbt parse`. This will parse your project and create a `semantic_manifest.json` file in your target directory.
-3. Run `mf list metrics` to view the metrics in your project.
-4. Run `mf validate-configs` to run semantic and warehouse validations. This ensures are configs are valid, and the underlying objects exist in your warehouse. 
-5. Test querying a metric by running `mf query --metrics <metric_name> --group-by <dimensions_name>`
-
-If you migrate using the dbt Cloud IDE, we will perform semantic validations every time we parse or compile your project. This will ensure correct configuration of your metrics and semantic models. You will need to test querying metrics values in downstream tools. 
-
-Once you've thoroughly tested and verified the accuracy of your metrics, commit and push the changes to your dbt repository. After you merge the new configurations, **the legacy Semantic Layer will no longer work**. 
-
-### Step 3: Update connection in downstream integrations
-
-Now that your Semantic Layer is set up, you will need to update any downstream integrations that relied on the legacy Semantic Layer. To set up a new connection or update an existing connection, you should do so in the downstream tool and not in dbt Cloud.
-
-<!---
-@mirnawong1 commenting out until these are available
-
- **Link to migration guide for Hex**
-
-**Link to migration guide for Mode**
------>
-
-
-## Resources
-
-- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
+- [MetricFlow quickstart guide](/docs/build/sl-getting-started)
 - [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
-- [Set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl)
-- [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
+- [Why we're deprecating the dbt_metrics package](/blog/deprecating-dbt-metrics) blog post
\ No newline at end of file

From abc48a3bd8f81ded218c68681824ed0d48cf46a5 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:49:12 -0400
Subject: [PATCH 477/703] remove the post hook code for Databricks

---
 .../2023-08-01-announcing-materialized-views.md |   2 +-
 .../materialized-incremental-twins.jpg          | Bin 0 -> 64580 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 website/static/img/blog/2023-08-01-announcing-materialized-views/materialized-incremental-twins.jpg

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 8ba4a037a0c..d2f3fcc441b 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -144,12 +144,12 @@ For Databricks:
 {{
 config(
 materialized='materialized_view',
-post_hook = 'alter materialized view {{this}} add SCHEDULE CRON "0 0 0 * * ? *" AT TIME ZONE "America/Los_Angeles";'
 )
 }}
 ```
 
 By default, materialized views are not refreshed on a schedule on Databricks in this materialization. To set up scheduling, you can use a post-hook to alter the MV with a cron schedule that will run in Databricks Workflows.
+```
 
 For Snowflake:
 
diff --git a/website/static/img/blog/2023-08-01-announcing-materialized-views/materialized-incremental-twins.jpg b/website/static/img/blog/2023-08-01-announcing-materialized-views/materialized-incremental-twins.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bd72dba36635a1a9d0cf7fe86f950fa438ba2c0b
GIT binary patch
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From 62522b65660b978ee7631e0d4011834bdd39d985 Mon Sep 17 00:00:00 2001
From: Amy Chen <amy@fishtownanalytics.com>
Date: Wed, 2 Aug 2023 17:56:27 -0400
Subject: [PATCH 478/703] I promise this is the last non-reviewed commit but
 adding back suggestion and fixing my sql

---
 website/blog/2023-08-01-announcing-materialized-views.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index d2f3fcc441b..4c93f52cbde 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -148,7 +148,10 @@ materialized='materialized_view',
 }}
 ```
 
-By default, materialized views are not refreshed on a schedule on Databricks in this materialization. To set up scheduling, you can use a post-hook to alter the MV with a cron schedule that will run in Databricks Workflows.
+By default, materialized views are not refreshed on a schedule on Databricks in this materialization. To set up scheduling, you can use a post-hook to alter the MV with a cron schedule that will run in Databricks Workflows. That could look like something like this
+
+```sql
+post_hook = 'ALTER MATERIALIZED VIEW {{this}} ADD SCHEDULE CRON "0 0 0 * * ? *" AT TIME ZONE "America/Los_Angeles";'
 ```
 
 For Snowflake:

From e2abd81e90ef073a8597c4492227ba6952f3ec9a Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Wed, 2 Aug 2023 16:52:14 -0700
Subject: [PATCH 479/703] moving release notes to august date

---
 .../{06-July-2023 => 05-Aug-2023}/sl-revamp-beta.md         | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)
 rename website/docs/docs/dbt-versions/release-notes/{06-July-2023 => 05-Aug-2023}/sl-revamp-beta.md (96%)

diff --git a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
similarity index 96%
rename from website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
rename to website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
index a3123237074..57d4218fb90 100644
--- a/website/docs/docs/dbt-versions/release-notes/06-July-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
@@ -1,9 +1,9 @@
 ---
 title: "Enhancement: Revamped dbt Semantic Layer available in public beta"
-description: "July 2023: The revamped dbt Semantic Layer, now available in public beta, introduces new semantic components and evolves the semantic layer's capability."
+description: "August 2023: The revamped dbt Semantic Layer, now available in public beta, introduces new semantic components and evolves the semantic layer's capability."
 sidebar_label: "Enhancement: Revamped dbt Semantic Layer in public beta"
-tags: [July-2023, dbt Semantic Layer]
-date: 2023-07-31
+tags: [Aug-2023, dbt Semantic Layer]
+date: 2023-08-03
 sidebar_position: 9
 ---
 

From 5868b80e6f02af9a2d45312f4765531dd143618f Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Wed, 2 Aug 2023 18:08:51 -0700
Subject: [PATCH 480/703] fixign bad links

---
 website/snippets/_new-sl-changes.md | 2 +-
 website/snippets/_sl-plan-info.md   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_new-sl-changes.md b/website/snippets/_new-sl-changes.md
index 6773dbdabf5..fa7c7abf743 100644
--- a/website/snippets/_new-sl-changes.md
+++ b/website/snippets/_new-sl-changes.md
@@ -3,6 +3,6 @@
 
 The dbt Semantic Layer has been re-released with [significant improvements](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics. 
 
-The new version is available in [public beta](/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta) and introduces [MetricFlow](/docs/build/about-metricflow), an essential component. It also includes new semantic elements, better governance, improved efficiency, easier data access, and new Semantic Layer API.
+The new version is available in [public beta](/docs/dbt-versions/release-notes/Aug-2023/sl-revamp-beta#public-beta) and introduces [MetricFlow](/docs/build/about-metricflow), an essential component. It also includes new semantic elements, better governance, improved efficiency, easier data access, and new Semantic Layer API.
 
 :::
diff --git a/website/snippets/_sl-plan-info.md b/website/snippets/_sl-plan-info.md
index f1a721c90fc..5294dab39d8 100644
--- a/website/snippets/_sl-plan-info.md
+++ b/website/snippets/_sl-plan-info.md
@@ -1 +1 @@
-<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/July-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, you can access {props.product} on <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span><br /><br />
+<span>During <a href="https://docs.getdbt.com/docs/dbt-versions/release-notes/Aug-2023/sl-revamp-beta#public-beta" target="_self">{props.cycle}</a>, you can access {props.product} on <a href="https://www.getdbt.com/pricing/">{props.plan}</a>  multi-tenant plans  <a href="https://docs.getdbt.com/docs/cloud/about-cloud/regions-ip-addresses" target="_self">{props.instance}</a> (cloud.getdbt.com login). It's available on dbt v1.6 or higher. dbt Cloud Developer plans and dbt Core users can use MetricFlow to define and test metrics locally, but can't dynamically query them with integrated tools.</span><br /><br />

From e1f98b1328fb2ced16263a82f2af82e13f5175d9 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Wed, 2 Aug 2023 19:14:32 -0600
Subject: [PATCH 481/703] add mode docs and filter docs

---
 website/docs/docs/build/metrics-overview.md   | 12 +++++++++++-
 website/docs/guides/migration/sl-migration.md |  3 +++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 3b377494565..5607fcbab99 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -15,7 +15,7 @@ The keys for metrics definitions are:
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
 | `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Optional |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
-| `filter` | For any type of metric, you may optionally include a filter string, which applies a dimensional filter when computing the metric. You can think of this as your WHERE clause.   | Optional |
+| `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |
 
 
@@ -147,6 +147,16 @@ metrics:
         {{ Dimension('order__value')}} > 100 and {{Dimension('user__acquisition')}}
 ```
 
+## Filters
+Filter are configured using jinja templating. Use the following syntax to refrence entites, dimensions and time dimensions in filters:
+```yaml
+filter: |
+  {{ Entity('entity_name') }} 
+filter: |
+  {{ Dimension('primary_entity__dimension_name') }}
+filter: |
+  {{ TimeDimension('time_dimension', 'granularity') }}
+```
 ### Further configuration 
 
 You can set more metadata for your metrics, which can be used by other tools later on. The way this metadata is used will vary based on the specific integration partner
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 88d55d44e18..d76f6d26ade 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -84,7 +84,10 @@ https://www.loom.com/share/118ca1a28e264cadaa531cc2f3c0af81
 
 
 NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently this connection will be pointing at the semantic layer migration environment
+**Migration guide for Mode**
+1. Setup a new connection for the semantic layer for your account. Follow [Mode's docs to setup your connect](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 
+2. Re-create the dashboards/reports that we’re using the legacy semantic layer.
 ### Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
 
 1. Upgrade your production environment to 1.6. Note the old metrics definitions are no longer valid so your dbt jobs will not pass. 

From a061f3ce234d9ac01b53f7a08b411683c22c4137 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Wed, 2 Aug 2023 19:33:34 -0600
Subject: [PATCH 482/703] add primary entity example

---
 website/docs/docs/build/semantic-models.md | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index 0919b5bee6e..a5d7ef29f7e 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -123,7 +123,21 @@ Defaults for the semantic model. Currently only `agg_time_dimension`. `agg_time_
 To specify the [entities](/docs/build/entities) in your model, use their columns as join keys and indicate their `type` as primary, foreign, or unique keys with the type parameter.
 
 ### Primary Entity
-If your data source does not have a primary entity, you need to manually specify one. Metricflow requers that all dimensions be tied to an entity.
+If your data source does not have a primary entity, you need to manually specify one. Metricflow requers that all dimensions be tied to an entity. You can define a primary entity using the following configs:
+
+```yaml:
+semantic_model:
+  name: bookings_monthly_source
+  description: bookings_monthly_source
+  defaults:
+    agg_time_dimension: ds
+  model: ref('bookings_monthly_source')
+  measures:
+    - name: bookings_monthly
+      agg: sum
+      create_metric: true
+  primary_entity: booking_id
+```
 
 <Tabs>
 
@@ -167,7 +181,7 @@ You can refer to entities (join keys) in a semantic model using the `name` param
 
 MetricFlow simplifies this by allowing you to query all metric groups and construct the join during the query. To specify dimensions parameters, include the `name` (either a column or SQL expression) and `type` (`categorical` or `time`). Categorical groups represent qualitative values, while time groups represent dates of varying granularity.
 
-Dimensions are identified using the name parameter, just like identifiers. The naming of groups must be unique within a semantic model, but not across semantic models since MetricFlow, uses entities to determine the appropriate groups.
+Dimensions are identified using the name parameter, just like identifiers. The naming of groups must be unique within a semantic model, but not across semantic models since MetricFlow, uses entities to determine the appropriate groups. MetricFlow requires all dimensions be tied to a primary entity. 
 
 :::info For time groups
 

From 3157f94d1a08bd537daf0d8d1e467af0a65ad7d9 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Wed, 2 Aug 2023 18:47:18 -0700
Subject: [PATCH 483/703] Update sl-jdbc.md

Some updates to where clauses
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 23 +++++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index d383ae3f12c..496711bb5f9 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -138,7 +138,7 @@ To query metric values, here are the following parameters that are available:
 | `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=[revenue]` | Required    |
 | `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=[user__country, metric_time]`     | Optional   |
 | `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | ```group_by=[`Dimension('metric_time').``` <br/> ```grain('week\|day\|month\|quarter\|year')]``` | Optional     |
-| `where`     | A where clause that allows you to filter on dimensions   | `where="metric_time >= '2022-03-08'"`   | Optional   |
+| `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where="Dimension('customer__country') = 'US')"`   | Optional   |
 | `limit`   | Limit the data returned    | `limit=10` | Optional  |
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
 | `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |
@@ -213,7 +213,23 @@ select * from {{
 	}}
 ``` 
 
-### Query with a filter
+### Querying with Where Filters
+
+Where filters have three components: 
+
+-`TimeDimension()` is used for any time dimension and requires a granularity argument - `TimeDimension('metric_time', 'DAY')`
+-`Dimension()` - This is used for any categorical dimensions - `Dimension('customer__country')`
+-`Entity()` - used for entities like primary and foreign keys - `Entity('order_id')`
+
+Use the following example to query using a `where` filter:
+
+```bash
+select * from {{
+semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
+group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
+where="{{ TimeDimension('metric_time', 'DAY') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new') AND {{ Entity('order_id') }} = 10)
+}}
+```
 
 Use the following example to query using a `where` filter:
 
@@ -221,7 +237,7 @@ Use the following example to query using a `where` filter:
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')")
+where="{{ TimeDimension('metric_time', 'DAY') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new')")
 }}
 ``` 
 
@@ -245,7 +261,6 @@ Use the following example to query using a `explain` keyword:
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 		group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-		where="metric_time__month >= '2017-03-09' AND customer__customer_type in ('new')",
 		explain=True)
 		}}
 ```

From 0ddac354f5b4ddb3525076deecfd80add9dd06ca Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Wed, 2 Aug 2023 18:50:41 -0700
Subject: [PATCH 484/703] Update sl-jdbc.md

bullet point error
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 496711bb5f9..edcab2f93e3 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -217,9 +217,11 @@ select * from {{
 
 Where filters have three components: 
 
--`TimeDimension()` is used for any time dimension and requires a granularity argument - `TimeDimension('metric_time', 'DAY')`
--`Dimension()` - This is used for any categorical dimensions - `Dimension('customer__country')`
--`Entity()` - used for entities like primary and foreign keys - `Entity('order_id')`
+- `TimeDimension()` is used for any time dimension and requires a granularity argument - `TimeDimension('metric_time', 'DAY')`
+
+- `Dimension()` - This is used for any categorical dimensions - `Dimension('customer__country')`
+
+- `Entity()` - used for entities like primary and foreign keys - `Entity('order_id')`
 
 Use the following example to query using a `where` filter:
 

From 52b765595304d7ace01574c6eb907c711d7b188e Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Wed, 2 Aug 2023 19:16:32 -0700
Subject: [PATCH 485/703] Update sl-jdbc.md

fixing weird query things with month and day
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index edcab2f93e3..29960babd5c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -229,7 +229,7 @@ Use the following example to query using a `where` filter:
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-where="{{ TimeDimension('metric_time', 'DAY') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new') AND {{ Entity('order_id') }} = 10)
+where="{{ TimeDimension('metric_time', 'MONTH') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new') AND {{ Entity('order_id') }} = 10)
 }}
 ```
 
@@ -239,7 +239,7 @@ Use the following example to query using a `where` filter:
 select * from {{
 semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-where="{{ TimeDimension('metric_time', 'DAY') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new')")
+where="{{ TimeDimension('metric_time', 'MONTH') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new')")
 }}
 ``` 
 

From a6da20712637b03c7a372b94971d03b6f03610fd Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 3 Aug 2023 13:36:17 +0300
Subject: [PATCH 486/703] add hex/mode links and fix typos

---
 .../avail-sl-integrations.md                  | 15 ++++--------
 .../use-dbt-semantic-layer/quickstart-sl.md   |  4 +++-
 website/docs/guides/migration/sl-migration.md | 23 +++++++++++--------
 website/snippets/_new-sl-setup.md             |  4 ++--
 website/snippets/_sl-partner-links.md         | 17 ++++++++++++++
 5 files changed, 39 insertions(+), 24 deletions(-)
 create mode 100644 website/snippets/_sl-partner-links.md

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 8f955986e07..2c4c7d4bd90 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -18,20 +18,13 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 There are a number of data applications that seamlessly integrate with the dbt Semantic Layer, powered by MetricFlow, from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-<span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="dbt Semantic Layer diagram displays the variety of different integration tools it supports."/>
 
-The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more. Here are some of the integrations you can query from:
+<span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.<br /><br />
 
-- Hex &mdash;
-- Mode &mdash;
-- Google Sheets - Coming Soon!
-- Any tool that allows you to write SQL and meets one of the two criteria: 
-  * Supports a generic JDBC driver option (such as DataGrip) or 
-  * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
+import AvailIntegrations from '/snippets/_sl-partner-links.md';
 
-Refer to [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations) for information on the partner integrations, their documentation, and more. 
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="dbt Semantic Layer supports many different tool integrations."/>
+<AvailIntegrations/>
 
 
 ## Related docs
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index e4f9810b58d..a51e7a58c74 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -164,10 +164,12 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
 
-You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer.
+You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer. 
 
 1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to use the JDBC API and what tools you can query it with.
 
+    * **Note** To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) and create a Semantic Layer Only service token. 
+
 2. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index d76f6d26ade..e6bc7e14d14 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -14,8 +14,8 @@ The metrics specification in dbt core is changed in v1.6 to support the integrat
 
 dbt Labs recommends completing these steps in a local dev environment instead of the IDE: 
 
-1. Create new Semantic Model configs as YAML files in your dbt project. *
-1. Upgrade the metrics configs in your project to the new spec. * 
+1. Create new Semantic Model configs as YAML files in your dbt project.*
+1. Upgrade the metrics configs in your project to the new spec.* 
 1. Delete your old metrics file or remove the `.yml` file extension so they're ignored at parse time. Remove the `dbt-metrics` package from your project. Remove any macros that reference `dbt-metrics`, like `metrics.calculate()`. Make sure that any packages you’re using don't have references to the old metrics spec. 
 1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]`. For example: 
 
@@ -65,8 +65,8 @@ This step is only relevant to users who want the legacy and new semantic layer t
 1. Create a new deployment environment in dbt cloud and set the dbt version to 1.6.
 2. Choose `Only run on a custom branch` and point to the branch that has the updated metric definition
 3. Set the deployment schema to a temporary migration schema i.e `tmp_sl_migration`. Optional, you can create a new database for the migration. 
-4. Create a job to parse your project i.e `dbt parse` and run it. Make sure this job succeeds,  There needs to be a succesful job in your environment in order to setup the semantic layer
-5. In Account Settings > Projects > Project details click `Configure the Semantic Layer`. Under  **********************Environment********************** select the deployment environment you created in the previous step. Save your configuration.
+4. Create a job to parse your project i.e `dbt parse` and run it. Make sure this job succeeds,  There needs to be a successful job in your environment in order to setup the semantic layer
+5. In Account Settings > Projects > Project details click `Configure the Semantic Layer`. Under  **Environment**select the deployment environment you created in the previous step. Save your configuration.
 6. In the Project details page, click `Generate service token` and grant it `Semantic Layer Only` and `Metadata Only` permissions. Save this token securely - you will need it to connect to the semantic layer. 
 
 At this point, both the new semantic layer and the old semantic layer will be running. The new semantic layer will be pointing at your migration branch with the updated metrics definitions. 
@@ -77,23 +77,26 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 **Migration guide for Hex**
 
-1. Setup a new connection for the semantic layer for your account . Note your old connection will still work. This Loom walks through setting up your SL in Hex:
-https://www.loom.com/share/118ca1a28e264cadaa531cc2f3c0af81
+1. Setup a new connection for the semantic layer for your account . Note your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
 
-2. Re-create the dashboards/reports that we’re using the legacy semantic layer. 
+<LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
+
+2. Re-create the dashboards/reports that we’re using the legacy dbt Semantic Layer. 
 
 
 NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently this connection will be pointing at the semantic layer migration environment
+
 **Migration guide for Mode**
+
 1. Setup a new connection for the semantic layer for your account. Follow [Mode's docs to setup your connect](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 
 2. Re-create the dashboards/reports that we’re using the legacy semantic layer.
 ### Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
 
 1. Upgrade your production environment to 1.6. Note the old metrics definitions are no longer valid so your dbt jobs will not pass. 
-2. Merge your updated metrics definitions to main. **************************************************************************************************************************At this point the legacy semantic layer will no longer work.************************************************************************************************************************** 
+2. Merge your updated metrics definitions to main. **At this point the legacy semantic layer will no longer work.**
 
-If you created a new environment in Step 3:
+If you created a new environment in [Step 3](#step-3-setup-the-semantic-layer-in-a-new-environment):
 
 1. Update your Environment in Account Settings > Project Details > Edit Semantic Layer Configuration to point to your production environment
 2. Delete your migration environment. Be sure to update your connections details in any down stream tools to account for the environment change.
@@ -103,4 +106,4 @@ If you created a new environment in Step 3:
 - [MetricFlow quickstart guide](/docs/build/sl-getting-started)
 - [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
-- [Why we're deprecating the dbt_metrics package](/blog/deprecating-dbt-metrics) blog post
\ No newline at end of file
+- [Why we're deprecating the dbt_metrics package](/blog/deprecating-dbt-metrics) blog post
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 9b6ac963d62..0eeeb0a8222 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -25,9 +25,9 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 6. You should see connection information that allows you to connect to downstream tools. 
 **Note:** If your tool supports JDBC, make sure to save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 
-7. Return to the **Project Details** page and select **Generate Service Token** to create a Semantic Layer service token. 
+7. Return to the **Project Details** page, then  select **Generate Service Token** to create a [Semantic Layer Only service token](/docs/dbt-cloud-apis/service-tokens).  
 
-8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools.
+8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools. For more info on how to integrate with partner integrations, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
 Great job, you've configured the Semantic Layer 🎉! 
 
diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
new file mode 100644
index 00000000000..c190afc3209
--- /dev/null
+++ b/website/snippets/_sl-partner-links.md
@@ -0,0 +1,17 @@
+The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
+
+- **Mode** &mdash; To connect to Mode, refer to [Mode documentation](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
+
+- **Google Sheets** &mdash; Integration with Google Sheets coming soon
+
+- **Tools that allows you to write SQL** &mdash; They must meet one of the two criteria: 
+  * Supports a generic JDBC driver option (such as DataGrip) or 
+  * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
+
+- **Hex** &mdash; Public docs coming soon, refer to the following Loom video to connect with Hex:
+
+<LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
+
+**Note** before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only service token. 
+
+For more info on the available partner integrations, refer [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations).

From 2c5990ffbb1a29512058d2c476d26913e6580b95 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 3 Aug 2023 13:40:08 +0300
Subject: [PATCH 487/703] fix faq

---
 website/docs/docs/use-dbt-semantic-layer/sl-architecture.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index b03023b3ae2..f09066a50f0 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -36,7 +36,7 @@ The Semantic Layer includes these components:
     
 <details>
 <summary>How are you storing my data?</summary>
-The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.
+User data passes through the Semantic Layer on its way back from the warehouse. dbt Labs ensures security by authenticating through the customer's data warehouse. Currently, we don't cache data for the long term, but it might temporarily stay in the system for up to 10 minutes, usually less. In the future, we'll introduce a caching feature that allows us to cache data on our infrastructure for up to 24 hours.
 </details>
 <details>
 <summary>Is the dbt Semantic Layer open source?</summary>

From 5249e11f20202ad6a275679ee625d89018fc1351 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 3 Aug 2023 13:42:08 +0300
Subject: [PATCH 488/703] fix bullet

---
 website/snippets/_sl-partner-links.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index c190afc3209..c25a159aa3c 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -8,10 +8,10 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
   * Supports a generic JDBC driver option (such as DataGrip) or 
   * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
 
-- **Hex** &mdash; Public docs coming soon, refer to the following Loom video to connect with Hex:
+- **Hex** &mdash; Public docs coming soon, however, you can refer to the following Loom video to connect with Hex:
 
 <LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
 
-**Note** before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only service token. 
+    * **Note** before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only service token. 
 
 For more info on the available partner integrations, refer [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations).

From 42d32b60417566784181e235236be40c0193931d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 13:18:06 +0100
Subject: [PATCH 489/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 29960babd5c..89407020919 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -213,7 +213,7 @@ select * from {{
 	}}
 ``` 
 
-### Querying with Where Filters
+### Query with where filters
 
 Where filters have three components: 
 

From 5e090abba5c25b6dbe487ec73d0268bece8e11bc Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 10:12:17 -0400
Subject: [PATCH 490/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 4c93f52cbde..0f9abe8f7c9 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -62,7 +62,7 @@ config(
 ```
 
 :::note
-We are only supporting dynamic tables on Snowflake, not Snowflake’s materialized views (for a comparison between Snowflake Dynamic Tables and Materialized Views, refer docs). Dynamic tables are better suited for continuous transformations due to functionality like the ability to join, union, and aggregate on base tables, views , and other dynamic tables. Due to those features, they are also more aligned with what other data platforms are calling Materialized Views. For the sake of simplicity, when I refer to materialized views in this blog, I mean dynamic tables in Snowflake.
+We are only supporting dynamic tables on Snowflake, not Snowflake’s materialized views (for a comparison between Snowflake Dynamic Tables and Materialized Views, refer [docs](https://docs.snowflake.com/en/user-guide/dynamic-tables-comparison#dynamic-tables-compared-to-materialized-views). Dynamic tables are better suited for continuous transformations due to functionality like the ability to join, union, and aggregate on base tables, views , and other dynamic tables. Due to those features, they are also more aligned with what other data platforms are calling Materialized Views. For the sake of simplicity, when I refer to materialized views in this blog, I mean dynamic tables in Snowflake.
 :::
 
 Now that we support materialized views: how do you fit them into your dbt workflow? It’s easy to imagine a world of unregulated computation because you didn’t put in proper guardrails and now you have materialized views running rampant unbeknownst to you in your data platform.

From a9478edf55d31e66db70fa5fd5dcb3a102f3a692 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 15:45:49 +0100
Subject: [PATCH 491/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 89407020919..46a696e695e 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -51,7 +51,7 @@ jdbc:arrow-flight-sql://semantic-layer.cloud.getdbt.com:443?&environmentId=20233
 | `jdbc:arrow-flight-sql://` | The protocol for the JDBC driver.  | `jdbc:arrow-flight-sql://` |
 | `semantic-layer.cloud.getdbt.com` | The [access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your account's dbt Cloud region. You must always add the `semantic-layer` prefix before the access URL.  | For dbt Cloud deployment hosted in North America, use `semantic-layer.cloud.getdbt.com`  |
 | `environmentId` | The unique identifier for the dbt production environment, you can retrieve this from the dbt Cloud URL <br /> when you navigate to **Environments** under **Deploy**. | If your URL ends with `.../environments/222222`, your `environmentId` is `222222`<br /><br />   |
-| `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” permission. Create a new service token in your **Account Settings** page. | `token=SERVICE_TOKEN` |
+| `SERVICE_TOKEN` | dbt Cloud [service token](/docs/dbt-cloud-apis/service-tokens) with “Semantic Layer Only” and "Metadata Only" permissions. Create a new service token on the **Account Settings** page. | `token=SERVICE_TOKEN` |
 
 *Note &mdash; If you're testing locally on a tool like DataGrip, you may also have to provide the following variable at the end or beginning of the JDBC URL `&disableCertificateVerification=true`.
 

From 5fe107e33db52846856bd83b00eb27b579745b20 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 3 Aug 2023 18:14:45 +0300
Subject: [PATCH 492/703] remove avail integrations

---
 .../docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md    | 1 -
 website/snippets/_sl-partner-links.md                            | 1 -
 2 files changed, 2 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 2c4c7d4bd90..267942155c4 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -26,7 +26,6 @@ import AvailIntegrations from '/snippets/_sl-partner-links.md';
 
 <AvailIntegrations/>
 
-
 ## Related docs
 
 - <span><a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC to query your metrics in downstream tools.
diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index c25a159aa3c..32cbfe1b84b 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -14,4 +14,3 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
     * **Note** before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only service token. 
 
-For more info on the available partner integrations, refer [dbt Semantic Layer integrations](https://www.getdbt.com/product/semantic-layer-integrations).

From 2cc9ca3989c7b15e9421804030c8fc649fab59f6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 16:18:24 +0100
Subject: [PATCH 493/703] Update _sl-partner-links.md

---
 website/snippets/_sl-partner-links.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index 32cbfe1b84b..accfe3b9ba1 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -12,5 +12,5 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
 
 <LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
 
-    * **Note** before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only service token. 
+    * **Note**, before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only and Metadata Only service token. 
 

From fec0fab7d4a2f453f06b651a867c1df01acd88e7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 16:20:08 +0100
Subject: [PATCH 494/703] Update _new-sl-setup.md

---
 website/snippets/_new-sl-setup.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 0eeeb0a8222..c03b9d5df5c 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -23,9 +23,9 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="To set up your Semantic Layer configuration, enter your credentials and select the deployment environment. This example selects a deployment environment named 'Production.'"/>
 
 6. You should see connection information that allows you to connect to downstream tools. 
-**Note:** If your tool supports JDBC, make sure to save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
+**Note:** If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 
-7. Return to the **Project Details** page, then  select **Generate Service Token** to create a [Semantic Layer Only service token](/docs/dbt-cloud-apis/service-tokens).  
+7. Return to the **Project Details** page, then  select **Generate Service Token** to create a [Semantic Layer Only an Metadata Only service token](/docs/dbt-cloud-apis/service-tokens).  
 
 8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools. For more info on how to integrate with partner integrations, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 

From e8ae050274d61025da408fcc79913d8603ac67ca Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 16:22:22 +0100
Subject: [PATCH 495/703] Update quickstart-sl.md

---
 website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index a51e7a58c74..b3c10941a40 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -168,7 +168,7 @@ You can query your metrics in a JDBC-enabled tool or use existing first-class in
 
 1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to use the JDBC API and what tools you can query it with.
 
-    * **Note** To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) and create a Semantic Layer Only service token. 
+    * **Note** To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) with Semantic Layer Only and Metadata Only permissions. 
 
 2. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 

From ed55ea556e86ee53a6e09b35076f912417658a8c Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 3 Aug 2023 18:30:51 +0300
Subject: [PATCH 496/703] remove embed

---
 website/snippets/_sl-partner-links.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index accfe3b9ba1..fa07a032b03 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -1,5 +1,7 @@
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
 
+- **Hex** &mdash; Public docs coming soon, however, you can refer to a detailed [Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a), which explains how to connect in more detail. 
+
 - **Mode** &mdash; To connect to Mode, refer to [Mode documentation](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 
 - **Google Sheets** &mdash; Integration with Google Sheets coming soon
@@ -8,9 +10,5 @@ The dbt Semantic Layer integrations are capable of querying dbt metrics, importi
   * Supports a generic JDBC driver option (such as DataGrip) or 
   * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
 
-- **Hex** &mdash; Public docs coming soon, however, you can refer to the following Loom video to connect with Hex:
-
-<LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
-
-    * **Note**, before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only and Metadata Only service token. 
+**Note**, before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only and Metadata Only service token. 
 

From f5516ff1b59c1006846b30523254f0f4ae4b288d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 16:47:34 +0100
Subject: [PATCH 497/703] Update _sl-partner-links.md

---
 website/snippets/_sl-partner-links.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index fa07a032b03..bd91f00ccd8 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -1,6 +1,6 @@
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
 
-- **Hex** &mdash; Public docs coming soon, however, you can refer to a detailed [Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a), which explains how to connect in more detail. 
+- **Hex** &mdash; Public docs are coming soon, however, you can refer to this [Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info on how to integrate with Hex.
 
 - **Mode** &mdash; To connect to Mode, refer to [Mode documentation](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 

From fcd587106f81e8862f0bfca56eeb4e097e95999f Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 3 Aug 2023 08:53:45 -0700
Subject: [PATCH 498/703] IDE v1.2 release note (#3855)

## What are you changing in this pull request and why?

Creating a new branch due to base branch history. Previous was
https://github.com/dbt-labs/docs.getdbt.com/pull/3853

This PR adds the release note for IDE 1.2, including limitation and new
features.


## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

---------

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 .../release-notes/05-Aug-2023/ide-v1.2.md     | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md

diff --git a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md
new file mode 100644
index 00000000000..d2f86724b07
--- /dev/null
+++ b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md
@@ -0,0 +1,38 @@
+---
+title: "Update: Cloud IDE v1.2 with new service"
+description: "August 2023: Cloud IDE now uses dbt-server to provide more reliable service and dbt Core feature parity, including support for commands like `dbt list`."
+sidebar_label: "Update: Cloud IDE v1.2"
+tags: [Aug-2023, IDE]
+date: 2023-08-03
+sidebar_position: 8
+---
+
+We're excited to announce that we replaced the backend service that powers the Cloud IDE with a more reliable server -- dbt-server. This significant update follows the rebuild of the IDE frontend last year. We're committed to improving our IDE to provide you with a better experience.
+
+Previously, the Cloud IDE used dbt-rpc, an outdated service that was unable to stay up-to-date with changes from dbt-core. The dbt-rpc integration used legacy dbt-core entry points and logging systems, causing it to be sluggish, brittle, and poorly tested. The Core team had been working around this outdated technology to avoid breaking it, which prevented them from developing with velocity and confidence.
+
+## New features
+
+- **Better dbt-core parity:** The Cloud IDE has better command parity with dbt-core, including support for commands like `dbt list` and improved treatment of flags like `--vars`, `--fail-fast`, etc.
+- **Improved maintainability:** With the new dbt-server, it's easier to fix bugs and improve the overall quality of the product. With dbt-rpc, fixing bugs was a time-consuming and challenging process that required extensive testing. With the new service, we can identify and fix bugs more quickly, resulting in a more stable and reliable IDE.
+- **A more reliable service:** Simplified architecture that's less prone to failure.
+
+### Product refinements
+
+- Improved `Preview` capabilities with Core v1.6 + IDE v1.2. [This Loom](https://www.loom.com/share/12838feb77bf463c8585fc1fc6aa161b) provides more information.
+
+### Bug fixes
+
+- Global page can become "inert" and stop handling clicks
+- Switching back and forth between files in the git diff view can cause overwrite
+- Browser gets stuck during markdown preview for doc with large table
+- Editor right click menu is offset
+- Unable to Cancel on the Save New File component when Closing All Files in the IDE
+- Mouse flicker in the modal's file tree makes it difficult to select a folder where you want to save a new file  
+- Snapshots not showing in Lineage when inside a subfolder and is mixed cased named
+- Tooltips do not work for Format and Save
+- When a dbt invocation is in progress or if parsing is ongoing, attempting to switch branches will cause the `Git Branch` dropdown to close automatically
+
+### Known issues
+
+- `{{this}}` function does not display properly in preview/compile with dbt-server

From 4da48098c73f5bd3b12493fd7a1bb45597f370dc Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 17:23:46 +0100
Subject: [PATCH 499/703] Update quickstart-sl.md

fix typo
---
 website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index b3c10941a40..ddba7ec153a 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -189,7 +189,8 @@ User data passes through the Semantic Layer on its way back from the warehouse.
 <details>
 <summary>Is the dbt Semantic Layer open source?</summary>
 The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
-   
+<br></br> 
+
 ## Next steps
 
 Ready to define your own metrics and bring consistency to data consumers?  Review the following documents to get started:

From 4f6d48218bc062e2b7475a534c3893359b500e0b Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 17:28:23 +0100
Subject: [PATCH 500/703] Update quickstart-sl.md

fix header
---
 .../use-dbt-semantic-layer/quickstart-sl.md    | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index ddba7ec153a..20729d1879d 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -172,6 +172,15 @@ You can query your metrics in a JDBC-enabled tool or use existing first-class in
 
 2. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
+## Next steps
+
+Ready to define your own metrics and bring consistency to data consumers?  Review the following documents to get started:
+
+- [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
+- [Build your metrics](/docs/build/build-metrics-intro)
+- [About MetricFlow](/docs/build/about-metricflow)
+- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
+
 ## FAQs
 
 If you're encountering some issues when defining your metrics or setting up the dbt Semantic Layer, check out a list of answers to some of the questions or problems you may be experiencing.
@@ -191,15 +200,6 @@ User data passes through the Semantic Layer on its way back from the warehouse.
 The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 <br></br> 
 
-## Next steps
-
-Ready to define your own metrics and bring consistency to data consumers?  Review the following documents to get started:
-
-- [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
-- [Build your metrics](/docs/build/build-metrics-intro)
-- [About MetricFlow](/docs/build/about-metricflow)
-- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
-
 </VersionBlock>
 
 <VersionBlock lastVersion="1.5">

From 6bdee85804277107a599ffc50a0a4f4ed9d69dc1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 17:37:58 +0100
Subject: [PATCH 501/703] fix headers for integration guide

---
 website/docs/guides/migration/sl-migration.md | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index e6bc7e14d14..49977a5076e 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -58,14 +58,14 @@ You might need to audit metric values during the migration to ensure that the hi
 
 1. Run the [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
 
-### Step 3: **Setup the Semantic Layer in a new environment**
+## Step 3: **Setup the Semantic Layer in a new environment**
 
 This step is only relevant to users who want the legacy and new semantic layer to run in parallel for a short time. This will let you recreate content in downstream tools like Hex and Mode with minimal downtime. If you do not need to recreate assets in these tools skip to step 5.
 
-1. Create a new deployment environment in dbt cloud and set the dbt version to 1.6.
+1. Create a new deployment environment in dbt Cloud and set the dbt version to 1.6.
 2. Choose `Only run on a custom branch` and point to the branch that has the updated metric definition
 3. Set the deployment schema to a temporary migration schema i.e `tmp_sl_migration`. Optional, you can create a new database for the migration. 
-4. Create a job to parse your project i.e `dbt parse` and run it. Make sure this job succeeds,  There needs to be a successful job in your environment in order to setup the semantic layer
+4. Create a job to parse your project, such as `dbt parse`, and run it. Make sure this job succeeds,  There needs to be a successful job in your environment in order to set up the semantic layer
 5. In Account Settings > Projects > Project details click `Configure the Semantic Layer`. Under  **Environment**select the deployment environment you created in the previous step. Save your configuration.
 6. In the Project details page, click `Generate service token` and grant it `Semantic Layer Only` and `Metadata Only` permissions. Save this token securely - you will need it to connect to the semantic layer. 
 
@@ -77,21 +77,22 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 **Migration guide for Hex**
 
-1. Setup a new connection for the semantic layer for your account . Note your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
+1. Setup a new connection for the semantic layer for your account. Note your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
 
 <LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
 
-2. Re-create the dashboards/reports that we’re using the legacy dbt Semantic Layer. 
+2. Re-create the dashboards/reports that you’re using the legacy dbt Semantic Layer. 
 
 
-NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently this connection will be pointing at the semantic layer migration environment
+NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently, this connection will be pointing at the semantic layer migration environment
 
 **Migration guide for Mode**
 
 1. Setup a new connection for the semantic layer for your account. Follow [Mode's docs to setup your connect](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 
-2. Re-create the dashboards/reports that we’re using the legacy semantic layer.
-### Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
+2. Re-create the dashboards/reports that you’re using in the legacy semantic layer.
+   
+## Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
 
 1. Upgrade your production environment to 1.6. Note the old metrics definitions are no longer valid so your dbt jobs will not pass. 
 2. Merge your updated metrics definitions to main. **At this point the legacy semantic layer will no longer work.**
@@ -99,7 +100,7 @@ NOTE: You will need to update your connection to your production environment onc
 If you created a new environment in [Step 3](#step-3-setup-the-semantic-layer-in-a-new-environment):
 
 1. Update your Environment in Account Settings > Project Details > Edit Semantic Layer Configuration to point to your production environment
-2. Delete your migration environment. Be sure to update your connections details in any down stream tools to account for the environment change.
+2. Delete your migration environment. Be sure to update your connections details in any downstream tools to account for the environment change.
 
 ## Related docs 
 

From 3969ff359b4482a15307c483961e34c031d4b0f4 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 18:22:05 +0100
Subject: [PATCH 502/703] Update sl-revamp-beta.md

Update per public messaging
---
 .../release-notes/05-Aug-2023/sl-revamp-beta.md             | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
index 57d4218fb90..221c129b0d5 100644
--- a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
@@ -4,7 +4,7 @@ description: "August 2023: The revamped dbt Semantic Layer, now available in pub
 sidebar_label: "Enhancement: Revamped dbt Semantic Layer in public beta"
 tags: [Aug-2023, dbt Semantic Layer]
 date: 2023-08-03
-sidebar_position: 9
+sidebar_position: 7
 ---
 
 :::important
@@ -48,9 +48,9 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
   - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt MetricFlow CLI only.
 
-- **What** &mdash; Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer in October 2023. For now, there will be no billing for usage. If you have any questions on pricing please reach out to your account representative.
+- **What** &mdash; Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. If you have any questions on pricing please, reach out to your account representative.
 
-- **When** &mdash; Public beta starts on July 31, 2023 and will end once the dbt Semantic Layer is available for GA in October 2023.
+- **When** &mdash; Public beta starts on August 1st, 2023.
 
 - **Where** &mdash; You can experience the dbt Semantic Layer in dbt Cloud. Public beta is enabled at the account level so you don’t need to worry about enabling it per user.
 

From a33e8c4694a6fdfac7494e42df5b88981d144cd6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 3 Aug 2023 18:22:43 +0100
Subject: [PATCH 503/703] Update sl-revamp-beta.md

---
 .../dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
index 221c129b0d5..921ed6dcd79 100644
--- a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
+++ b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/sl-revamp-beta.md
@@ -48,7 +48,7 @@ The dbt Semantic Layer is currently available as a public beta, which means:
 
   - Developer plans or dbt Core users can use MetricFlow to define and test metrics using the dbt MetricFlow CLI only.
 
-- **What** &mdash; Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. If you have any questions on pricing please, reach out to your account representative.
+- **What** &mdash; Public beta provides early access to new features. The dbt Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to general availability later this year. We may also introduce new functionality that isn't backwards compatible. We provide support, and relevant service level objectives (SLOs) apply. If you have any questions on pricing, please reach out to your account representative.
 
 - **When** &mdash; Public beta starts on August 1st, 2023.
 

From e9b0349844a86b4e92b9ee6a0125ef8a26d20140 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 13:33:00 -0400
Subject: [PATCH 504/703] Update snowflake-configs.md

fix the confusion
---
 website/docs/reference/resource-configs/snowflake-configs.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/website/docs/reference/resource-configs/snowflake-configs.md b/website/docs/reference/resource-configs/snowflake-configs.md
index 09ebe11f4c7..f822773ee41 100644
--- a/website/docs/reference/resource-configs/snowflake-configs.md
+++ b/website/docs/reference/resource-configs/snowflake-configs.md
@@ -346,8 +346,6 @@ In the configuration format for the model SQL file:
 
 ## Dynamic Tables
 
-[Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) are Snowflake's version of Materialized Views.
-
 ### Parameters
 
 dbt-snowflake requires the following parameters:

From 5ab96171c67d4699ec11bd188f18e7ee8485a4f5 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 14:19:40 -0400
Subject: [PATCH 505/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 0f9abe8f7c9..4df1981ca5b 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -1,5 +1,5 @@
 ---
-title: "Introducing Materialized Views and how to use them"
+title: "Optimizing Materialized Views with dbt"
 description: "In dbt v1.6, we introduce support for materialized views. In this blog post, Amy will review how to use them in your workflow"
 slug: announcing-materialized-views
 

From 678c3fca09a6e863d884a7510847ef1f23de9ac6 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 14:19:55 -0400
Subject: [PATCH 506/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 4df1981ca5b..6140efc31f8 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -8,7 +8,7 @@ authors: [amy_chen]
 tags: [analytics craft, dbt product updates, data ecosystem]
 hide_table_of_contents: false
 
-date: 2023-08-01
+date: 2023-08-03
 is_featured: true
 ---
 

From e7a92cddba1d313160ddecc499985c8ab6fb088a Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Thu, 3 Aug 2023 14:36:16 -0400
Subject: [PATCH 507/703] Delete dbt-semantic-layer.md

---
 .../dbt-semantic-layer.md                     | 146 ------------------
 1 file changed, 146 deletions(-)
 delete mode 100644 website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md

diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md b/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
deleted file mode 100644
index 320bebad3bf..00000000000
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-semantic-layer.md
+++ /dev/null
@@ -1,146 +0,0 @@
----
-title: "dbt Semantic Layer"
-id: dbt-semantic-layer
-description: "Introducing the dbt Semantic Layer"
-sidebar_label: "dbt Semantic Layer"
----
-
-:::info Coming soon
-The dbt Semantic Layer is undergoing a [significant revamp](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/), making it more efficient to define and query metrics.
-
-**What’s changing?** The dbt_metrics package will be [deprecated](https://docs.getdbt.com/blog/deprecating-dbt-metrics) and replaced with [MetricFlow](/docs/build/about-metricflow?version=1.6), a new way framework for defining metrics in dbt.
-
-**What's new?**  Learn how to [Build your metrics](/docs/build/build-metrics-intro?version=1.6) using MetricFlow, one of the key components that makes up the revamped dbt Semantic Layer. It handles SQL query construction and defines the specification for dbt semantic models and metrics. 
-:::
-
-The dbt Semantic Layer allows data teams to centrally define essential business metrics like `revenue`, `customer`, and `churn` in the modeling layer (your dbt project) for consistent self-service within downstream data tools like BI and metadata management solutions. The dbt Semantic Layer provides the flexibility to define metrics on top of your existing models and then query those metrics and models in your analysis tools of choice.
-
-The result? You have less duplicate coding for data teams and more consistency for data consumers. 
-
-The dbt Semantic Layer has four main parts:
-
-- Define your metrics in version-controlled dbt project code using MetricFlow
-- Import your metric definitions via the [Discovery API](/docs/dbt-cloud-apis/discovery-api)
-- Query your metric data via the dbt Proxy Server
-- Explore and analyze dbt metrics in downstream tools
-
-
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl_architecture.png" title="dbt Semantic Layer architecture" />
-
-### What makes the dbt Semantic Layer different?
-
-The dbt Semantic Layer reduces code duplication and inconsistency regarding your business metrics. By moving metric definitions out of the BI layer and into the modeling layer, data teams can feel confident that different business units are working from the same metric definitions, regardless of their tool of choice. If a metric definition changes in dbt, it’s refreshed everywhere it’s invoked and creates consistency across all applications. You can also use the dbt Semantic Layer to query models and use macros.
-
-
-## Prerequisites
-To use the dbt Semantic Layer, you’ll need to meet the following:
-
-<Snippet path="sl-prerequisites" />
-
-<Snippet path="sl-considerations-banner" />
-
-## Public Preview
-
-The dbt Semantic Layer is currently available for Public Preview, which means:
-
-&mdash; **Who?** The dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise) during Public Preview. Review [Product architecture](/docs/use-dbt-semantic-layer/dbt-semantic-layer#product-architecture) for more info on plan availability.
-
-- Team and Enterprise accounts will be able to set up the Semantic Layer and [Discovery API](/docs/dbt-cloud-apis/discovery-api) in the integrated
-partner tool to import metric definition.
-- Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse dbt metrics in external tools, which requires access to the Discovery API. 
-
-&mdash; **What?** Public Previews provide early access to new features. The Semantic Layer is stable and you can use it for production deployments, but there may still be some planned additions and modifications to product behaviors before moving to General Availability. We may also introduce new functionality that is not backwards compatible. dbt Labs provides support, and relevant service level objectives (SLOs) apply. We will introduce pricing for the dbt Semantic Layer alongside the General Available (GA) release (future GA date to be announced).
-    
-&mdash; **When?** Public Preview will end once the dbt Semantic Layer is available for GA. After GA, the dbt Semantic Layer will only be available to dbt Cloud **Team** and **Enterprise** plans.
-
-&mdash; **Where?** Public Preview is enabled at the account level so you don’t need to worry about enabling it per user.
-
-## Product architecture 
-
-The dbt Semantic Layer product architecture includes four primary components:
-
-| Components | Information | Developer plans | Team plans | Enterprise plans | License |
-| --- | --- | :---: | :---: | :---: | --- |
-| **[dbt project](/docs/build/metrics)** | Define models and metrics in dbt Core. | ✅ | ✅ |  ✅  | Open source, Core |
-| **[dbt Server](https://github.com/dbt-labs/dbt-server)**| A persisted HTTP server that wraps dbt core to handle RESTful API requests for dbt operations. | ✅ | ✅ | ✅ | BSL |
-| **SQL Proxy** | Reverse-proxy that accepts dbt-SQL (SQL + Jinja like query models and metrics, use macros), compiles the query into pure SQL, and executes the query against the data platform. | ✅ <br></br>_* Available during Public Preview only_ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
-| **[Discovery API](/docs/dbt-cloud-apis/discovery-api)**  | Accesses metric definitions primarily via integrations and is the source of truth for objects defined in dbt projects (like models, macros, sources, metrics). The Discovery API is updated at the end of every dbt Cloud run. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise |
-    
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture-flow.png" title="dbt Semantic components" />
-
-dbt Semantic Layer integrations will:
-
-- Leverage the Discovery API to fetch a list of objects and their attributes, like metrics
-- Generate a dbt-SQL statement
-- Then query the SQL proxy to evaluate the results of this statement
-
-
-## Manage metrics
-
-:::info 📌
-
-New to dbt or metrics? Check out our [quickstart guide](/quickstarts) to build your first dbt project! If you'd like to define your first metrics, try our [Jaffle Shop](https://github.com/dbt-labs/jaffle_shop_metrics) example project.
-
-:::
-
-If you're not sure whether to define a metric in dbt or not, ask yourself the following: 
-
-> *Is this something our teams consistently need to report on?*  
-
-An important business metric should be:
-
-- Well-defined (the definition is agreed upon throughout the entire organization)
-- Time-bound (able to be compared across time)
-
-A great example of this is **revenue** &mdash; it can be aggregated on multiple levels (weekly, monthly, etc) and is key for the broader business to understand.
-
-- ✅ `Monthly recurring revenue` or `Weekly active users` or `Average order value` 
-- ❌ `1-off experimental metric`
-
-
-### Design and define metrics
-
-**Design metrics**
-To read about best practices on structuring and organizing your metrics, review our [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog post first.
-
-**Define metrics**
-You can define your metrics in `.yml` files nested under a metrics key and to design or define your own metrics in your dbt project, review the following documents: <br />
-
-- [How to design and structure dbt metrics: Recommendations for getting started](https://docs.getdbt.com/blog/how-to-design-and-structure-metrics) blog to understand best practices for designing and structuring metrics in your dbt project
-- [dbt metrics](/docs/build/metrics) for in-depth detail on attributes, filters, how to define and query your metrics and [dbt-metrics package](https://github.com/dbt-labs/dbt_metrics)
-- [dbt Semantic Layer quickstart](/docs/use-dbt-semantic-layer/quickstart-semantic-layer) to get started
-- [Understanding the components of the dbt Semantic Layer](https://docs.getdbt.com/blog/understanding-the-components-of-the-dbt-semantic-layer) blog post to see further examples
-    
-Review our helpful metrics video below, which explains what metrics are, why they're important and how you can get started:
-    
-<LoomVideo id="b120ca9d042d46abad1d873a676bf20a" />    
-
-## Related questions
-    
-<details>
-  <summary>How are you storing my data?</summary>
-  <div>
-    <div>The dbt Semantic Layer does not store, or cache, or log your data. On each query to the Semantic Layer, the resulting data passes through dbt Cloud servers where it is never stored, cached, or logged. The data from your data platform gets routed through dbt Cloud servers, to your connecting data tool.</div>
-  </div>
-</details>
-<details>
-  <summary>Is the dbt Semantic Layer open source?</summary>
-  <div>
-    <div>Some components of the dbt Semantic Layer are open source like dbt-core, the dbt_metrics package, and the BSL licensed dbt-server. The dbt Proxy Server (what is actually compiling the dbt code) and the Discovery API are not open source. <br></br><br></br>
-      
-During Public Preview, the dbt Semantic Layer is open to all dbt Cloud tiers (Developer, Team, and Enterprise).<br></br><br></br>
-
-<ul>
-<li>dbt Core users can define metrics in their dbt Core projects and calculate them using macros from the metrics package. To use the dbt Semantic Layer integrations, users will need to have a dbt Cloud account.</li><br></br>
-<li>Developer accounts will be able to query the Proxy Server using SQL, but will not be able to browse pre-populated dbt metrics in external tools, which requires access to the Discovery API.</li><br></br>
-<li>Team and Enterprise accounts will be able to set up the Semantic Layer and Discovery API in the integrated partner tool to import metric definition.</li>
-    </ul></div> </div>
-</details>
-<details>
-    <summary>Is there a dbt Semantic Layer discussion hub?</summary>
-  <div>
-    <div>Yes absolutely! Join the <a href="https://getdbt.slack.com">dbt Slack community</a> and <a href="https://getdbt.slack.com/archives/C046L0VTVR6">#dbt-cloud-semantic-layer slack channel</a> for all things related to the dbt Semantic Layer. 
-    </div>
-  </div>
-</details>
-      <br></br>   

From 9ea81b680b4a361799c2fed8eee7c4718bd77e25 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 3 Aug 2023 11:42:23 -0700
Subject: [PATCH 508/703] Update ide-v1.2.md (#3856)

## What are you changing in this pull request and why?
<!---
Describe your changes and why you're making them. If linked to an open
issue or a pull request on dbt Core, then link to them here!

To learn more about the writing conventions used in the dbt Labs docs,
see the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md).
-->

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

---------

Co-authored-by: Jason Ganz <jason.ganz@dbtlabs.com>
Co-authored-by: gwen windflower <gwenwindflower@gmail.com>
---
 .../release-notes/05-Aug-2023/ide-v1.2.md     |     4 +-
 website/package-lock.json                     | 88353 ++++++++--------
 website/static/_redirects                     |     1 +
 3 files changed, 43971 insertions(+), 44387 deletions(-)

diff --git a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md
index d2f86724b07..10baa5cd6d7 100644
--- a/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md
+++ b/website/docs/docs/dbt-versions/release-notes/05-Aug-2023/ide-v1.2.md
@@ -1,5 +1,5 @@
 ---
-title: "Update: Cloud IDE v1.2 with new service"
+title: "Update: Cloud IDE v1.2 includes a new service"
 description: "August 2023: Cloud IDE now uses dbt-server to provide more reliable service and dbt Core feature parity, including support for commands like `dbt list`."
 sidebar_label: "Update: Cloud IDE v1.2"
 tags: [Aug-2023, IDE]
@@ -7,7 +7,7 @@ date: 2023-08-03
 sidebar_position: 8
 ---
 
-We're excited to announce that we replaced the backend service that powers the Cloud IDE with a more reliable server -- dbt-server. This significant update follows the rebuild of the IDE frontend last year. We're committed to improving our IDE to provide you with a better experience.
+We're excited to announce that we replaced the backend service that powers the Cloud IDE with a more reliable server -- dbt-server. Because this release contains foundational changes, IDE v1.2 requires dbt v1.6 or higher. This significant update follows the rebuild of the IDE frontend last year. We're committed to improving the IDE to provide you with a better experience.
 
 Previously, the Cloud IDE used dbt-rpc, an outdated service that was unable to stay up-to-date with changes from dbt-core. The dbt-rpc integration used legacy dbt-core entry points and logging systems, causing it to be sluggish, brittle, and poorly tested. The Core team had been working around this outdated technology to avoid breaking it, which prevented them from developing with velocity and confidence.
 
diff --git a/website/package-lock.json b/website/package-lock.json
index 9bb80cad9f9..b15a903e97f 100644
--- a/website/package-lock.json
+++ b/website/package-lock.json
@@ -1,44385 +1,43968 @@
-{
-  "name": "website",
-  "version": "0.0.0",
-  "lockfileVersion": 2,
-  "requires": true,
-  "packages": {
-    "": {
-      "version": "0.0.0",
-      "dependencies": {
-        "@docusaurus/core": "2.3.1",
-        "@docusaurus/plugin-ideal-image": "2.3.1",
-        "@docusaurus/preset-classic": "2.3.1",
-        "@docusaurus/theme-search-algolia": "2.3.1",
-        "@mdx-js/react": "^1.6.21",
-        "@monaco-editor/react": "^4.4.6",
-        "@stoplight/elements": "^7.7.17",
-        "@svgr/webpack": "^6.0.0",
-        "axios": "^0.27.2",
-        "classnames": "^2.3.1",
-        "clsx": "^1.1.1",
-        "color": "^3.1.2",
-        "core-js": "^3.20.0",
-        "feed": "^4.2.2",
-        "file-loader": "^6.2.0",
-        "fs": "0.0.2",
-        "gray-matter": "^4.0.3",
-        "hast-util-is-element": "^1.1.0",
-        "js-yaml": "^4.1.0",
-        "mobx": "^6.3.9",
-        "node-polyfill-webpack-plugin": "^1.1.4",
-        "papaparse": "^5.3.2",
-        "prism-react-renderer": "^1.2.1",
-        "query-string": "^8.1.0",
-        "raw-loader": "^4.0.2",
-        "react": "^17.0.1",
-        "react-dom": "^17.0.1",
-        "react-full-screen": "^1.1.1",
-        "react-is": "^18.1.0",
-        "react-tooltip": "^4.2.21",
-        "redoc": "^2.0.0-rc.57",
-        "rehype-katex": "^5.0.0",
-        "remark-math": "^3.0.1",
-        "sanitize-html": "^2.8.0",
-        "slugify": "^1.6.1",
-        "styled-components": "5.3.3",
-        "swiper": "^8.4.3",
-        "url-loader": "^4.1.1"
-      },
-      "devDependencies": {
-        "@cypress/react": "^7.0.2",
-        "@cypress/webpack-dev-server": "^3.2.3",
-        "@docusaurus/eslint-plugin": "2.3.1",
-        "@swc/core": "^1.2.174",
-        "@swc/jest": "^0.2.20",
-        "@testing-library/jest-dom": "^5.16.2",
-        "@testing-library/react": "^12.1.3",
-        "@testing-library/user-event": "^13.5.0",
-        "@typescript-eslint/eslint-plugin": "^5.54.0",
-        "@typescript-eslint/parser": "^5.54.0",
-        "css-loader": "^3.4.2",
-        "cypress": "^12.6.0",
-        "dotenv": "^10.0.0",
-        "eslint": "^8.35.0",
-        "eslint-config-prettier": "^8.6.0",
-        "eslint-plugin-markdown": "^3.0.0",
-        "eslint-plugin-prettier": "^4.2.1",
-        "husky": "^8.0.3",
-        "jest": "^27.5.1",
-        "jest-cli": "^27.5.1",
-        "lint-staged": "^13.1.2",
-        "path-browserify": "^1.0.1",
-        "process": "^0.11.10",
-        "stream-http": "^3.2.0",
-        "style-loader": "^1.1.3",
-        "svg-inline-loader": "^0.8.2",
-        "tty-browserify": "0.0.1",
-        "webpack": "^5.75.0",
-        "webpack-dev-server": "^4.11.1"
-      },
-      "optionalDependencies": {
-        "@dbt-labs/react-dbt-dag": "0.8.7"
-      }
-    },
-    "node_modules/@adobe/css-tools": {
-      "version": "4.2.0",
-      "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.2.0.tgz",
-      "integrity": "sha512-E09FiIft46CmH5Qnjb0wsW54/YQd69LsxeKUOWawmws1XWvyFGURnAChH0mlr7YPFR1ofwvUQfcL0J3lMxXqPA==",
-      "dev": true
-    },
-    "node_modules/@algolia/autocomplete-core": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.7.4.tgz",
-      "integrity": "sha512-daoLpQ3ps/VTMRZDEBfU8ixXd+amZcNJ4QSP3IERGyzqnL5Ch8uSRFt/4G8pUvW9c3o6GA4vtVv4I4lmnkdXyg==",
-      "dependencies": {
-        "@algolia/autocomplete-shared": "1.7.4"
-      }
-    },
-    "node_modules/@algolia/autocomplete-preset-algolia": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.7.4.tgz",
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-      },
-      "peerDependencies": {
-        "@algolia/client-search": ">= 4.9.1 < 6",
-        "algoliasearch": ">= 4.9.1 < 6"
-      }
-    },
-    "node_modules/@algolia/autocomplete-shared": {
-      "version": "1.7.4",
-      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.7.4.tgz",
-      "integrity": "sha512-2VGCk7I9tA9Ge73Km99+Qg87w0wzW4tgUruvWAn/gfey1ZXgmxZtyIRBebk35R1O8TbK77wujVtCnpsGpRy1kg=="
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-    "node_modules/@algolia/cache-browser-local-storage": {
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-    "node_modules/@algolia/cache-in-memory": {
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-      }
-    },
-    "node_modules/@algolia/client-common": {
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-        "@algolia/transporter": "4.16.0"
-      }
-    },
-    "node_modules/@algolia/client-personalization": {
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+      "requires": {
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+      }
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+      "dev": true
+    },
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+      "resolved": "https://registry.npmjs.org/xml-parser-xo/-/xml-parser-xo-3.2.0.tgz",
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+      "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
+      "dev": true,
+      "requires": {
+        "cliui": "^7.0.2",
+        "escalade": "^3.1.1",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.0",
+        "y18n": "^5.0.5",
+        "yargs-parser": "^20.2.2"
+      },
+      "dependencies": {
+        "emoji-regex": {
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+        },
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+          "dev": true,
+          "requires": {
+            "emoji-regex": "^8.0.0",
+            "is-fullwidth-code-point": "^3.0.0",
+            "strip-ansi": "^6.0.1"
+          }
+        }
+      }
+    },
+    "yargs-parser": {
+      "version": "20.2.9",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz",
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+      "dev": true
+    },
+    "yauzl": {
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+      "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz",
+      "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==",
+      "dev": true,
+      "requires": {
+        "buffer-crc32": "~0.2.3",
+        "fd-slicer": "~1.1.0"
+      }
+    },
+    "yocto-queue": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-1.0.0.tgz",
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+      "dev": true
+    },
+    "zustand": {
+      "version": "3.7.2",
+      "resolved": "https://registry.npmjs.org/zustand/-/zustand-3.7.2.tgz",
+      "integrity": "sha512-PIJDIZKtokhof+9+60cpockVOq05sJzHCriyvaLBmEJixseQ1a5Kdov6fWZfWOu5SK9c+FhH1jU0tntLxRJYMA==",
+      "requires": {}
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+      "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-1.0.5.tgz",
+      "integrity": "sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw=="
+    }
+  }
+}
diff --git a/website/static/_redirects b/website/static/_redirects
index ddb585f17b4..d081f5c6d7c 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -5,6 +5,7 @@ https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  /docs/use-dbt-
 /guides/dbt-ecosystem/sl-partner-integration-guide  /docs/use-dbt-semantic-layer/avail-sl-integrations 301
 /docs/use-dbt-semantic-layer/dbt-semantic-layer /docs/use-dbt-semantic-layer/dbt-sl 301
 /docs/use-dbt-semantic-layer/set-up-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
+/docs/use-dbt-semantic-layer/setup-dbt-semantic-layer /docs/use-dbt-semantic-layer/setup-sl 301
 /docs/use-dbt-semantic-layer/quickstart-semantic-layer /docs/use-dbt-semantic-layer/quickstart-sl 301
 
 ## refocus deploy page

From bf357c5b21d43cbd62fe2d4e038179dd46634a3b Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 15:12:23 -0400
Subject: [PATCH 509/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 6140efc31f8..09f48363d50 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -143,7 +143,7 @@ For Databricks:
 ```sql
 {{
 config(
-materialized='materialized_view',
+       materialized='materialized_view',
 )
 }}
 ```

From ba3aace897dd990ba39b2be420719ab256bc1f14 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 15:12:42 -0400
Subject: [PATCH 510/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 09f48363d50..657d2b6b7d2 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -143,7 +143,7 @@ For Databricks:
 ```sql
 {{
 config(
-       materialized='materialized_view',
+     materialized='materialized_view',
 )
 }}
 ```

From a6646e2f7edf791bd0a09bd06aad18e751c1bb55 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 15:15:17 -0400
Subject: [PATCH 511/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 .../blog/2023-08-01-announcing-materialized-views.md   | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 657d2b6b7d2..98c6de90094 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -158,13 +158,15 @@ For Snowflake:
 
 ```sql
 {{
+{{
 config(
-materialized = 'dynamic_table',
-snowflake_warehouse = '<warehouse>',
-target_lag = '<desired_lag>',
-on_configuration_change = 'apply',
+    materialized = 'dynamic_table',
+    snowflake_warehouse = '<warehouse>',
+    target_lag = '<desired_lag>',
+    on_configuration_change = 'apply',
 )
 }}
+}}
 ```
 
 Now if you do need to more fully build out your development pipeline (making sure scheduling/syncs do happen), you can schedule but make sure to drop the materialized views when you are done with them. I encourage you to invest in an operations macro that drops all MVs in the schema that you use as your sandbox and run it as needed. You could even create a dbt Cloud job to manage that. This way, you don’t have any stray MVs running in your sandbox, consuming credits unnecessarily.

From 910f18e4d68f163f22e378062d0e599f62dff58a Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 3 Aug 2023 15:17:47 -0400
Subject: [PATCH 512/703] Update
 website/blog/2023-08-01-announcing-materialized-views.md

---
 website/blog/2023-08-01-announcing-materialized-views.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/website/blog/2023-08-01-announcing-materialized-views.md b/website/blog/2023-08-01-announcing-materialized-views.md
index 98c6de90094..3917e3f192c 100644
--- a/website/blog/2023-08-01-announcing-materialized-views.md
+++ b/website/blog/2023-08-01-announcing-materialized-views.md
@@ -158,7 +158,6 @@ For Snowflake:
 
 ```sql
 {{
-{{
 config(
     materialized = 'dynamic_table',
     snowflake_warehouse = '<warehouse>',
@@ -166,7 +165,6 @@ config(
     on_configuration_change = 'apply',
 )
 }}
-}}
 ```
 
 Now if you do need to more fully build out your development pipeline (making sure scheduling/syncs do happen), you can schedule but make sure to drop the materialized views when you are done with them. I encourage you to invest in an operations macro that drops all MVs in the schema that you use as your sandbox and run it as needed. You could even create a dbt Cloud job to manage that. This way, you don’t have any stray MVs running in your sandbox, consuming credits unnecessarily.

From 2ff387a7f5f861a54e6cece8cf36ce38570dc2bb Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Thu, 3 Aug 2023 17:21:38 -0700
Subject: [PATCH 513/703] Making Semantic Layer QS and getting started guides
 consistent (#3858)

## What are you changing in this pull request and why?

As requested by @Jstein77, making certain sections of guides consistent.

* added reusables for sections that are the same.
* Copied content from the getting started guide to the reusables.
* Put reusables in 1.6 section only

cc @mirnawong1

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page
---
 website/docs/docs/build/sl-getting-started.md | 205 +-----------------
 .../use-dbt-semantic-layer/quickstart-sl.md   |  98 +--------
 website/snippets/_sl-configure-metricflow.md  |   1 +
 website/snippets/_sl-create-semanticmodel.md  | 150 +++++++++++++
 website/snippets/_sl-define-metrics.md        |  19 ++
 website/snippets/_sl-install-metricflow.md    |   7 +
 .../snippets/_sl-test-and-query-metrics.md    |  19 ++
 7 files changed, 219 insertions(+), 280 deletions(-)
 create mode 100644 website/snippets/_sl-configure-metricflow.md
 create mode 100644 website/snippets/_sl-create-semanticmodel.md
 create mode 100644 website/snippets/_sl-define-metrics.md
 create mode 100644 website/snippets/_sl-install-metricflow.md
 create mode 100644 website/snippets/_sl-test-and-query-metrics.md

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index dbd953b2a5c..7259a7eb22c 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -8,6 +8,12 @@ meta:
   api_name: dbt Semantic Layer API
 ---
 
+import InstallMetricFlow from '/snippets/_sl-install-metricflow.md';
+import CreateModel from '/snippets/_sl-create-semanticmodel.md';
+import DefineMetrics from '/snippets/_sl-define-metrics.md';
+import ConfigMetric from '/snippets/_sl-configure-metricflow.md';
+import TestQuery from '/snippets/_sl-test-and-query-metrics.md';
+
 This getting started page presents a sample workflow to help you create your first metrics. It uses the [Jaffle shop example project](https://github.com/dbt-labs/jaffle-sl-template) as the project data source and is available for you to use. If you prefer, you can create semantic models and metrics for your own dbt project.
 
 To fully experience the power of a universal dbt Semantic Layer, take the following steps:
@@ -38,212 +44,23 @@ New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/
 
 ## Install MetricFlow
 
-Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
-
-Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
+<InstallMetricFlow />
 
-- Create or activate your virtual environment. `python -m venv venv` or `source your-venv/bin/activate`
-- Run `pip install "dbt-metricflow[your_adapter_name]"`
-  * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
- 
 ## Create a semantic model
 
-The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
-
-We highly recommend you read the overview of what a [semantic model](https://docs.getdbt.com/docs/build/semantic-models) is before getting started. If you're working in the [Jaffle shop example](https://github.com/dbt-labs/jaffle-sl-template), delete the `orders.yml` config or delete the .yml extension so it's ignored during parsing. **We'll be rebuilding it step by step in this example.** 
-
-If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly. 
-
-1. Create a new yml config file for the orders model, such as `orders.yml`. 
-
-It's best practice to create semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name and appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now, `default_agg_time_dimension` is the only supported default. 
-
-```yaml
-semantic_models:
-  #The name of the semantic model.
-  - name: orders
-    defaults:
-      agg_time_dimension: ordered_at
-    description: |
-      Order fact table. This table is at the order grain with one row per order. 
-    #The name of the dbt model and schema
-    model: ref('orders')
-  ```
-
-2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `order_id`, and so on.
-
-```yaml
-  #Entities. These usually correspond to keys in the table.
-    entities:
-      - name: order_id
-        type: primary
-      - name: location
-        type: foreign
-        expr: location_id
-      - name: customer
-        type: foreign
-        expr: customer_id
-  ```
-
-3. Define your dimensions and measures. Dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
-
-```yaml
-    #Measures. These are the aggregations on the columns in the table.
-    measures: 
-      - name: order_total
-        description: The total revenue for each order.
-        agg: sum
-      - name: order_count
-        expr: 1
-        agg: sum
-      - name: tax_paid
-        description: The total tax paid on each order. 
-        agg: sum
-      - name: customers_with_orders
-        description: Distinct count of customers placing orders
-        agg: count_distinct
-        expr: customer_id
-      - name: locations_with_orders
-        description: Distinct count of locations with order
-        expr: location_id
-        agg: count_distinct
-      - name: order_cost
-        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
-        agg: sum
-  #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
-    dimensions:
-      - name: ordered_at
-        type: time
-        type_params:
-          time_granularity: day 
-      - name: order_total_dim
-        type: categorical
-        expr: order_total
-      - name: is_food_order
-        type: categorical
-      - name: is_drink_order
-        type: categorical  
-```
-
-Putting it all together, a complete semantic model configurations based on the order model would look like the following example:
-
-```yaml
-semantic_models:
-  #The name of the semantic model.
-  - name: orders
-    defaults:
-      agg_time_dimension: ordered_at
-    description: |
-      Order fact table. This table is at the order grain with one row per order. 
-    #The name of the dbt model and schema
-    model: ref('orders')
-    #Entities. These usually corespond to keys in the table.
-    entities:
-      - name: order_id
-        type: primary
-      - name: location
-        type: foreign
-        expr: location_id
-      - name: customer
-        type: foreign
-        expr: customer_id
-    #Measures. These are the aggregations on the columns in the table.
-    measures: 
-      - name: order_total
-        description: The total revenue for each order.
-        agg: sum
-      - name: order_count
-        expr: 1
-        agg: sum
-      - name: tax_paid
-        description: The total tax paid on each order. 
-        agg: sum
-      - name: customers_with_orders
-        description: Distinct count of customers placing orders
-        agg: count_distinct
-        expr: customer_id
-      - name: locations_with_orders
-        description: Distinct count of locations with order
-        expr: location_id
-        agg: count_distinct
-      - name: order_cost
-        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
-        agg: sum
-    #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
-    dimensions:
-      - name: ordered_at
-        type: time
-        type_params:
-          time_granularity: day 
-      - name: order_total_dim
-        type: categorical
-        expr: order_total
-      - name: is_food_order
-        type: categorical
-      - name: is_drink_order
-        type: categorical  
-```
-
-:::tip
-If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
-
-```sql
-select
-  metric_time_day,  -- time
-  country,  -- categorical dimension
-  sum(revenue_usd) -- measure
-from
-  snowflake.fact_transactions  -- sql table
-group by metric_time_day, country  -- dimensions
-  ```
-:::
+<CreateModel />
 
 ## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](/docs/build/metrics-overview) before getting started. 
-
-1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line. 
-
-2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the original `orders.yml` file, or removing the .yml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step. 
-
-```yaml
-metrics:
-  - name: order_total
-    description: Sum of total order amonunt. Includes tax + revenue.
-    type: simple
-    label: Order Total
-    type_params:
-      measure: order_total
-```
- 
-3. Save your code, and in the next section, you'll validate your configs before committing them to your repository.
-
-To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
+<DefineMetrics />
 
 ## Configure the MetricFlow time spine model
 
-MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article](/docs/build/metricflow-time-spine) explains how to add the `metricflow_time_spine` model to your project.
+<ConfigMetric />
 
 ## Test and query metrics
 
-This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
-
-:::tip
-- dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternatively, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
-
-- dbt Core or Developer plan &mdash; Users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
-:::
-
-**Query and commit your metrics using the CLI:**
-
-MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before preceding to ensure the data exists in your warehouse. 
-
-1. Make sure you have the MetricFlow CLI installed and up to date.
-2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
-3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. For example, `mf query --metrics order_total --group-by metric_time`
-4. Verify that the metric values are what you expect. To further understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
-5. Run `mf validate-configs` to run validation on your semantic models and metrics. 
-6. Commit and merge the code changes that contain the metric definitions.
+<TestQuery />
 
 ## Run a production job
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 20729d1879d..8dbcc6c46f3 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -11,6 +11,13 @@ meta:
 <VersionBlock firstVersion="1.6">
 
 import NewSLChanges from '/snippets/_new-sl-changes.md';
+import InstallMetricFlow from '/snippets/_sl-install-metricflow.md';
+import CreateModel from '/snippets/_sl-create-semanticmodel.md';
+import DefineMetrics from '/snippets/_sl-define-metrics.md';
+import ConfigMetric from '/snippets/_sl-configure-metricflow.md';
+import TestQuery from '/snippets/_sl-test-and-query-metrics.md';
+
+
 
 <NewSLChanges />
 
@@ -39,100 +46,19 @@ New to dbt or metrics? Try our [Jaffle shop example project](https://github.com/
 
 ## Install MetricFlow
 
-Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
-
-Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
-
-- Create or activate your virtual environment. `python -m venv venv`
-- `pip install "dbt-metricflow[your_adapter_name]"`
-  * You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
- 
-Currently, the supported adapters are Snowflake and Postgres (BigQuery, Databricks, and Redshift coming soon). 
+<InstallMetricFlow />
 
 ## Create a semantic model
 
-This step will guide you through setting up your semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).
-
-1. Name your semantic model, fill in appropriate metadata, and map it to a model in your dbt project. 
-
-```yaml
-semantic_models:
-  - name: transactions
-    description: |
-    This table captures every transaction starting July 02, 2014. Each row represents one transaction
-    model: ref('fact_transactions')
-  ```
-
-2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `transaction_id`, and so on.
-
-```yaml
-  entities:
-    - name: transaction
-      type: primary
-      expr: id_transaction
-    - name: customer
-      type: foreign
-      expr: id_customer
-  ```
-
-3. Define your dimensions and measures. dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
-
-```yaml
-measures:
-    - name: transaction_amount_usd
-      description: The total USD value of the transaction.
-      agg: sum
-  dimensions:
-    - name: is_large
-      type: categorical
-      expr: case when transaction_amount_usd >= 30 then true else false end
-```
-
-:::tip
-
-If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
-
-```sql
-select
-  metric_time_day,  -- time
-  country,  -- categorical dimension
-  sum(revenue_usd) -- measure
-from
-  snowflake.fact_transactions  -- sql table
-group by metric_time_day, country  -- dimensions
-  ```
-:::
+<CreateModel />
 
 ## Define metrics
 
-Now that you've created your first semantic model, it's time to define your first metric. MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). 
-
-1. You can define metrics in the same YAML files as your semantic models, or create a new file.
-
-2. The example metric we'll create is a simple metric that refers directly to a measure, based on the `transaction_amount_usd` measure, which will be implemented as a `sum()` function in SQL.
-
-```yaml
----
-metrics:
-  - name: transaction_amount_usd
-    type: simple
-    type_params:
-    measure: transaction_amount_usd
-```
- 
-3. Click **Save** and then **Preview** the code in the dbt Cloud IDE.
-
-## Test metrics
-
-The following steps explain how to test and manually query your metrics. Currently, you can only manually test your metrics using the CLI (dbt Cloud IDE support coming soon)
+<DefineMetrics />
 
-1. Make sure you have the [MetricFlow CLI](/docs/build/metricflow-cli) installed and up to date.
-2. In the CLI, run `mf validate-configs` to validate the changes before committing them.
-3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to manually query the metrics and dimensions.
-4. Verify that the metric values are what you expect. You can view the generated SQL if you type `--explain` in the CLI.
-5. Commit and merge the code changes that contain the metric definitions.
+## Test and query metrics
 
-To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
+<TestQuery />
 
 ## Run a production job
 
diff --git a/website/snippets/_sl-configure-metricflow.md b/website/snippets/_sl-configure-metricflow.md
new file mode 100644
index 00000000000..10f92161783
--- /dev/null
+++ b/website/snippets/_sl-configure-metricflow.md
@@ -0,0 +1 @@
+MetricFlow requires a time spine for certain metric types and join resolution patterns, like cumulative metrics. You will have to create this model in your dbt project. [This article](/docs/build/metricflow-time-spine) explains how to add the `metricflow_time_spine` model to your project.
diff --git a/website/snippets/_sl-create-semanticmodel.md b/website/snippets/_sl-create-semanticmodel.md
new file mode 100644
index 00000000000..032bbe37143
--- /dev/null
+++ b/website/snippets/_sl-create-semanticmodel.md
@@ -0,0 +1,150 @@
+The following steps will walk you through setting up semantic models in your dbt project, which consist of [entities](/docs/build/entities), [dimensions](/docs/build/dimensions), and [measures](/docs/build/measures).  
+
+We highly recommend you read the overview of what a [semantic model](/docs/build/semantic-models) is before getting started. If you're working in the [Jaffle shop example](https://github.com/dbt-labs/jaffle-sl-template), delete the `orders.yml` config or delete the .yml extension so it's ignored during parsing. **We'll be rebuilding it step by step in this example.**
+
+If you're following the guide in your own project, pick a model that you want to build a semantic manifest from and fill in the config values accordingly.
+
+1. Create a new yml config file for the orders model, such as `orders.yml`.
+
+It's best practice to create semantic models in the `/models/semantic_models` directory in your project. Semantic models are nested under the `semantic_models` key. First, fill in the name and appropriate metadata, map it to a model in your dbt project, and specify model defaults. For now, `default_agg_time_dimension` is the only supported default.
+
+```yaml
+semantic_models:
+  #The name of the semantic model.
+  - name: orders
+    defaults:
+      agg_time_dimension: ordered_at
+    description: |
+      Order fact table. This table is at the order grain with one row per order. 
+    #The name of the dbt model and schema
+    model: ref('orders')
+  ```
+
+2. Define your entities. These are the keys in your table that MetricFlow will use to join other semantic models. These are usually columns like `customer_id`, `order_id`, and so on.
+
+```yaml
+  #Entities. These usually correspond to keys in the table.
+    entities:
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
+  ```
+
+3. Define your dimensions and measures. Dimensions are properties of the records in your table that are non-aggregatable. They provide categorical or time-based context to enrich metrics. Measures are the building block for creating metrics. They are numerical columns that MetricFlow aggregates to create metrics.
+
+```yaml
+    #Measures. These are the aggregations on the columns in the table.
+    measures: 
+      - name: order_total
+        description: The total revenue for each order.
+        agg: sum
+      - name: order_count
+        expr: 1
+        agg: sum
+      - name: tax_paid
+        description: The total tax paid on each order. 
+        agg: sum
+      - name: customers_with_orders
+        description: Distinct count of customers placing orders
+        agg: count_distinct
+        expr: customer_id
+      - name: locations_with_orders
+        description: Distinct count of locations with order
+        expr: location_id
+        agg: count_distinct
+      - name: order_cost
+        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
+        agg: sum
+  #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
+    dimensions:
+      - name: ordered_at
+        type: time
+        type_params:
+          time_granularity: day 
+      - name: order_total_dim
+        type: categorical
+        expr: order_total
+      - name: is_food_order
+        type: categorical
+      - name: is_drink_order
+        type: categorical  
+```
+
+Putting it all together, a complete semantic model configurations based on the order model would look like the following example:
+
+```yaml
+semantic_models:
+  #The name of the semantic model.
+  - name: orders
+    defaults:
+      agg_time_dimension: ordered_at
+    description: |
+      Order fact table. This table is at the order grain with one row per order. 
+    #The name of the dbt model and schema
+    model: ref('orders')
+    #Entities. These usually corespond to keys in the table.
+    entities:
+      - name: order_id
+        type: primary
+      - name: location
+        type: foreign
+        expr: location_id
+      - name: customer
+        type: foreign
+        expr: customer_id
+    #Measures. These are the aggregations on the columns in the table.
+    measures: 
+      - name: order_total
+        description: The total revenue for each order.
+        agg: sum
+      - name: order_count
+        expr: 1
+        agg: sum
+      - name: tax_paid
+        description: The total tax paid on each order. 
+        agg: sum
+      - name: customers_with_orders
+        description: Distinct count of customers placing orders
+        agg: count_distinct
+        expr: customer_id
+      - name: locations_with_orders
+        description: Distinct count of locations with order
+        expr: location_id
+        agg: count_distinct
+      - name: order_cost
+        description: The cost for each order item. Cost is calculated as a sum of the supply cost for each order item. 
+        agg: sum
+    #Dimensions. Either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.  
+    dimensions:
+      - name: ordered_at
+        type: time
+        type_params:
+          time_granularity: day 
+      - name: order_total_dim
+        type: categorical
+        expr: order_total
+      - name: is_food_order
+        type: categorical
+      - name: is_drink_order
+        type: categorical  
+```
+
+:::tip
+If you're familiar with writing SQL, you can think of dimensions as the columns you would group by and measures as the columns you would aggregate.
+
+```sql
+select
+  metric_time_day,  -- time
+  country,  -- categorical dimension
+  sum(revenue_usd) -- measure
+from
+  snowflake.fact_transactions  -- sql table
+group by metric_time_day, country  -- dimensions
+  ```
+
+:::
diff --git a/website/snippets/_sl-define-metrics.md b/website/snippets/_sl-define-metrics.md
new file mode 100644
index 00000000000..77d3a51c873
--- /dev/null
+++ b/website/snippets/_sl-define-metrics.md
@@ -0,0 +1,19 @@
+Now that you've created your first semantic model, it's time to define your first metric! MetricFlow supports different metric types like [simple](/docs/build/simple), [ratio](/docs/build/ratio), [cumulative](/docs/build/cumulative), and [derived](/docs/build/derived). It's recommended that you read the [metrics overview docs](/docs/build/metrics-overview) before getting started.
+
+1. You can define metrics in the same YAML files as your semantic models or create a new file. If you want to create your metrics in a new file, create another directory called `/models/metrics`. The file structure for metrics can become more complex from here if you need to further organize your metrics, for example, by data source or business line.
+
+2. The example metric we'll create is a simple metric that refers directly to the the `order_total` measure, which will be implemented as a `sum()` function in SQL. Again, if you're working in the Jaffle shop sandbox, we recommend deleting the original `orders.yml` file, or removing the .yml extension so it's ignored during parsing. We'll be rebuilding the `order_total` metric from scratch. If you're working in your own project, create a simple metric like the one below using one of the measures you created in the previous step.
+
+```yaml
+metrics:
+  - name: order_total
+    description: Sum of total order amonunt. Includes tax + revenue.
+    type: simple
+    label: Order Total
+    type_params:
+      measure: order_total
+```
+
+3. Save your code, and in the next section, you'll validate your configs before committing them to your repository.
+
+To continue building out your metrics based on your organization's needs, refer to the [Build your metrics](/docs/build/build-metrics-intro) for detailed info on how to define different metric types and semantic models.
diff --git a/website/snippets/_sl-install-metricflow.md b/website/snippets/_sl-install-metricflow.md
new file mode 100644
index 00000000000..4677cd572fe
--- /dev/null
+++ b/website/snippets/_sl-install-metricflow.md
@@ -0,0 +1,7 @@
+Before you begin, install the [MetricFlow CLI](/docs/build/metricflow-cli) as an extension of a dbt adapter from PyPI. The MetricFlow CLI is compatible with Python versions 3.8, 3.9, 3.10 and 3.11
+
+Use pip install `metricflow` and your [dbt adapter](/docs/supported-data-platforms):
+
+- Create or activate your virtual environment. `python -m venv venv` or `source your-venv/bin/activate`
+- Run `pip install "dbt-metricflow[your_adapter_name]"`
+  - You must specify `[your_adapter_name]`. For example, run `pip install "dbt-metricflow[snowflake]"` if you use a Snowflake adapter.
diff --git a/website/snippets/_sl-test-and-query-metrics.md b/website/snippets/_sl-test-and-query-metrics.md
new file mode 100644
index 00000000000..8f859c1c1f6
--- /dev/null
+++ b/website/snippets/_sl-test-and-query-metrics.md
@@ -0,0 +1,19 @@
+This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
+
+:::tip
+
+- dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternatively, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
+
+- dbt Core or Developer plan &mdash; Users can only test and query metrics manually using the CLI, but won't be able to use the dbt Semantic Layer to dynamically query metrics.
+:::
+
+**Query and commit your metrics using the CLI:**
+
+MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before preceding to ensure the data exists in your warehouse.
+
+1. Make sure you have the MetricFlow CLI installed and up to date.
+2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
+3. Run `mf query --metrics <metric_name> --group-by <dimension_name>` to query the metrics and dimensions. For example, `mf query --metrics order_total --group-by metric_time`
+4. Verify that the metric values are what you expect. To further understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
+5. Run `mf validate-configs` to run validation on your semantic models and metrics.
+6. Commit and merge the code changes that contain the metric definitions.

From 6c45a30c38bf92c6b85ce6fbb88acd60674e7daf Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 3 Aug 2023 20:10:25 -0700
Subject: [PATCH 514/703] Updates matching latest UI

---
 website/docs/docs/deploy/ci-jobs.md            |  16 +++++++++-------
 .../docs/docs/deploy/continuous-integration.md |   2 +-
 website/docs/docs/deploy/job-settings.md       |  11 +++++++----
 .../using-dbt-cloud/ci-job-adv-settings.png    | Bin 0 -> 117665 bytes
 .../using-dbt-cloud/create-ci-job.png          | Bin 0 -> 115033 bytes
 .../using-dbt-cloud/create-deploy-job.png      | Bin 0 -> 126281 bytes
 .../deploy-job-adv-settings.png                | Bin 0 -> 127218 bytes
 7 files changed, 17 insertions(+), 12 deletions(-)
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/ci-job-adv-settings.png
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/create-ci-job.png
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/deploy-job-adv-settings.png

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 849d3e34d9c..eb090f06f0f 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -1,6 +1,6 @@
 ---
 title: "Continuous integration jobs in dbt Cloud"
-sidebar_label: "Continuous integration jobs"
+sidebar_label: "CI jobs"
 description: "Learn how to create and set up CI checks to test code changes before deploying to production."
 ---
 
@@ -16,7 +16,7 @@ You can set up [continuous integration](/docs/deploy/continuous-integration) (CI
 
 ## Set up CI jobs
 
-dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings), and accordingly the CI job in that dedicated environment will be triggered only when PRs are made to the specified, custom branch.
+dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
 
 :::tip Join our beta 
 
@@ -44,9 +44,9 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 <TabItem value="beta" label="Beta version">
 
-To make CI job creation easier, many options on the **CI job** page are set to default values that dbt Labs recommends that you use. You can change some of these defaults and there are some that you can’t (which are greyed out).
+To make CI job creation easier, many options on the **CI job** page are set to default values that dbt Labs recommends that you use. If you don't want to use the defaults, you can change them.
 
-1. On your **Staging** environment page, click **Create Job** > **Continuous Integration Job** to create a new CI job. 
+1. On your deployment environment page, click **Create Job** > **Continuous Integration Job** to create a new CI job. 
 
 2. Options in the **Job Description** section:
     - **Job Name** &mdash; Specify the name for this CI job.
@@ -54,19 +54,21 @@ To make CI job creation easier, many options on the **CI job** page are set to d
     - **Triggered by pull requests** &mdash; By default, it’s enabled.
 
 3. Options in the **Execution Settings** section:
-    - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
     - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred job selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
+    - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since doc generation testing isn't typically performed for every CI check.
-    - **Add API trigger** &mdash; Set up an [API](/docs/dbt-cloud-apis/overview) trigger to run this CI job. In the POST request method, you must also include the fields `github_pull_request_id` and `git_sha`.
+
+  <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-ci-job.png" width="90%" title="Example of CI Job page in dbt Cloud UI"/>
 
 4. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
     - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too many of your warehouse resources.
-    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment.
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this CI job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 
+    <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/ci-job-adv-settings.png" width="90%" title="Example of Advanced Settings on the CI Job page"/>
 
 </TabItem>
 
diff --git a/website/docs/docs/deploy/continuous-integration.md b/website/docs/docs/deploy/continuous-integration.md
index b2f26af728e..779eafbb62c 100644
--- a/website/docs/docs/deploy/continuous-integration.md
+++ b/website/docs/docs/deploy/continuous-integration.md
@@ -48,7 +48,7 @@ Below describes the conditions when CI checks are run concurrently and when they
 
 When you push a new commit to a PR, dbt Cloud enqueues a new CI run for the latest commit and cancels any CI run that is (now) stale and still in flight. This can happen when you’re pushing new commits while a CI build is still in process and not yet done. By cancelling runs in a safe and deliberate way, dbt Cloud helps improve productivity and reduce data platform spend on wasteful CI runs.
 
-<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/example-smart-cancel-job.png" width="70%" title="Example of an automatically cancelled run"/>
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/example-smart-cancel-job.png" width="70%" title="Example of an automatically canceled run"/>
 
 ### Run slot treatment
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index ded5b98a34f..a26deebe88a 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -79,19 +79,22 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs.
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
-    - **Add API trigger** &mdash; Set up an [API](/docs/dbt-cloud-apis/overview) trigger to run this deploy job.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
-    - **Timing** and **Run at** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Exact Intervals** that runs the job at specific times of day, **Intervals** that runs the job every specified number of hours, or to **Enter custom cron schedule (UTC)** that runs the job specified using [cron syntax](#custom-cron-schedule).
-    - **Days of the Week** &mdash; By default, it’s set to every day.
+    - **Timing** and **Run every (UTC)** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or to **Cron Schedule** that runs the job specified using [cron syntax](#custom-cron-schedule).
+    - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
+
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>
+
 5. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this deploy job runs.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this deploy job to the settings in your project.
     - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
-    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment.
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
 
+    <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/deploy-job-adv-settings.png" width="90%" title="Example of Advanced Settings on Deploy Job page"/>
 
 </TabItem>
 
diff --git a/website/static/img/docs/dbt-cloud/using-dbt-cloud/ci-job-adv-settings.png b/website/static/img/docs/dbt-cloud/using-dbt-cloud/ci-job-adv-settings.png
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From 407e087fea18edc1c1896721014fe38fb58445ae Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 3 Aug 2023 20:17:18 -0700
Subject: [PATCH 515/703] Move beta callout

---
 website/docs/docs/deploy/ci-jobs.md      | 16 ++++++++--------
 website/docs/docs/deploy/job-settings.md | 16 ++++++++--------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index eb090f06f0f..acc42a4594f 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -6,6 +6,14 @@ description: "Learn how to create and set up CI checks to test code changes befo
 
 You can set up [continuous integration](/docs/deploy/continuous-integration) (CI) jobs to run when someone opens a new pull request in your dbt repository. By running and testing only _modified_ models, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
 
+:::tip Join our beta 
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the **Beta version** tab below.
+
+If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
+
+:::
+
 ## Prerequisites
 
 - You have a dbt Cloud account.
@@ -18,14 +26,6 @@ You can set up [continuous integration](/docs/deploy/continuous-integration) (CI
 
 dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
 
-:::tip Join our beta 
-
-dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the **Beta version** tab below.
-
-If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
-
-:::
-
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index a26deebe88a..1e50c6f3bdf 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -15,6 +15,14 @@ Jobs make it easy to run dbt commands against a project in your cloud data platf
 
 You can create a job and configure it to run on [scheduled days and times](/docs/deploy/job-triggers#schedule-days) or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules). 
 
+:::tip Join our beta 
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the **Beta version** tab below.
+
+If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
+
+:::
+
 ## Prerequisites
 
 - You must have a dbt Cloud account and [Developer seat license](/docs/cloud/manage-access/seats-and-users). If you don't, you can [sign up](https://www.getdbt.com/signup/) for a [free account](https://www.getdbt.com/pricing/). 
@@ -24,14 +32,6 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 ## Create and schedule jobs {#create-and-schedule-jobs}
 
-:::tip Join our beta 
-
-dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the **Beta version** tab below.
-
-If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
-
-:::
-
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>
 

From 70f5a4a5a9bb0e363c04edcc2183065a24bd2666 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 3 Aug 2023 20:29:27 -0700
Subject: [PATCH 516/703] Add query string to beta links

---
 website/docs/docs/deploy/ci-jobs.md      | 4 ++--
 website/docs/docs/deploy/job-settings.md | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index acc42a4594f..feccd1cb49a 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -8,7 +8,7 @@ You can set up [continuous integration](/docs/deploy/continuous-integration) (CI
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, click on the **Beta version** tab below.
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs).
 
 If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
 
@@ -22,7 +22,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - If you’re using GitLab, you must use a paid or self-hosted account which includes support for GitLab webhooks.
     - If you previously configured your dbt project by providing a generic git URL that clones using SSH, you must reconfigure the project to connect through dbt Cloud's native integration.
 
-## Set up CI jobs
+## Set up CI jobs {#set-up-ci-jobs}
 
 dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 1e50c6f3bdf..40d528757ef 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -17,7 +17,7 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, click on the **Beta version** tab below.
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, refer to [Create and schedule jobs (Beta version)](/docs/deploy/job-settings?version=beta#create-and-schedule-jobs).
 
 If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
 

From d97ec9c8e3c8a989f3ffad66c4e88a5d7043d87a Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 3 Aug 2023 20:34:27 -0700
Subject: [PATCH 517/703] Nits

---
 website/docs/docs/deploy/ci-jobs.md      | 2 +-
 website/docs/docs/deploy/job-settings.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index feccd1cb49a..7a5267ed6bc 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -8,7 +8,7 @@ You can set up [continuous integration](/docs/deploy/continuous-integration) (CI
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs on this, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs).
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs) on this page.
 
 If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 40d528757ef..8be11c4f265 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -17,7 +17,7 @@ You can create a job and configure it to run on [scheduled days and times](/docs
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs on this, refer to [Create and schedule jobs (Beta version)](/docs/deploy/job-settings?version=beta#create-and-schedule-jobs).
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs, refer to [Create and schedule jobs (Beta version)](/docs/deploy/job-settings?version=beta#create-and-schedule-jobs) on this page.
 
 If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
 

From 21e2f3bbdc07deeac1cea81037fe641581e378d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Fri, 4 Aug 2023 05:09:24 +0100
Subject: [PATCH 518/703] Update _new-sl-setup.md

Fast follow to Fix typo and capitalization
---
 website/snippets/_new-sl-setup.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index c03b9d5df5c..54a2f280295 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -6,7 +6,7 @@ You can set up the dbt Semantic Layer in dbt Cloud at the environment and projec
 If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade your dbt version](/docs/dbt-versions/upgrade-core-in-cloud) to dbt v1.6 or higher to use the new dbt Semantic Layer. Refer to the dedicated [migration guide](/guides/migration/sl-migration) for more info.
 :::
 
-1. in dbt Cloud, create a new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
+1. In dbt Cloud, create a new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher.
     * Note &mdash; Deployment environment is currently supported (_development experience coming soon_)
 
 2. Navigate to **Account Settings** and select the specific project you want to enable the Semantic Layer for.
@@ -25,7 +25,7 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 6. You should see connection information that allows you to connect to downstream tools. 
 **Note:** If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
 
-7. Return to the **Project Details** page, then  select **Generate Service Token** to create a [Semantic Layer Only an Metadata Only service token](/docs/dbt-cloud-apis/service-tokens).  
+7. Return to the **Project Details** page, then  select **Generate Service Token** to create a Semantic Layer Only and Metadata Only [service token](/docs/dbt-cloud-apis/service-tokens).  
 
 8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools. For more info on how to integrate with partner integrations, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 

From 5f125fb74afe5bf5897cdb7de0ada8b9f27845a7 Mon Sep 17 00:00:00 2001
From: schottj <julia.schottenstein@fishtownanalytics.com>
Date: Fri, 4 Aug 2023 07:34:45 -0700
Subject: [PATCH 519/703] Update job-settings.md

---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 8be11c4f265..7d69286b542 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -1,6 +1,6 @@
 ---
 title: "Job settings"
-description: "Learn how to create and schedule jobs in dbt Cloud for the scheduler to run. Jobs help you build observability into transformation workflows with the in-app scheduling, logging, and alerting." 
+description: "Learn how to create and schedule jobs in dbt Cloud for the scheduler to run. When you run with dbt Cloud, you get built in observability, logging, and alerting." 
 tags: [scheduler]
 ---
 

From 0e414dd16e1f56c9687849e1ea062cea499c7bf7 Mon Sep 17 00:00:00 2001
From: schottj <julia.schottenstein@fishtownanalytics.com>
Date: Fri, 4 Aug 2023 07:38:40 -0700
Subject: [PATCH 520/703] Update job-settings.md

---
 website/docs/docs/deploy/job-settings.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 7d69286b542..be0e7fd7666 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -4,14 +4,14 @@ description: "Learn how to create and schedule jobs in dbt Cloud for the schedul
 tags: [scheduler]
 ---
 
-Jobs make it easy to run dbt commands against a project in your cloud data platform, triggered either by schedule or events. Each job run in dbt Cloud will have a run history, run status, and a run overview, which provides you with:
+Jobs make it easy to run dbt commands against a project in your cloud data platform, triggered either by schedule or events. Each job run in dbt Cloud will have an entry in the job's run history and a detailed run overview, which provides you with:
 
 - Job trigger type
 - Commit SHA
 - Environment name
-- Sources and documentation info 
+- Sources and documentation info, if applicable
 - Job run details, including run timing, [model timing data](#model-timing), and [artifacts](/docs/deploy/artifacts)
-- Detailed run steps with logs and their statuses
+- Detailed run steps with logs and their run step statuses
 
 You can create a job and configure it to run on [scheduled days and times](/docs/deploy/job-triggers#schedule-days) or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules). 
 

From 578be22283ec52a8ed79e61dbc001fac5cadc435 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 08:36:46 -0700
Subject: [PATCH 521/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index be0e7fd7666..03f3a02b2e2 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -81,7 +81,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
-    - **Timing** and **Run every (UTC)** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or to **Cron Schedule** that runs the job specified using [cron syntax](#custom-cron-schedule).
+    - **Timing** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](#custom-cron-schedule).
     - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>

From 6e9cf18802b4957ca093b0d9d45828fc1d1f69ab Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 08:38:06 -0700
Subject: [PATCH 522/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 7a5267ed6bc..d88c8be8cba 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -51,7 +51,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 2. Options in the **Job Description** section:
     - **Job Name** &mdash; Specify the name for this CI job.
     - **Environment** &mdash; By default, it’s set to the environment you created the CI job from.
-    - **Triggered by pull requests** &mdash; By default, it’s enabled.
+    - **Triggered by pull requests** &mdash; By default, it’s enabled. Every time a developer opens up a pull request or pushes a commit to an existing pull request, this job will get triggered to run.
 
 3. Options in the **Execution Settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred job selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.

From bd1deb3a32d01d30f452674975a68c61242a7282 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 08:38:36 -0700
Subject: [PATCH 523/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index d88c8be8cba..1b8837205a0 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -56,7 +56,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 3. Options in the **Execution Settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred job selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
     - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
-    - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since doc generation testing isn't typically performed for every CI check.
+    - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since most teams do not want to test doc generation on every CI check.
 
   <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-ci-job.png" width="90%" title="Example of CI Job page in dbt Cloud UI"/>
 

From e1b23353322846ac0033a186baac17e18d843534 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 08:39:29 -0700
Subject: [PATCH 524/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 1b8837205a0..8de1f50b165 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -54,7 +54,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
     - **Triggered by pull requests** &mdash; By default, it’s enabled. Every time a developer opens up a pull request or pushes a commit to an existing pull request, this job will get triggered to run.
 
 3. Options in the **Execution Settings** section:
-    - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred job selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
+    - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred environment selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
     - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since most teams do not want to test doc generation on every CI check.
 

From ad685c091cc6261e3f1c7690eb7e9c4f65245845 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 08:39:59 -0700
Subject: [PATCH 525/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 8de1f50b165..48733c400b1 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -63,7 +63,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 4. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
-    - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too many of your warehouse resources.
+    - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too much of your warehouse resources.
     - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this CI job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.

From 288d9f731070fc4b3c9f3a81637b704a66415146 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 09:24:36 -0700
Subject: [PATCH 526/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 03f3a02b2e2..86649c93d7e 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -88,7 +88,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 5. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this deploy job runs.
-    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this deploy job to the settings in your project.
+    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to customize the behavior of your project when this deploy job runs. Environment variables and target name are often used interchangeably. 
     - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
     - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.

From 1257f448a5eee1fdedd781dbfeddeb1d46f2010d Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 09:32:45 -0700
Subject: [PATCH 527/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 48733c400b1..6ac37af398e 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -61,7 +61,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
   <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-ci-job.png" width="90%" title="Example of CI Job page in dbt Cloud UI"/>
 
 4. (optional) Options in the **Advanced Settings** section: 
-    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context.
+    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context. It's common for teams to process only a subset of data for CI runs, using environment variables to branch logic in their dbt project code.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
     - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too much of your warehouse resources.
     - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.

From 64827112932e6e60f2ddd2d9a51f87b0b66cdd6c Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 09:34:14 -0700
Subject: [PATCH 528/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: schottj <julia.schottenstein@fishtownanalytics.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 6ac37af398e..6807e4becd4 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -62,7 +62,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 
 4. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context. It's common for teams to process only a subset of data for CI runs, using environment variables to branch logic in their dbt project code.
-    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
+    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names). Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
     - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too much of your warehouse resources.
     - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.

From 3d2610a5c57fb937e9736f51ed436171ec04e6c9 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Fri, 4 Aug 2023 09:48:25 -0700
Subject: [PATCH 529/703] Fold in feedback from PM

---
 website/docs/docs/deploy/ci-jobs.md      | 7 ++++++-
 website/docs/docs/deploy/job-settings.md | 9 +++++++--
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 48733c400b1..52edadc4372 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -56,6 +56,11 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 3. Options in the **Execution Settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred environment selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
     - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
+
+    :::info
+    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ deploy jobs that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
+    :::
+
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since most teams do not want to test doc generation on every CI check.
 
   <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-ci-job.png" width="90%" title="Example of CI Job page in dbt Cloud UI"/>
@@ -64,7 +69,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this CI job runs. You can specify that a CI job is running in a _Staging_ or _CI_ environment by setting an environment variable and modifying your project code to behave differently, depending on the context.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this CI job to the settings in your project. Similar to **Environment Variables**, this option lets you customize the behavior of the project. You can use this option to specify that a CI job is running in a _Staging_ or _CI_ environment by setting the target name and modifying your project code to behave differently, depending on the context. 
     - **Run Timeout** &mdash; Cancel this CI job if the run time exceeds the timeout value. You can use this option to help ensure that a CI check doesn't consume too much of your warehouse resources.
-    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting. This option to change the version at the job level is useful only when you upgrade a project to the next dbt version; otherwise, mismatched versions between the environment and job can lead to confusing behavior.
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this CI job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 03f3a02b2e2..26321decfa1 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -1,6 +1,6 @@
 ---
 title: "Job settings"
-description: "Learn how to create and schedule jobs in dbt Cloud for the scheduler to run. When you run with dbt Cloud, you get built in observability, logging, and alerting." 
+description: "Learn how to create and schedule jobs in dbt Cloud for the scheduler to run. When you run with dbt Cloud, you get built-in observability, logging, and alerting." 
 tags: [scheduler]
 ---
 
@@ -91,7 +91,12 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to correspond this deploy job to the settings in your project.
     - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
-    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting as mismatched versions can result in unexpected behavior.
+
+    :::info
+    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ deploy jobs that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
+    :::
+
+    - **dbt Version** &mdash; By default, it’s set to inherit the [dbt version](/docs/dbt-versions/core) from the environment. dbt Labs strongly recommends that you don't change the default setting. This option to change the version at the job level is useful only when you upgrade a project to the next dbt version; otherwise, mismatched versions between the environment and job can lead to confusing behavior. 
     - **Threads** &mdash; By default, it’s set to 4 [threads](/docs/core/connect-data-platform/connection-profiles#understanding-threads). Increase the thread count to increase model execution concurrency.
 
     <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/deploy-job-adv-settings.png" width="90%" title="Example of Advanced Settings on Deploy Job page"/>

From a84dc9574fca8ccf74279d37a51151b4b8032a62 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Fri, 4 Aug 2023 10:04:34 -0700
Subject: [PATCH 530/703] Add link to deploy jobs page

---
 website/docs/docs/deploy/ci-jobs.md      | 2 +-
 website/docs/docs/deploy/job-settings.md | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index d78feacb6e2..8befa51bc6e 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -58,7 +58,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
     - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
 
     :::info
-    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ deploy jobs that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
+    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ [deploy jobs](/docs/deploy/job-settings) that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
     :::
 
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since most teams do not want to test doc generation on every CI check.
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index d260daf4a7a..e73684fcfcf 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -13,7 +13,7 @@ Jobs make it easy to run dbt commands against a project in your cloud data platf
 - Job run details, including run timing, [model timing data](#model-timing), and [artifacts](/docs/deploy/artifacts)
 - Detailed run steps with logs and their run step statuses
 
-You can create a job and configure it to run on [scheduled days and times](/docs/deploy/job-triggers#schedule-days) or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules). 
+You can create a deploy job and configure it to run on [scheduled days and times](/docs/deploy/job-triggers#schedule-days) or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules). 
 
 :::tip Join our beta 
 
@@ -35,7 +35,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>
 
-1. Create a new job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
+1. Create a new deploy job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
 1. Provide a job name, for example "Hourly Customer Job". 
 1. Under **Environment**, add the following:
     * **Environment** &mdash; Link to an existing deployment environment.
@@ -57,7 +57,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 <Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/execution-settings.jpg" width="85%" title="Configuring your execution job settings"/>
 
-6. Under the **Triggers** section, you can configure when and how dbt will trigger the job. Refer to [job triggers](/docs/deploy/job-triggers) for more details.
+6. Under the **Triggers** section, you can configure when and how dbt will trigger the deploy job. Refer to [job triggers](/docs/deploy/job-triggers) for more details.
 
     * **Schedule** tab &mdash; Use the **Run on schedule** toggle to configure your job to run on [scheduled](/docs/deploy/job-triggers#schedule-days) days and time, or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules).
     * **Continuous Integration** tab &mdash; Configure [continuous integration (CI)](/docs/deploy/continuous-integration) to run when someone opens a new pull request in your dbt repository.
@@ -65,7 +65,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 <Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
 
-7. Select **Save**, then click **Run Now** to run your job. Click the run and watch its progress under **Run history**.
+7. Select **Save**, then click **Run Now** to run your deploy job. Click the run and watch its progress under **Run history**.
 
 </TabItem>
 

From 6b4d9e780a4323bdbe54f7acb142359c6b6d2772 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 10:14:42 -0700
Subject: [PATCH 531/703] Update website/docs/docs/deploy/job-settings.md

Fix broken links
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index e73684fcfcf..5c9a3cd1e1e 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -81,7 +81,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
-    - **Timing** &mdash; Specify whether to [schedule](#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](#custom-cron-schedule).
+    - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).
     - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>

From 53b3afa4219093be91b2c50b78bc4a7cb720381f Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:56:08 -0700
Subject: [PATCH 532/703] Update website/docs/docs/deploy/ci-jobs.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 8befa51bc6e..08fbd5f4225 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -24,7 +24,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 ## Set up CI jobs {#set-up-ci-jobs}
 
-dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schemas builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch in your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
+dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schema builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch as part of your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
 
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>

From 490d6ee2da2964fc1614125c3a1558535141828b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:57:39 -0700
Subject: [PATCH 533/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 5c9a3cd1e1e..061b77194a3 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -35,7 +35,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>
 
-1. Create a new deploy job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
+1. Create a new deploy job by clicking **Deploy** in the header, click **Jobs**, and then **Create job**.
 1. Provide a job name, for example "Hourly Customer Job". 
 1. Under **Environment**, add the following:
     * **Environment** &mdash; Link to an existing deployment environment.

From f00ed4185f0d160caad26d09c05c72f9b0b15cba Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:57:52 -0700
Subject: [PATCH 534/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 061b77194a3..dd578553bbf 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -78,7 +78,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 3. Options in the **Execution Settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs.
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
-    - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running this deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
+    - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
     - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).

From 1c8997d94dfd0997bb45aef2249d3b30742cf08e Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:58:04 -0700
Subject: [PATCH 535/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index dd578553bbf..622169efb85 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -73,7 +73,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 1. On your deployment environment page, click **Create Job** > **Deploy Job** to create a new deploy job. 
 2. Options in the **Job Description** section:
-    - **Job Name** &mdash; Specify the name for this deploy job. For example, `Daily build`.
+    - **Job Name** &mdash; Specify the name for the deploy job. For example, `Daily build`.
     - **Environment** &mdash;  By default, it’s set to the deployment environment you created the deploy job from.
 3. Options in the **Execution Settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs.

From b46f676f3b22ad8ad3ad99f663e5dc741f48137e Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:58:26 -0700
Subject: [PATCH 536/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 622169efb85..a5398dfe650 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -76,7 +76,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Job Name** &mdash; Specify the name for the deploy job. For example, `Daily build`.
     - **Environment** &mdash;  By default, it’s set to the deployment environment you created the deploy job from.
 3. Options in the **Execution Settings** section:
-    - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when this job runs.
+    - **Commands** &mdash; By default, it includes the `dbt build` command. Click **Add command** to add more [commands](/docs/deploy/job-commands) that you want to be invoked when the job runs.
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:

From 9b843fdd96497ba5bf576529145483f5d1a8ac9b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:58:36 -0700
Subject: [PATCH 537/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index a5398dfe650..b3241162f24 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -80,7 +80,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this deploy job runs.
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
-    - **Run on schedule** &mdash; Enable this option to run this deploy job on a set schedule.
+    - **Run on schedule** &mdash; Enable this option to run the deploy job on a set schedule.
     - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).
     - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
 

From dcd5b6d9d8716a467e555c826245222e15f07374 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:58:59 -0700
Subject: [PATCH 538/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index b3241162f24..07ffc740108 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -81,7 +81,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run the deploy job on a set schedule.
-    - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) this deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).
+    - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) the deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).
     - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>

From aa5d35cc6befde7abb1640d2fe01f22fc1ec254b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:59:09 -0700
Subject: [PATCH 539/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 07ffc740108..3311e161b9b 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -87,7 +87,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>
 
 5. (optional) Options in the **Advanced Settings** section: 
-    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when this deploy job runs.
+    - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when the deploy job runs.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to customize the behavior of your project when this deploy job runs. Environment variables and target name are often used interchangeably. 
     - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.

From 7c0f3bd709cbc1baabe7365a44646cc752a68da0 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:59:35 -0700
Subject: [PATCH 540/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index 3311e161b9b..d19bdce7bc7 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -88,7 +88,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 5. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when the deploy job runs.
-    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to customize the behavior of your project when this deploy job runs. Environment variables and target name are often used interchangeably. 
+    - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to customize the behavior of your project when the deploy job runs. Environment variables and target names are often used interchangeably. 
     - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
 

From 5219b63fb406cc911cd23e074047f7d2733a18af Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 11:59:47 -0700
Subject: [PATCH 541/703] Update website/docs/docs/deploy/job-settings.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/deploy/job-settings.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/job-settings.md
index d19bdce7bc7..8df0f48925d 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/job-settings.md
@@ -89,7 +89,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 5. (optional) Options in the **Advanced Settings** section: 
     - **Environment Variables** &mdash; Define [environment variables](/docs/build/environment-variables) to customize the behavior of your project when the deploy job runs.
     - **Target Name** &mdash; Define the [target name](/docs/build/custom-target-names) to customize the behavior of your project when the deploy job runs. Environment variables and target names are often used interchangeably. 
-    - **Run Timeout** &mdash; Cancel this deploy job if the run time exceeds the timeout value. 
+    - **Run Timeout** &mdash; Cancel the deploy job if the run time exceeds the timeout value. 
     - **Compare changes against an environment (Deferral)** option — By default, it’s set to **No deferral**.
 
     :::info

From 309a6be0f5fddad479cb94638cd15ce6a7a5e111 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Fri, 4 Aug 2023 15:16:23 -0400
Subject: [PATCH 542/703] Updating Usage Language

---
 website/docs/reference/global-configs/usage-stats.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/global-configs/usage-stats.md b/website/docs/reference/global-configs/usage-stats.md
index ea02fe0bb59..ce2d3e49652 100644
--- a/website/docs/reference/global-configs/usage-stats.md
+++ b/website/docs/reference/global-configs/usage-stats.md
@@ -8,14 +8,14 @@ We want to build the best version of dbt possible, and a crucial part of that is
 
 Usage statistics are fired when dbt is invoked and when models are run. These events contain basic platform information (OS + python version) and metadata such as whether the invocation succeeded, how long it took, an anonymized hash key representing the raw model content, and number of nodes that were run. You can see all the event definitions in [`tracking.py`](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/tracking.py).
 
-By default this is turned on – you can opt out of event tracking at any time by adding the following to your `profiles.yml` file:
+By default this is enabled – dbt Core users can opt out of event tracking at any time by adding the following to your `profiles.yml` file:
 
 ```yaml
 config:
   send_anonymous_usage_stats: False
 ```
 
-You can also use the DO_NOT_TRACK environment variable to enable or disable sending anonymous data. For more information, see [Environment variables](/docs/build/environment-variables).
+dbt Core users can also use the DO_NOT_TRACK environment variable to enable or disable sending anonymous data. For more information, see [Environment variables](/docs/build/environment-variables).
 
 `DO_NOT_TRACK=1` is the same as `DBT_SEND_ANONYMOUS_USAGE_STATS=False`
 `DO_NOT_TRACK=0` is the same as `DBT_SEND_ANONYMOUS_USAGE_STATS=True`

From 2f89b869cb634df1ffca0d4f1c2c24ec36e8850e Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Fri, 4 Aug 2023 16:16:43 -0400
Subject: [PATCH 543/703] Update
 website/docs/reference/global-configs/usage-stats.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/reference/global-configs/usage-stats.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/global-configs/usage-stats.md b/website/docs/reference/global-configs/usage-stats.md
index ce2d3e49652..1f9492f4a43 100644
--- a/website/docs/reference/global-configs/usage-stats.md
+++ b/website/docs/reference/global-configs/usage-stats.md
@@ -8,7 +8,7 @@ We want to build the best version of dbt possible, and a crucial part of that is
 
 Usage statistics are fired when dbt is invoked and when models are run. These events contain basic platform information (OS + python version) and metadata such as whether the invocation succeeded, how long it took, an anonymized hash key representing the raw model content, and number of nodes that were run. You can see all the event definitions in [`tracking.py`](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/tracking.py).
 
-By default this is enabled – dbt Core users can opt out of event tracking at any time by adding the following to your `profiles.yml` file:
+By default, this is enabled. dbt Core users can opt out of event tracking at any time by adding the following to your `profiles.yml` file:
 
 ```yaml
 config:

From 3c29c57406bc12648c2c75b03c09ea765dc9ee94 Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Fri, 4 Aug 2023 22:22:21 +0200
Subject: [PATCH 544/703] Update Cloud support for retry with additional nuance

---
 website/docs/reference/commands/retry.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/retry.md b/website/docs/reference/commands/retry.md
index 692b2f6b744..8f5c617ef80 100644
--- a/website/docs/reference/commands/retry.md
+++ b/website/docs/reference/commands/retry.md
@@ -4,9 +4,11 @@ sidebar_label: "retry"
 id: "retry"
 ---
 
-:::warning dbt Cloud support coming soon
+:::info Support in dbt Cloud
 
-Native support for `dbt retry` is coming to dbt Cloud soon.
+`dbt retry` is supported in the dbt Cloud IDE.
+
+Native support for restarting scheduled runs from point of failure is currently in development & coming soon.
 
 :::
 

From e5e4cc88236c718eeec5c3d1da9c89032aecd2fb Mon Sep 17 00:00:00 2001
From: Jeremy Cohen <jeremy@dbtlabs.com>
Date: Fri, 4 Aug 2023 22:25:02 +0200
Subject: [PATCH 545/703] Add back to IDE list

---
 website/docs/reference/dbt-commands.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index 000839820ba..ff9fea4cc00 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -53,6 +53,7 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project
+- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
 - [run](/reference/commands/run): runs the models in a project
 - [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
 - [seed](/reference/commands/seed): loads CSV files into the database

From e43fd90e6b346a2225a9df9efa58ab7db7f5aa4d Mon Sep 17 00:00:00 2001
From: "Alison (she/her)" <alison985@users.noreply.github.com>
Date: Fri, 4 Aug 2023 17:07:59 -0500
Subject: [PATCH 546/703] Update grants.md with grant syntax note (#3814)

## What are you changing in this pull request and why?
Adds a note to avoid some frustration when using grants in a
dbt_project.yml file.

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

---------

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Co-authored-by: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
---
 website/docs/reference/resource-configs/grants.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/grants.md b/website/docs/reference/resource-configs/grants.md
index 8ef726788dc..68d1e6eb14e 100644
--- a/website/docs/reference/resource-configs/grants.md
+++ b/website/docs/reference/resource-configs/grants.md
@@ -121,7 +121,7 @@ For example:
 
 ```yml
 models:
-  +grants:
+  +grants:  # In this case the + is not optional, you must include it for your project to parse.
     select: ['user_a', 'user_b']
 ```
 

From 9163fbecbe30741928e6affb5079abe2e1e52e4a Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Fri, 4 Aug 2023 15:41:56 -0700
Subject: [PATCH 547/703] Update website/docs/reference/dbt-commands.md

---
 website/docs/reference/dbt-commands.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/dbt-commands.md b/website/docs/reference/dbt-commands.md
index ff9fea4cc00..862829ef809 100644
--- a/website/docs/reference/dbt-commands.md
+++ b/website/docs/reference/dbt-commands.md
@@ -53,7 +53,7 @@ Use the following dbt commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/
 - [compile](/reference/commands/compile): compiles (but does not run) the models in a project
 - [deps](/reference/commands/deps): downloads dependencies for a project
 - [docs](/reference/commands/cmd-docs) : generates documentation for a project
-- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or higher)
+- [retry](/reference/commands/retry): retry the last run `dbt` command from the point of failure (requires dbt 1.6 or later)
 - [run](/reference/commands/run): runs the models in a project
 - [run-operation](/reference/commands/run-operation): invoke a macro, including running arbitrary maintenance SQL against the database
 - [seed](/reference/commands/seed): loads CSV files into the database

From 370bf2556124f314eadc27228d8bdceda71eac2a Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Fri, 4 Aug 2023 16:02:14 -0700
Subject: [PATCH 548/703] Add link to Related docs section

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 46a696e695e..21b5f4f921a 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -278,3 +278,7 @@ semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
 - **What is the default output when adding granularity?**<br />
 	The default output follows the format `{time_dimension_name}__{granularity_level}`. So for example, if the time dimension name is `ds` and the granularity level is yearly, the output is `ds__year`.
 
+## Related docs
+
+- [dbt Semantic Layer integration best practices](/guides/dbt-ecosystem/sl-partner-integration-guide)
+

From e869badf22bc6992c0bd2d37c31b047f987f4b05 Mon Sep 17 00:00:00 2001
From: Doug Guthrie <douglas.p.guthrie@gmail.com>
Date: Sat, 5 Aug 2023 16:01:07 -0600
Subject: [PATCH 549/703] Update sl-migration.md

Small fix - adding quote
---
 website/docs/guides/migration/sl-migration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 49977a5076e..9f8a9ac3cfa 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -20,7 +20,7 @@ dbt Labs recommends completing these steps in a local dev environment instead of
 1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]`. For example: 
 
     ```bash
-    pip install "dbt-metricflow[snowflake]
+    pip install "dbt-metricflow[snowflake]"
     ```
     The MetricFlow CLI is not available in the IDE at this time. Support for it is coming soon. 
 

From 101965be0465fa316b15d96f67f299357575de0b Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 09:09:01 +0100
Subject: [PATCH 550/703] Add files via upload

---
 .../using-dbt-cloud/prod-settings.jpg          | Bin 0 -> 140124 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/prod-settings.jpg

diff --git a/website/static/img/docs/dbt-cloud/using-dbt-cloud/prod-settings.jpg b/website/static/img/docs/dbt-cloud/using-dbt-cloud/prod-settings.jpg
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literal 0
HcmV?d00001


From 2809854ae14eabf10f437dd7ab3f2d1b2d98b594 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 09:13:15 +0100
Subject: [PATCH 551/703] Update deploy-environments.md

fix image link
---
 website/docs/docs/deploy/deploy-environments.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md
index e2c4758a29b..9421d664739 100644
--- a/website/docs/docs/deploy/deploy-environments.md
+++ b/website/docs/docs/deploy/deploy-environments.md
@@ -34,7 +34,7 @@ import ExpBeta from '/snippets/_explorer-beta-banner.md';
 
 <ExpBeta/>
 
-<img width="764" alt="image" src="https://github.com/dbt-labs/docs.getdbt.com/assets/38054082/39f63ab8-6b0d-4444-a659-3f34543aa96e">
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/prod-settings.jpg" width="70%" title="Set your production environment as the default environment in your Environment Settings"/>
 
 In dbt Cloud, each project can have one designated deployment environment, which serves as its production environment. This production environment is _essential_ for using features like dbt Explorer and cross-project references. It acts as the source of truth for the project's production state in dbt Cloud.
 

From 4e2dc83457c2efdb0dcf5c9833279bfe611c48b3 Mon Sep 17 00:00:00 2001
From: Benoit Perigaud <8754100+b-per@users.noreply.github.com>
Date: Mon, 7 Aug 2023 10:39:37 +0200
Subject: [PATCH 552/703] Typo in code example

---
 website/docs/docs/build/cumulative-metrics.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index aebe3fd7ecc..9b1434f7f52 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -19,9 +19,9 @@ metrics:
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
       measure: the measure you are referencing # Required
-      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Cannot be used with window
+      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Cannot be used with grain_to_date
       grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next. 
-      # Optional. Can not be used with grain_to_date. 
+      # Optional. Can not be used with window. 
 
 
 ```

From 66d512e398df9dd8a9b936e208d3d5ecaeafb21c Mon Sep 17 00:00:00 2001
From: Benoit Perigaud <8754100+b-per@users.noreply.github.com>
Date: Mon, 7 Aug 2023 10:43:55 +0200
Subject: [PATCH 553/703] Align allowed aggregations

---
 website/docs/docs/build/measures.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index 13758fa094a..58b28b3d0f4 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -12,7 +12,7 @@ Measures are aggregations performed on columns in your model. They can be used a
 | --- | --- | --- |
 | [`name`](#name) | Provide a name for the measure, which must be unique and can't be repeated across all semantic models in your dbt project. | Required |
 | [`description`](#description) | Describes the calculated measure. | Optional |
-| [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `max`, `min`, `count_distinct`, and `sum_boolean`. | Required |
+| [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `min`, `max`, `average`, `sum_boolean`, `count_distinct`, `median`, and `percentile`. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
 | `agg_params` | specific aggregation properties such as a percentile. | Optional |

From 44da122d29d564d79f7f47ee74abeadf93e40429 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 09:51:42 +0100
Subject: [PATCH 554/703] Update website/docs/docs/build/measures.md

---
 website/docs/docs/build/measures.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index 58b28b3d0f4..a80ff9f5e3e 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -12,7 +12,7 @@ Measures are aggregations performed on columns in your model. They can be used a
 | --- | --- | --- |
 | [`name`](#name) | Provide a name for the measure, which must be unique and can't be repeated across all semantic models in your dbt project. | Required |
 | [`description`](#description) | Describes the calculated measure. | Optional |
-| [`agg`](#aggregation) | dbt supports the following aggregations: `sum`, `min`, `max`, `average`, `sum_boolean`, `count_distinct`, `median`, and `percentile`. | Required |
+| [`agg`](#aggregation) | dbt supports aggregations such as `sum`, `min`, `max`, and more. Refer to [Aggregation](/docs/build/measures#aggregation) for the full list of supported aggregation types. | Required |
 | [`expr`](#expr) | You can either reference an existing column in the table or use a SQL expression to create or derive a new one. | Optional |
 | [`non_additive_dimension`](#non-additive-dimensions) | Non-additive dimensions can be specified for measures that cannot be aggregated over certain dimensions, such as bank account balances, to avoid producing incorrect results. | Optional |
 | `agg_params` | specific aggregation properties such as a percentile. | Optional |

From 161b276cf81472fe4f6f5c989c8aef2adc82cf73 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 11:59:58 +0100
Subject: [PATCH 555/703] Update build-metrics-intro.md

add clarification that postgres supported in metricflow but not dbt cloud yet per [slack thread](https://getdbt.slack.com/archives/C046L0VTVR6/p1691092050990009?thread_ts=1691089665.187629&cid=C046L0VTVR6)

- Use MetricFlow with Snowflake, BigQuery, Databricks, Postgres (CLI only), or Redshift. (dbt Cloud Postgres support coming soon)
---
 website/docs/docs/build/build-metrics-intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/build-metrics-intro.md b/website/docs/docs/build/build-metrics-intro.md
index ed10a874a3c..232b3f83ee0 100644
--- a/website/docs/docs/build/build-metrics-intro.md
+++ b/website/docs/docs/build/build-metrics-intro.md
@@ -21,7 +21,7 @@ Before you start, consider the following guidelines:
 
 - Define metrics in YAML and query them using these [new metric specifications](https://github.com/dbt-labs/dbt-core/discussions/7456).
 - You must be on dbt v1.6 or higher to use MetricFlow. [Upgrade your dbt Cloud version](/docs/dbt-versions/upgrade-core-in-cloud) to get started.
-- Use MetricFlow with Snowflake, Postgres, BigQuery, Databricks, or Redshift.
+- Use MetricFlow with Snowflake, BigQuery, Databricks, Postgres (CLI only), or Redshift. (dbt Cloud Postgres support coming soon)
 - Unlock insights and query your metrics using the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and its diverse range of [available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
 

From 17f4408f0311f70e09bfece56dc13e1fa1439604 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:00:39 +0100
Subject: [PATCH 556/703] Update quickstart-sl.md

---
 website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index 8dbcc6c46f3..f70433cd10a 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -23,7 +23,7 @@ import TestQuery from '/snippets/_sl-test-and-query-metrics.md';
 
 The dbt Semantic Layer, powered by [MetricFlow](/docs/build/about-metricflow), simplifies defining and using critical business metrics. It centralizes metric definitions, eliminates duplicate coding, and ensures consistent self-service access to metrics in downstream tools. 
 
-MetricFlow is a powerful component within the dbt Semantic Layer that helps users define and manage company metrics efficiently. It provides flexible abstractions and SQL query generation, and also allows data consumers to retrieve metric datasets quickly and easily from a data platform.
+MetricFlow is a powerful component within the dbt Semantic Layer that helps users define and manage company metrics efficiently. It provides flexible abstractions and SQL query generation and also allows data consumers to retrieve metric datasets quickly and easily from a data platform.
 
 Use this guide to fully experience the power of a universal dbt Semantic Layer. Here are the following steps you'll take:
 

From c263c9155e53c826217c89e19ff86811e49bfcb8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:01:48 +0100
Subject: [PATCH 557/703] Update _v2-sl-prerequisites.md

---
 website/snippets/_v2-sl-prerequisites.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 95d563c9359..cce4555af87 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -3,11 +3,11 @@
 
 To use the Semantic Layer, you must:
 
-- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America
-- Have both your production and development environments running dbt version 1.6 or higher 
-- Use Snowflake, BigQuery, Databricks, and Redshift data platform
+- Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
+- Have both your production and development environments running dbt version 1.6 or higher.
+- Use Snowflake, BigQuery, Databricks, and Redshift.
 -  Create a successful run in the environment where you configure the Semantic Layer. <br/>
-  **Note:** Semantic Layer currently supports Deployment environment. (_development experience coming soon_)
+  **Note:** Semantic Layer currently supports the Deployment environment. (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.
 - Set up the [Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) in the integrated tool to import metric definitions. <br /> 
 **Note:** Developer accounts can only query data manually using the [MetricFlow CLI](/docs/build/metricflow-cli) and SQL. To dynamically query metrics using external tools, you must have a dbt Cloud Team or Enterprise account with access to the Semantic Layer API.<br />

From 090ae602666d4c46ab3938b4640370c6e884fad8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:02:56 +0100
Subject: [PATCH 558/703] Update sl-getting-started.md

---
 website/docs/docs/build/sl-getting-started.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index 7259a7eb22c..bfe8eb00f4d 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -29,7 +29,7 @@ To fully experience the power of a universal dbt Semantic Layer, take the follow
 
 - Have an understanding of key concepts in [MetricFlow](/docs/build/about-metricflow), which powers the revamped dbt Semantic Layer.
 - Have both your production and development environments running dbt version 1.6 or higher. Refer to [upgrade in dbt Cloud](/docs/dbt-versions/upgrade-core-in-cloud) for more info.
-- Use Snowflake, BigQuery, Databricks, or Redshift data platform
+- Use MetricFlow with Snowflake, BigQuery, Databricks, Postgres (CLI only), or Redshift. (dbt Cloud Postgres support coming soon)
 - A successful run in the environment where your Semantic Layer is configured
 - To query with dbt Cloud:
   * Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America (Other regions coming soon)

From 12d8824d43f2bb2beed449ff1fae0dec003deb19 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:04:09 +0100
Subject: [PATCH 559/703] Update website/snippets/_v2-sl-prerequisites.md

---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index cce4555af87..28c82e6f913 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -5,7 +5,7 @@ To use the Semantic Layer, you must:
 
 - Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
 - Have both your production and development environments running dbt version 1.6 or higher.
-- Use Snowflake, BigQuery, Databricks, and Redshift.
+- Use Snowflake, BigQuery, Databricks, or Redshift. (dbt Cloud Postgres support coming soon).
 -  Create a successful run in the environment where you configure the Semantic Layer. <br/>
   **Note:** Semantic Layer currently supports the Deployment environment. (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.

From 2d8d40162cc5b89e39b914de877ebbfb16e791d6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:04:32 +0100
Subject: [PATCH 560/703] Update website/snippets/_v2-sl-prerequisites.md

---
 website/snippets/_v2-sl-prerequisites.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_v2-sl-prerequisites.md b/website/snippets/_v2-sl-prerequisites.md
index 28c82e6f913..8e7075db257 100644
--- a/website/snippets/_v2-sl-prerequisites.md
+++ b/website/snippets/_v2-sl-prerequisites.md
@@ -5,7 +5,7 @@ To use the Semantic Layer, you must:
 
 - Have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America.
 - Have both your production and development environments running dbt version 1.6 or higher.
-- Use Snowflake, BigQuery, Databricks, or Redshift. (dbt Cloud Postgres support coming soon).
+- Use Snowflake, BigQuery, Databricks, or Redshift (dbt Cloud Postgres support coming soon).
 -  Create a successful run in the environment where you configure the Semantic Layer. <br/>
   **Note:** Semantic Layer currently supports the Deployment environment. (_development experience coming soon_)
 - Install the [MetricFlow CLI](/docs/build/metricflow-cli). After installing the package, make sure you run at least one model.

From 36b1140b2d87f036c986919ac1ce9989e64632df Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:20:47 +0100
Subject: [PATCH 561/703] Update metrics-overview.md

update to required and fix typo
---
 website/docs/docs/build/metrics-overview.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 5607fcbab99..fd84c96e0ec 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -13,9 +13,9 @@ The keys for metrics definitions are:
 | Component | Description | Type |
 | --------- | ----------- | ---- |
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
-| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Optional |
+| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Required |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
-| `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
+| `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity, or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |
 
 
@@ -38,7 +38,7 @@ metrics:
 This page explains the different supported metric types you can add to your dbt project. 
 <!--
 - [Cumulative](#cumulative-metrics) — Cumulative metrics aggregate a measure over a given window.
-- [Derived](#derived-metrics) — An expression of other metrics, which allows you to do calculation on top of metrics.
+- [Derived](#derived-metrics) — An expression of other metrics, which allows you to do calculations on top of metrics.
 - [Expression](#expression-metrics) — Allow measures to be modified using a SQL expression.
 - [Measure proxy](#measure-proxy-metrics) — Metrics that refer directly to one measure.
 - [Ratio](#ratio-metrics) — Create a ratio out of two measures. 
@@ -148,7 +148,8 @@ metrics:
 ```
 
 ## Filters
-Filter are configured using jinja templating. Use the following syntax to refrence entites, dimensions and time dimensions in filters:
+
+A filter is configured using Jinja templating. Use the following syntax to reference entities, dimensions, and time dimensions in filters:
 ```yaml
 filter: |
   {{ Entity('entity_name') }} 
@@ -163,7 +164,7 @@ You can set more metadata for your metrics, which can be used by other tools lat
 
 - **Description** &mdash;  Write a detailed description of the metric.
 
-<!--Provide a detailed description of the metric. This description is surfaced in the main “definition” section of the metric page using rich Markdown formatting in the Transform UI. [this includes transform and not sure how this looks in core and cloud]-->
+<!--Provide a detailed description of the metric. This description surfaced in the main “definition” section of the metric page using rich Markdown formatting in the Transform UI. [this includes transform and not sure how this looks in core and cloud]-->
 
 
 ## Related docs

From 278987ace0feebbb96f3f8d5f12fe823c84ed3d7 Mon Sep 17 00:00:00 2001
From: Sudarsan Balaji <hello@artful.dev>
Date: Mon, 7 Aug 2023 14:24:02 +0100
Subject: [PATCH 562/703] Fix typo

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 18e4da73225..9972ab6f8b4 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -143,6 +143,6 @@ We hope to address the following limitations in a future release.
 
 Swapping a materialized view to a table or view is not supported. You must manually drop the existing materialized view in the data warehouse before calling `dbt run` again.
 
-For example, assume that a view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="table"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCASE` on the data warehouse before trying the model again.
+For example, assume that a view, `my_mv.sql`, has already been materialized to the underlying data platform via `dbt run`. If a user then changes the model's config to be `materialized="table"`, they will get an error. The workaround is to execute `DROP MATERIALIZE VIEW my_mv CASCADE` on the data warehouse before trying the model again.
 
 </VersionBlock>

From 0df86d2fb81cd91d7898874976bc6e003e0f3109 Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Mon, 7 Aug 2023 10:21:54 -0500
Subject: [PATCH 563/703] Un nest filter for simple metrics

---
 website/docs/docs/build/metrics-overview.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 5607fcbab99..a1be4969537 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -142,9 +142,9 @@ metrics:
   - name: cancellations
     type: simple
     type_params:
-      measure: cancellations_usd  # Specify the measure you are creating a proxy for. 
-      filter: |
-        {{ Dimension('order__value')}} > 100 and {{Dimension('user__acquisition')}}
+      measure: cancellations_usd  # Specify the measure you are creating a proxy for.
+    filter: |
+      {{ Dimension('order__value')}} > 100 and {{Dimension('user__acquisition')}}
 ```
 
 ## Filters

From bb14288aca21dbe289a954df24d6cbf0399b99eb Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 7 Aug 2023 17:23:48 +0100
Subject: [PATCH 564/703] Update semantic-layer-3-build-semantic-models.md

opening to fix some typos and suggestions.
---
 .../semantic-layer-3-build-semantic-models.md | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
index 549dbccf8dd..ba4d0cab726 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
@@ -43,7 +43,7 @@ semantic_models:
 semantic_models:
   - name: orders
     description: |
-      Model containting order data. The grain of the table is the order id.
+      Model containing order data. The grain of the table is the order id.
     model: ref('stg_orders')
     entities:
       ...
@@ -117,13 +117,13 @@ semantic_models:
 - 🧮 Dimensions are the columns that we want to **filter and group by**, **the adjectives of our project**. They come in three types:
   - **categorical**
   - **time**
-  - slowly changing dimensions — [these are covered in the documentation](https://docs.getdbt.com/docs/build/dimensions#scd-type-ii), and a little more complex. To focus on building your mental models of MetricFlow's fundamentals, we won't be using SCDs this guide.
+  - slowly changing dimensions — [these are covered in the documentation](https://docs.getdbt.com/docs/build/dimensions#scd-type-ii), and a little more complex. To focus on building your mental models of MetricFlow's fundamentals, we won't be using SCDs in this guide.
 - ➕ We're **not limited to existing columns**, we can use the `expr` property to add simple computations in our dimensions.
 - 📛 Categorical dimensions are the simplest, they simply require a `name` and `type` (type being categorical). **If the `name` property matches the name of the dimension column**, that's it, you're done. If you want or need to use a `name` other than the column name, or do some filtering or computation, **you can supply an optional `expr` property** to evaluate for the dimension.
 
 ### Dimensions in action
 
-- 👀 Lets look at our staging model again and see what fields we have available.
+- 👀 Let's look at our staging model again and see what fields we have available.
 
 ```SQL
 select
@@ -145,7 +145,7 @@ from source
 
 - ⏰ For now the only dimension to add is a **time dimension**.
 - 🕰️ At least one **primary time dimension** is **required** for any semantic models that **have measures**.
-- 1️⃣ We denote this with the `is_primary` property, or if there is only one time dimension supplied it is primary by default. Below we only have `ordered_at` as a timestamp so we don't need to specify anything except the maximum granularity we're bucketing to (in this case, day).
+- 1️⃣ We denote this with the `is_primary` property, or if there is only a one-time dimension supplied it is primary by default. Below we only have `ordered_at` as a timestamp so we don't need to specify anything except the maximum granularity we're bucketing to (in this case, day).
 
 ```YAML
 dimensions:
@@ -161,7 +161,7 @@ dimensions:
 We'll discuss an alternate situation, dimensional tables that have static numeric values like supply costs or tax rates but no time dimensions, later in the Guide.
 :::
 
-- 🔢 We can also **make a dimension out of numeric column** that would be typically be a measures.
+- 🔢 We can also **make a dimension out of a numeric column** that would typically be a measure.
 - 🪣 Using `expr` we can **create buckets of values that we label** for our dimension. We'll add one of these in for labeling 'large orders' as any order totals over $50.
 
 ```YAML
@@ -185,7 +185,7 @@ dimensions:
 
 ### Measures in action
 
-- 👀 Lets look at **our staging model** one last time and see what **fields we want to measure**.
+- 👀 Let's look at **our staging model** one last time and see what **fields we want to measure**.
 
 ```SQL
 select
@@ -213,7 +213,7 @@ from source
 ```YAML
 measures:
   - name: order_total
-    description: The total amount for each order inlcuding taxes.
+    description: The total amount for each order including taxes.
     agg: sum
   - name: tax_paid
     description: The total tax paid on each order.
@@ -275,17 +275,17 @@ semantic_models:
         agg: sum
 ```
 
-- 🦺 We can check that it's valid configuration and works with the real data our dbt project is generating by using the `mf validate-configs` command. This will:
+- 🦺 We can check that it's a valid configuration and works with the real data our dbt project is generating by using the `mf validate-configs` command. This will:
   1. **Parse the semantic manifest** our configuration describes out of the dbt project.
   2. Validate the **internal semantics** of the manifest as described by our code.
   3. Validate the **external semantics** of the manifest against your data warehouse (e.g. making sure that a column specified as a dimension exists on the proper table)
 
 ## Review and next steps
 
-Let's review what the basics of semantic models, they:
+Let's review the basics of semantic models:
 
 - 🧱 Consist off **entities, dimensions, and measures**.
 - 🫂 Describe the **semantics and relationships of objects** in the warehouse.
 - 1️⃣ Correspond to a **single logical model** in your dbt project.
 
-Next up, lets use our new semantic model to **build a metric**!
+Next up, let's use our new semantic model to **build a metric**!

From 194b04e30e9e20325935433f6b1b76534101f858 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 7 Aug 2023 12:50:14 -0400
Subject: [PATCH 565/703] Adding Databricks to MV

---
 website/docs/guides/migration/versions/01-upgrading-to-v1.6.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index 22111828e24..ea231ce7f9a 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -47,8 +47,9 @@ Supported on:
 - [Postgres](/reference/resource-configs/postgres-configs#materialized-view)
 - [Redshift](/reference/resource-configs/redshift-configs#materialized-view)
 - Snowflake (docs forthcoming)
+- Databricks (docs forthcoming)
 
-Support for BigQuery and Databricks forthcoming.
+Support for BigQuery coming soon.
 
 ### New commands for mature deployment
 

From 471f346bb7091f9d13ce099ba9d72c09f4405b63 Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Tue, 8 Aug 2023 08:20:22 +1200
Subject: [PATCH 566/703] Create new guide for updated CI (#3722)

## What are you changing in this pull request and why?
Working with Julia to update our guidance based on the new CI
functionality.

This will also obsolete [this
guide](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview),
and I've move most of the CI content in [this
guide](https://docs.getdbt.com/guides/orchestration/custom-cicd-pipelines/1-cicd-background)
out so that the latter becomes a CD-only guide.

Preview link:
-
https://deploy-preview-3722--docs-getdbt-com.netlify.app/guides/orchestration/set-up-ci/overview

Showing a redirect working:
-
https://deploy-preview-3722--docs-getdbt-com.netlify.app/guides/best-practices/environment-setup/1-env-guide-overview

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.

Adding new pages (delete if not applicable):
- [x] Add page to `website/sidebars.js`
- [x] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [x] Remove page from `website/sidebars.js`
- [x] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page
---
 .../environment-setup/1-env-guide-overview.md | 67 ----------------
 .../2-one-deployment-environment.md           | 61 ---------------
 .../3-many-deployment-environments.md         | 77 -------------------
 .../1-cicd-background.md                      |  4 +-
 .../orchestration/set-up-ci/1-introduction.md | 10 +++
 .../orchestration/set-up-ci/2-quick-setup.md  | 58 ++++++++++++++
 .../set-up-ci/3-run-dbt-project-evaluator.md  | 46 +++++++++++
 .../4-lint-on-push.md}                        | 43 +++++------
 .../set-up-ci/5-multiple-checks.md            | 69 +++++++++++++++++
 website/sidebars.js                           | 33 ++++----
 website/static/_redirects                     |  4 +
 11 files changed, 226 insertions(+), 246 deletions(-)
 delete mode 100644 website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
 delete mode 100644 website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
 delete mode 100644 website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
 create mode 100644 website/docs/guides/orchestration/set-up-ci/1-introduction.md
 create mode 100644 website/docs/guides/orchestration/set-up-ci/2-quick-setup.md
 create mode 100644 website/docs/guides/orchestration/set-up-ci/3-run-dbt-project-evaluator.md
 rename website/docs/guides/orchestration/{custom-cicd-pipelines/2-lint-on-push.md => set-up-ci/4-lint-on-push.md} (55%)
 create mode 100644 website/docs/guides/orchestration/set-up-ci/5-multiple-checks.md

diff --git a/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md b/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
deleted file mode 100644
index 8c1d79877ae..00000000000
--- a/website/docs/guides/best-practices/environment-setup/1-env-guide-overview.md
+++ /dev/null
@@ -1,67 +0,0 @@
----
-title: "dbt Cloud environment best practices"
-id: 1-env-guide-overview
-description: Learn how to configure environments in dbt Cloud.
-displayText: "dbt Cloud environment best practices"
-hoverSnippet: Learn how to configure environments in dbt Cloud.
----
-
-> *How do I manage environments in my dbt Cloud project? How many do I need?*
->
-> *How does my <Term id="data-warehouse" /> structure map to environments in dbt Cloud?*
->
-> *What do git branches have to do with my dbt Cloud environments?*
->
-
-If these questions keep you up at night, you’ve come to the right place! When it comes to managing your dbt Cloud environments, there is not a one-size-fits-all solution for all teams. In this guide we’ll walk you through a few environment architecture options for dbt Cloud that we’d recommend, and hopefully you find an option that works for you.
-
-## Learning goals
-
-This guide has three main goals:
-
-- Provide our recommendations on managing dbt Cloud environments
-- Illustrate these recommendations with comprehensive examples
-- At each stage, explain *why* we recommend the approach that we do, so that you're equipped to decide when and where to deviate from these recommendations to better fit your organization’s unique needs
-
-:::info
-☁️ This guide focuses on architecture for **dbt Cloud**. However, similar principles apply for developers using dbt Core. Before diving into this guide we recommend taking a look at our **[dbt Cloud environments](/docs/dbt-cloud-environments)** page for more context.
-
-:::
-
-### How many environments do I really need?
-
-Environments define the way that dbt will execute your code, including:
-
-- The **version of dbt** that will run.
-- The **version of your code** to be executed.
-- The **connection information** for your warehouse.
-- In dbt Cloud, there are **two types of environments:**
-  - **Development** — the environment settings in which you work in the IDE on a development branch.
-  - **Deployment** — the environment settings in which a dbt Cloud job runs.
-
-In this guide, we’re going to focus on **deployment environments**, which determine how your project is executed when a **dbt Cloud job executes**. When using both approaches, make sure to designate one environment as "Production." This will allow you to use features such as dbt Explorer and cross-project references. Refer to [Set product environment](/docs/deploy/deploy-environments#set-as-production-environment-beta) for details.
-
-Depending on your git workflow and testing strategy, you'll be choosing between one deployment environment or many deployment environments. We provide a high-level overview of how these two deployment strategies work here, but use each section of this guide to get a deep-dive into how these setups differ. 
-
-| Setup option | Works well if you | Relative complexity level  |
-| --- | --- | --- |
-| One deployment environment | - only scheduled runs for one set of data objects <br /> - development branches are merged directly to main | Low |
-| Many deployment environments | - feature branches move through several promotion stages | High |
-
-### TL;DR — One deployment environment
-
-We usually recommended folks start with the basics; having one deployment environment is usually the simplest and most maintainable approach to start. This approach works well if:
-
-- You only need to have **scheduled jobs running in a single environment** within your data warehouse.
-- You use a **single primary branch** and follow a direct promotion (**Dev —> Prod**) strategy
-
-With this option, your production jobs and your [CI jobs](/docs/deploy/continuous-integration) that ensure code integrity are managed within one single deployment environment.
-
-### TL;DR — Many deployment environments
-This approach adds a bit more complexity and may slow down the development process, but adds a layer of security that can be worth the tradeoff. This approach works well if:
-
-- Your organization maintains **several long-lived git branches** to control how and when changes are tested and promoted to production.
-  - Some orgs follow a **Dev —> QA —>  Prod release cycle** — if that sounds like your org, this approach is probably right for you.
-- The **output of your dbt project is an input to other systems** and you need to test and validate many changes on a stable, long-lived staging dataset in a pre-production environment.
-
-The two options are explored in more detail in the following sections, including the benefits, trade-offs, the steps required to implement the setup in dbt Cloud.
diff --git a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md b/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
deleted file mode 100644
index 5b6c3b742e3..00000000000
--- a/website/docs/guides/best-practices/environment-setup/2-one-deployment-environment.md
+++ /dev/null
@@ -1,61 +0,0 @@
----
-title: "One deployment environment"
-id: 2-one-deployment-environment
-description: Learn how to configure a single deployment environment setup in dbt Cloud.
-displayText: "dbt Cloud environment best practices"
-hoverSnippet: Learn how to configure a single deployment environment setup in dbt Cloud.
----
-import ExpNote from '/snippets/_explorer-beta-note.md';
-
-
-## What this looks like
-
-1. You have a **single *development* environment** where dbt users can access the dbt Cloud IDE and make changes to their code on feature branches created off of your default branch in your repository (most often the `main` branch).
-2. You have a **single *deployment* environment** (let’s call it “Production”) where your scheduled jobs run referencing the `main` branch. <br />
- 
- <ExpNote/>
-
-3. You also have a [**CI job**](/docs/deploy/continuous-integration) that kicks off anytime you open a PR to merge a feature branch into `main`. This CI job can run in your dbt “Production” environment.
-
-:::info
-
-☁️ CI jobs run in a dedicated custom schema for each PR, so there will no collision with your production schemas.
-
-:::
-
-<Lightbox src="/img/guides/best-practices/environment-setup/one-deployment-table.png" title="Table of basic setup for one deployment environment" />
-
-### Git workflow
-
-<Lightbox src="/img/guides/best-practices/environment-setup/one-branch-git.png" title="git flow diagram for one deployment environment" />
-
-
-1. In the dbt Cloud IDE, developers work on feature branches, created from the `main` branch (`feature_a`, `feature_b`, `feature_c` above)
-2. When code is ready, developer opens a PR to merge feature branch into `main`
-3. [**CI Job**](/docs/deploy/continuous-integration) automatically kicks off, and tests the changes made in the PR
-4. When CI Job is successful and team is ready to deploy changes to Production, the PR is merged directly into the `main` branch. The next time a production job runs, these changes will be incorporated and executed.
-
-### dbt Cloud setup 
-
-1. Create your [**development environment**](/docs/dbt-cloud-environments) to power the dbt Cloud IDE. No extra customization needed!
-2. Create your **[production deployment environment](/docs/deploy/deploy-environments)**.
-3. Define your **dbt Cloud jobs** in the production deployment environment from step 2.
-    1. **Production job(s)**: You will need to set up **at least one scheduled job** that deploys your project to your production databases/schemas. You may create multiple jobs based on your business SLAs.
-    2. **CI Job**: Unlike the production jobs, which are triggered via the scheduler, this job will be triggered when PRs are opened in your repository. Refer to [CI jobs](/docs/deploy/ci-jobs) for details.
-
-
-### When this works well
-
-This approach is recommended for most use cases because it enables you to quickly and safely implement code changes in the production environment. It also gives developers the confidence to trust and rely on these changes. With this option, multiple developers can easily contribute to and collaborate on the same codebase with confidence.
-
-:::info
-💡 Check out [Sunrun's Coalesce 2022 talk](https://www.youtube.com/watch?v=vmBAO2XN-fM) on Automating CI/CD in dbt Cloud, where they simplified their CI/CD process from several long-lived branches to a single long-lived main branch with feature branches.
-
-:::
-
-### When this doesn’t work so well
-
-- You have a **formal QA process** before merging code into production.
-- You want to **control when features are released** to production.
-- You need to have scheduled **jobs running in many environments** due to dependencies on outside systems.
-  - e.g. Your organization has many applications that consume and test data changes in a lower non-Production environment before changes should be promoted to Production.
diff --git a/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md b/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
deleted file mode 100644
index 2ac46f811b1..00000000000
--- a/website/docs/guides/best-practices/environment-setup/3-many-deployment-environments.md
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: "Many deployment environments"
-id: 3-many-deployment-environments
-description: Learn how to configure a many deployment environment setup in dbt Cloud.
-displayText: "dbt Cloud environment best practices"
-hoverSnippet: Learn how to configure a many deployment environment setup in dbt Cloud.
----
-import ExpNote from '/snippets/_explorer-beta-note.md';
-
-## What this looks like
-
-1. You have a **single *development* environment** where dbt users can access the dbt Cloud IDE and make changes to their code. However, you’ll want to update the **[custom branch settings](faqs/Environments/custom-branch-settings)** to ensure that developers create feature branches off of the a non-production branch. For this example, we’ll refer to this as the `qa` branch.
-2. You have a **QA deployment environment**, running scheduled jobs from the `qa` branch that deploys your dbt project to a pre-production warehouse location.
-3. You have a **Production deployment environment,** running scheduled jobs from the `main` branch that deploys your dbt project to your production warehouse location. <br />
-
- <ExpNote/>
- 
-4. You have **multiple CI jobs** (one in each deployment environment) to ensure changes to each branch are tested.  
-
-<Lightbox src="/img/guides/best-practices/environment-setup/many-deployments-table.png" title="Table of basic setup for many deployment environment" />
-
-### Git workflow
-
-<Lightbox src="/img/guides/best-practices/environment-setup/many-branch-git.png" title="git flow diagram for many deployment environments" />
-
-1. In the dbt Cloud IDE, developers work on feature branches, **created from the `qa` branch** (`feature_a`, `feature_b`, `feature_c` above).
-2. When code is ready, developer opens a PR to merge feature branch into `qa`.
-3. The **first CI Job** automatically kicks off to test the changes introduced in the PR. This job will *defer to a regularly-scheduled job in the QA environment* and run in the QA deployment environment.
-4. When **CI Job is successful** and team is ready to deploy changes, the **PR is merged into `qa`.**
-5. Scheduled jobs run in the QA deployment environment, running on `qa` branch to ensure the new changes work as intended.
-6. When **all feature branches** for a given release (e.g. sprint) have been **successfully merged** to `qa` and are **running without error** in the QA deployment environment, a team member opens a **PR to merge `qa` → `main`.**
-7. The **second CI Job** automatically kicks off to test changes in PR. This job will *defer to a regularly-scheduled job in the Production environment* and run in the Production deployment environment.
-8. When **second CI Job** is successful and team is ready to deploy changes, the **PR is merged into `main`**.
-9. Monitor scheduled jobs in the Production deployment environment that are running on `main` branch. Voila! All changes are released and ready for your stakeholders.
-
-:::info
-💡 Considering a different branching strategy that involves cherry picking? [Maybe reconsider!](https://docs.getdbt.com/blog/the-case-against-git-cherry-picking)
-
-:::
-
-### dbt Cloud setup
-
-1. Create your [**development environment**](/docs/dbt-cloud-environments) to power the dbt Cloud IDE.
-
-    Here, we’ll set a **custom branch** so that users in the IDE create their feature branches from `qa` instead of `main`. Click **Only run on a custom branch** in **General settings**, enter `qa` into **Custom Branch.**
-
-2. Set up your **QA [deployment environment](/docs/deploy/deploy-environments)**
-
-    Here, we’ll apply the same custom branch settings as the development environment in Step 1. All scheduled jobs in the QA deployment environment will use the code from the `qa` branch during execution.
-
-3. **Define QA jobs**
-    1. **QA job(s)**: You’ll want to create at least one scheduled job, running on a roughly daily cadence. This will allow us to make sure all the code executes without error before you release it to production, and will also power the first CI job.
-    2. **CI Job**: As above, this job will be triggered when PRs are opened in your repository. Enable this option by selecting **Run on Pull Requests?** under the **Continuous Integration(CI)** tab under the **Triggers** section. Since we’re using the custom branch setting in the QA environment, you'll also want to be sure to select the second option **Run only on Custom Branch** (selected by default) — this means that only PRs created against the `qa` branch will trigger this job, rather than any PR at all.
-
-        This job will also need to defer to one of the QA jobs created in step 3a. This enables the use of the `state` modifier in your selection syntax to only run changes introduced by your PR.
-
-4. Set up your **Production [deployment environment](/docs/deploy/deploy-environments)**
-
-    Here, we’ll *also* use the same custom branch settings as the other environments, but set the custom branch as `main`. Even thought the `main` branch is the default, setting this value enables us to properly set up the CI Job in the next step.
-
-5. **Define production jobs**
-    1. **Production job(s)**: You will need to set up at least one scheduled job that deploys your project to your production databases/schemas. You may create multiple jobs based on your business SLAs.
-    2. **Production CI Job**: As above, this job will be triggered when PRs are opened in your repository. Enable this option by selecting **Run on Pull Requests?** under the **Continuous Integration(CI)** tab under the **Triggers** section. Since we’re using the custom branch setting in the QA environment, we’ll also want to select the second option **Run only on Custom Branch** — this means that only PRs created against the `main` branch will trigger this job, rather than any PR at all.
-
-        This job will also need to defer to one of the QA jobs created in step 5a. This enables the use of the `state` modifier in your selection syntax to only run changes introduced by your PR.
-
-### When this works well
-
-This approach works well when it’s critical to **apply user acceptance and integration testing to your project in a pre-production environment**. This approach allows you to have scheduled jobs running in **many environments** on your data warehouse.
-
-### When this doesn’t work so well
-
-This approach may slow down the time it takes to get new feature into production, since it requires additional steps in the deployment process and additional branches to maintain. Keep in mind that adding complexity to your deployment process might cause some slowdown in your release cycle.
-
-## Conclusion
-
-While there’s no single correct answer to how to setup your dbt Cloud environments, they are flexible enough to enable just about any code promotion workflow your organization uses. We would love to hear how you’ve set up your deployment infrastructure in dbt Cloud!
diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/1-cicd-background.md b/website/docs/guides/orchestration/custom-cicd-pipelines/1-cicd-background.md
index 048fe637de0..a66259c6c49 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/1-cicd-background.md
+++ b/website/docs/guides/orchestration/custom-cicd-pipelines/1-cicd-background.md
@@ -1,10 +1,8 @@
 ---
-title: Customizing CI/CD
+title: Customizing CI/CD with Custom Pipelines
 id: 1-cicd-background
 ---
 
-# Creating Custom CI/CD Pipelines
-
 One of the core tenets of dbt is that analytic code should be version controlled. This provides a ton of benefit to your organization in terms of collaboration, code consistency, stability, and the ability to roll back to a prior version. There’s an additional benefit that is provided with your code hosting platform that is often overlooked or underutilized. Some of you may have experience using dbt Cloud’s [webhook functionality](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration) to run a job when a PR is created. This is a fantastic capability, and meets most use cases for testing your code before merging to production. However, there are circumstances when an organization needs additional functionality, like running workflows on every commit (linting), or running workflows after a merge is complete. In this article, we will show you how to setup custom pipelines to lint your project and trigger a dbt Cloud job via the API.
 
 A note on parlance in this article since each code hosting platform uses different terms for similar concepts. The terms `pull request` (PR) and `merge request` (MR) are used interchangeably to mean the process of merging one branch into another branch.
diff --git a/website/docs/guides/orchestration/set-up-ci/1-introduction.md b/website/docs/guides/orchestration/set-up-ci/1-introduction.md
new file mode 100644
index 00000000000..97df16b4ce1
--- /dev/null
+++ b/website/docs/guides/orchestration/set-up-ci/1-introduction.md
@@ -0,0 +1,10 @@
+---
+title: "Get started with Continuous Integration tests"
+slug: overview
+---
+
+By validating your code _before_ it goes into production, you don't need to spend your afternoon fielding messages from people whose reports are suddenly broken.
+
+A solid CI setup is critical to preventing avoidable downtime and broken trust. dbt Cloud uses **sensible defaults** to get you up and running in a performant and cost-effective way in minimal time.
+
+After that, there's time to get fancy, but let's walk before we run.
diff --git a/website/docs/guides/orchestration/set-up-ci/2-quick-setup.md b/website/docs/guides/orchestration/set-up-ci/2-quick-setup.md
new file mode 100644
index 00000000000..89e04daa8df
--- /dev/null
+++ b/website/docs/guides/orchestration/set-up-ci/2-quick-setup.md
@@ -0,0 +1,58 @@
+---
+title: "Baseline: Enable CI in 15 minutes"
+slug: in-15-minutes
+description: Find issues before they are deployed to production with dbt Cloud's Slim CI.
+---
+
+:::tip Join the beta
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs). This guide assumes you are using the improvements available in the beta.
+
+If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
+
+:::
+
+## Prerequisites
+
+As part of your initial dbt Cloud setup, you should already have Development and Production environments configured. Let's recap what each does:
+
+- Your **Development environment** powers the IDE. Each user has individual credentials, and builds into an individual dev schema. Nothing you do here impacts any of your colleagues.
+- Your **Production environment** brings the canonical version of your project to life for downstream consumers. There is a single set of deployment credentials, and everything is built into your production schema(s).
+
+In this guide, we're going to add a **CI environment**, where proposed changes can be validated in the context of the entire project without impacting production systems. We will use a single set of deployment credentials (like the Prod environment), but models are built in a separate location to avoid impacting others (like the Dev environment).
+
+Your git flow will look like this:
+<Lightbox src="/img/guides/best-practices/environment-setup/one-branch-git.png" title="git flow diagram" />
+
+## Step 1: Create a new CI environment
+
+See [Create a new environment](/docs/dbt-cloud-environments#create-a-deployment-environment). The environment should be called **CI**. Just like your existing Production environment, it will be a Deployment-type environment.
+
+When setting a Schema in the **Deployment Credentials** area, remember that dbt Cloud will automatically generate a custom schema name for each PR to ensure that they don't interfere with your deployed models. This means you can safely set the same Schema name as your Production job.
+
+## Step 2: Double-check your Production environment is identified
+
+Go into your existing Production environment, and ensure that the **Set as Production environment** checkbox is set. It'll make things easier later.
+
+## Step 3: Create a new job in the CI environment
+
+Use the **Continuous Integration Job** template, and call the job **CI Check**.
+
+In the Execution Settings, your command will be preset to `dbt build --select state:modified+`. Let's break this down:
+
+- [`dbt build`](/reference/commands/build) runs all nodes (seeds, models, snapshots, tests) at once in DAG order. If something fails, nodes that depend on it will be skipped.
+- The [`state:modified+` selector](/reference/node-selection/methods#the-state-method) means that only modified nodes and their children will be run ("Slim CI"). In addition to [not wasting time](https://discourse.getdbt.com/t/how-we-sped-up-our-ci-runs-by-10x-using-slim-ci/2603) building and testing nodes that weren't changed in the first place, this significantly reduces compute costs.
+
+To be able to find modified nodes, dbt needs to have something to compare against. dbt Cloud uses the last successful run of any job in your Production environment as its [comparison state](/reference/node-selection/syntax#about-node-selection). As long as you identified your Production environment in Step 2, you won't need to touch this. If you didn't, pick the right environment from the dropdown.
+
+## Step 4: Test your process
+
+That's it! There are other steps you can take to be even more confident in your work, such as [validating your structure follows best practices](/guides/orchestration/set-up-ci/run-dbt-project-evaluator) and [linting your code](/guides/orchestration/set-up-ci/lint-on-push), but this covers the most critical checks.
+
+To test your new flow, create a new branch in the dbt Cloud IDE then add a new file or modify an existing one. Commit it, then create a new Pull Request (not a draft). Within a few seconds, you’ll see a new check appear in your git provider.
+
+## Things to keep in mind
+
+- If you make a new commit while a CI run based on older code is in progress, it will be automatically canceled and replaced with the fresh code.
+- An unlimited number of CI jobs can run at once. If 10 developers all commit code to different PRs at the same time, each person will get their own schema containing their changes. Once each PR is merged, dbt Cloud will drop that schema.
+- CI jobs will never block a production run.
diff --git a/website/docs/guides/orchestration/set-up-ci/3-run-dbt-project-evaluator.md b/website/docs/guides/orchestration/set-up-ci/3-run-dbt-project-evaluator.md
new file mode 100644
index 00000000000..646a9cb42b7
--- /dev/null
+++ b/website/docs/guides/orchestration/set-up-ci/3-run-dbt-project-evaluator.md
@@ -0,0 +1,46 @@
+---
+title: "Enforce best practices with dbt project evaluator"
+slug: run-dbt-project-evaluator
+description: dbt Project Evaluator can be run from inside of your existing dbt Cloud CI job to identify common flaws in projects.
+---
+
+dbt Project Evaluator is a package designed to identify deviations from best practices common to many dbt projects, including modeling, testing, documentation, structure and performance problems. For an introduction to the package, read its [launch blog post](/blog/align-with-dbt-project-evaluator).
+
+## Step 1: Install the package
+
+As with all packages, add a reference to `dbt-labs/dbt_project_evaluator` to your `packages.yml` file. See the [dbt Package Hub](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/) for full installation instructions.
+
+## Step 2: Define test severity with an environment variable
+
+As noted in the [documentation](https://dbt-labs.github.io/dbt-project-evaluator/latest/ci-check/), tests in the package are set to `warn` severity by default.
+
+To have these tests fail in CI, create a new environment called `DBT_PROJECT_EVALUATOR_SEVERITY`. Set the project-wide default to `warn`, and set it to `error` in the CI environment.
+
+In your `dbt_project.yml` file, override the severity configuration:
+
+```yaml
+tests:
+dbt_project_evaluator:
+    +severity: "{{ env_var('DBT_PROJECT_EVALUATOR_SEVERITY', 'warn') }}"
+```
+
+## Step 3: Update your CI commands
+
+Because these tests should only run after the rest of your project has been built, your existing CI command will need to be updated to exclude the dbt_project_evaluator package. You will then add a second step which builds _only_ the package's models and tests.
+
+Update your steps to:
+
+```bash
+dbt build --select state:modified+ --exclude package:dbt_project_evaluator
+dbt build --select package:dbt_project_evaluator
+```
+
+## Step 4: Apply any customizations
+
+Depending on the state of your project when you roll out the evaluator, you may need to skip some tests or allow exceptions for some areas. To do this, refer to the documentation on:
+
+- [disabling tests](https://dbt-labs.github.io/dbt-project-evaluator/latest/customization/customization/)
+- [excluding groups of models from a specific test](https://dbt-labs.github.io/dbt-project-evaluator/latest/customization/exceptions/)
+- [excluding packages or sources/models based on path](https://dbt-labs.github.io/dbt-project-evaluator/latest/customization/excluding-packages-and-paths/)
+
+If you create a seed to exclude groups of models from a specific test, remember to disable the default seed and include `dbt_project_evaluator_exceptions` in your second `dbt build` command above.
diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md b/website/docs/guides/orchestration/set-up-ci/4-lint-on-push.md
similarity index 55%
rename from website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md
rename to website/docs/guides/orchestration/set-up-ci/4-lint-on-push.md
index 465994e4442..1932ffe1019 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/2-lint-on-push.md
+++ b/website/docs/guides/orchestration/set-up-ci/4-lint-on-push.md
@@ -1,11 +1,12 @@
 ---
-title: Lint code on push
-id: 2-lint-on-push
+title: "Run linting checks with SQLFluff"
+slug: lint-on-push
+description: Enforce your organization's SQL style guide with by running SQLFluff in your git workflow whenever new code is pushed.
 ---
 
-This section shows a very basic example of linting a project every time a commit is pushed to the repo. While it is simple, it shows the power of CI and can be expanded on to meet the needs of your organization. 
+By [linting](/docs/cloud/dbt-cloud-ide/lint-format#lint) your project during CI, you can ensure that code styling standards are consistently enforced, without spending human time nitpicking comma placement.
 
-The steps below use [SQLFluff](https://docs.sqlfluff.com/en/stable/) to scan your code and look for linting errors. In the example, it's set to use the `snowflake` dialect, and specifically runs the rules L019, L020, L021, and L022. This is purely for demonstration purposes. You should update this to reflect your code base's [dialect](https://docs.sqlfluff.com/en/stable/dialects.html) and the [rules](https://docs.sqlfluff.com/en/stable/rules.html) you've established for your repo.
+The steps below create an action/pipeline which uses [SQLFluff](https://docs.sqlfluff.com/en/stable/) to scan your code and look for linting errors. If you don't already have SQLFluff rules defined, check out [our recommended config file](/guides/best-practices/how-we-style/2-how-we-style-our-sql).
 
 ### 1. Create a YAML file to define your pipeline
 
@@ -21,8 +22,8 @@ The YAML files defined below are what tell your code hosting platform the steps
 }>
 <TabItem value="github">
 
-In order for GitHub to know that you want to run an action, you need to have a few specific folders in your project. Add a new folder named `.github`, and within that folder add a new one named `workflows`. Your final folder structure will look like this: 
-    
+GitHub Actions are defined in the `.github/workflows` directory. To define the job for your action, add a new file named `lint_on_push.yml` under the `workflows` folder. Your final folder structure will look like this:
+
 ```sql
 my_awesome_project
 ├── .github
@@ -30,16 +31,14 @@ my_awesome_project
 │   │   └── lint_on_push.yml
 ```
 
-To define the job for our action, let’s add a new file named `lint_on_push.yml` under the `workflows` folder. This file is how we tell the GitHub runner what to execute when the job is triggered.
-
-Below I touch on the important pieces for running a dbt Cloud job, but if you want a full run-down of all the components of this YAML file checkout [this GitHub article](https://docs.github.com/en/actions/learn-github-actions/understanding-github-actions#understanding-the-workflow-file) on actions.
-
 **Key pieces:**
 
-- `on:` - this is used to filter when the pipeline is run. In this example we’re running it on every push except for pushes to branches named `main`. For more filters, checkout [GitHub’s docs](https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows).
+- `on:` defines when the pipeline is run. This workflow will run whenever code is pushed to any branch except `main`. For other trigger options, check out [GitHub’s docs](https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows).
 - `runs-on: ubuntu-latest` - this defines the operating system we’re using to run the job
-- `uses:` - remember the virtual servers we covered in the background section? They’re just empty operating systems, so there are two pieces of setup that are needed in order to access the code in your repo, and setup Python correctly on the virtual server. These two actions are called from other repos in GitHub to provide those services. For more information on them, checkout their repos: [actions/checkout](https://github.com/actions/checkout#checkout-v3) and [actions/setup-python](https://github.com/actions/setup-python#setup-python-v3).
-- `run:` - this is how we’re telling the GitHub runner to execute the Python script we defined above.
+- `uses:` - When the Ubuntu server is created, it is completely empty. [`checkout`](https://github.com/actions/checkout#checkout-v3) and [`setup-python`](https://github.com/actions/setup-python#setup-python-v3) are public GitHub Actions which enable the server to access the code in your repo, and set up Python correctly.
+- `run:` - these steps are run at the command line, as though you typed them at a prompt yourself. This will install sqlfluff and lint the project. Be sure to set the correct `--dialect` for your project.
+
+For a full breakdown of the properties in a workflow file, see [Understanding the workflow file](https://docs.github.com/en/actions/learn-github-actions/understanding-github-actions#understanding-the-workflow-file) on GitHub's website.
 
 ```yaml
 name: lint dbt project on push
@@ -50,7 +49,7 @@ on:
       - 'main'
 
 jobs:
-# this job runs SQLFluff with a specific set of rules
+  # this job runs SQLFluff with a specific set of rules
   # note the dialect is set to Snowflake, so make that specific to your setup
   # details on linter rules: https://docs.sqlfluff.com/en/stable/rules.html
   lint_project:
@@ -63,9 +62,9 @@ jobs:
         with:
           python-version: "3.9"
       - name: Install SQLFluff
-        run: "pip install sqlfluff==0.13.1"
+        run: "pip install sqlfluff"
       - name: Lint project
-        run: "sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022"
+        run: "sqlfluff lint models --dialect snowflake"
 
 ```
 
@@ -83,7 +82,7 @@ my_awesome_project
 **Key pieces:**
 
 - `image: python:3.9` - this defines the virtual image we’re using to run the job
-- `rules:` - this is used to filter when the pipeline runs. In this case we’re telling it to run on every push event except when the branch is named `main`. Filters are very powerful to run commands on specific events, and you can find a full list in [GitLab’s documentation](https://docs.gitlab.com/ee/ci/yaml/#rules).
+- `rules:` - defines when the pipeline is run. This workflow will run whenever code is pushed to any branch except `main`. For other rules, refer to [GitLab’s documentation](https://docs.gitlab.com/ee/ci/yaml/#rules).
 - `script:` - this is how we’re telling the GitLab runner to execute the Python script we defined above.
 
 ```yaml
@@ -100,8 +99,8 @@ lint-project:
   rules:
     - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH != 'main'
   script:
-    - pip install sqlfluff==0.13.1
-    - sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022
+    - pip install sqlfluff
+    - sqlfluff lint models --dialect snowflake
 ```
 
 </TabItem>
@@ -118,7 +117,7 @@ my_awesome_project
 **Key pieces:**
 
 - `image: python:3.11.1` - this defines the virtual image we’re using to run the job
-- `'**':` - this is used to filter when the pipeline runs. In this case we’re telling it to run on every push event, and you can see at line 12 we're creating a dummy pipeline for `master`. More information on filtering when a pipeline is run can be found in [Bitbucket's documentation](https://support.atlassian.com/bitbucket-cloud/docs/pipeline-triggers/)
+- `'**':` - this is used to filter when the pipeline runs. In this case we’re telling it to run on every push event, and you can see at line 12 we're creating a dummy pipeline for `main`. More information on filtering when a pipeline is run can be found in [Bitbucket's documentation](https://support.atlassian.com/bitbucket-cloud/docs/pipeline-triggers/)
 - `script:` - this is how we’re telling the Bitbucket runner to execute the Python script we defined above.
 
 ```yaml
@@ -134,7 +133,7 @@ pipelines:
             - pip install sqlfluff==0.13.1
             - sqlfluff lint models --dialect snowflake --rules L019,L020,L021,L022
 
-    'master': # override if your default branch doesn't run on a branch named "master"
+    'main': # override if your default branch doesn't run on a branch named "main"
       - step:
           script:
             - python --version
@@ -145,7 +144,7 @@ pipelines:
 
 ### 2. Commit and push your changes to make sure everything works
 
-After you finish creating the YAML files, commit and push your code. Doing this will trigger your pipeline for the first time! If everything goes well, you should see the pipeline in your code platform. When you click into the job you’ll get a log showing that SQLFluff was run. If your code failed linting you’ll get an error in the job with a description of what needs to be fixed. If everything passed the lint check, you’ll see a successful job run.
+After you finish creating the YAML files, commit and push your code to trigger your pipeline for the first time. If everything goes well, you should see the pipeline in your code platform. When you click into the job you’ll get a log showing that SQLFluff was run. If your code failed linting you’ll get an error in the job with a description of what needs to be fixed. If everything passed the lint check, you’ll see a successful job run.
 
 <Tabs
   defaultValue="github"
diff --git a/website/docs/guides/orchestration/set-up-ci/5-multiple-checks.md b/website/docs/guides/orchestration/set-up-ci/5-multiple-checks.md
new file mode 100644
index 00000000000..0b4173cffee
--- /dev/null
+++ b/website/docs/guides/orchestration/set-up-ci/5-multiple-checks.md
@@ -0,0 +1,69 @@
+---
+title: "Advanced: Create a release train with additional environments"
+slug: multiple-environments
+description: Large and complex enterprises sometimes require additional layers of validation before deployment. Learn how to add these checks with dbt Cloud.
+---
+
+:::caution Are you sure you need this?
+This approach can increase release safety, but creates additional manual steps in the deployment process as well as a greater maintenance burden.
+
+As such, it may slow down the time it takes to get new features into production.
+
+The team at Sunrun maintained a SOX-compliant deployment in dbt while reducing the number of environments. Check out [their Coalesce presentation](https://www.youtube.com/watch?v=vmBAO2XN-fM) to learn more.
+:::
+
+:::tip Join the beta
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up CI jobs. For docs, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs). This guide assumes you are using the improvements available in the beta.
+
+If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
+
+:::
+
+## Prerequisites
+
+This section assumes you already have the **Development**, **CI** and **Production** environments described in [the Baseline setup](/guides/orchestration/set-up-ci/in-15-minutes).
+
+In this section, we will add a new **QA** environment. New features will branch off from and be merged back into the associated `qa` branch, and a member of your team (the "Release Manager") will create a PR against `main` to be validated in the CI environment before going live.
+
+The git flow will look like this:
+<Lightbox src="/img/guides/best-practices/environment-setup/many-branch-git.png" title="git flow diagram with an intermediary branch" />
+
+## Step 1: Create a `release` branch in your git repo
+
+As noted above, this branch will outlive any individual feature, and will be the base of all feature development for a period of time. Your team might choose to create a new branch for each sprint (`qa/sprint-01`, `qa/sprint-02`, etc), tie it to a version of your data product (`qa/1.0`, `qa/1.1`), or just have a single `qa` branch which remains active indefinitely.
+
+## Step 2: Update your Development environment to use the `qa` branch
+
+See [Custom branch behavior](/docs/dbt-cloud-environments#custom-branch-behavior). Setting `qa` as your custom branch ensures that the IDE creates new branches and PRs with the correct target, instead of using `main`.
+
+<Lightbox src="/img/docs/dbt-cloud/cloud-configuring-dbt-cloud/dev-environment-custom-branch.png" title="A demonstration of configuring a custom branch for an environment" />
+
+## Step 3: Create a new QA environment
+
+See [Create a new environment](/docs/dbt-cloud-environments#create-a-deployment-environment). The environment should be called **QA**. Just like your existing Production and CI environments, it will be a Deployment-type environment.
+
+Set its branch to `qa` as well.
+
+## Step 4: Create a new job
+
+Use the **Continuous Integration Job** template, and call the job **QA Check**.
+
+In the Execution Settings, your command will be preset to `dbt build --select state:modified+`. Let's break this down:
+
+- [`dbt build`](/reference/commands/build) runs all nodes (seeds, models, snapshots, tests) at once in DAG order. If something fails, nodes that depend on it will be skipped.
+- The [`state:modified+` selector](/reference/node-selection/methods#the-state-method) means that only modified nodes and their children will be run ("Slim CI"). In addition to [not wasting time](https://discourse.getdbt.com/t/how-we-sped-up-our-ci-runs-by-10x-using-slim-ci/2603) building and testing nodes that weren't changed in the first place, this significantly reduces compute costs.
+
+To be able to find modified nodes, dbt needs to have something to compare against. Normally, we use the Production environment as the source of truth, but in this case there will be new code merged into `qa` long before it hits the `main` branch and Production environment. Because of this, we'll want to defer the Release environment to itself.
+
+### Optional: also add a compile-only job
+
+dbt Cloud uses the last successful run of any job in that environment as its [comparison state](/reference/node-selection/syntax#about-node-selection). If you have a lot of PRs in flight, the comparison state could switch around regularly.
+
+Adding a regularly-scheduled job inside of the QA environment whose only command is `dbt compile` can regenerate a more stable manifest for comparison purposes.
+
+## Step 5: Test your process
+
+When the Release Manager is ready to cut a new release, they will manually open a PR from `qa` into `main` from their git provider (e.g. GitHub, GitLab, Azure DevOps). dbt Cloud will detect the new PR, at which point the existing check in the CI environment will trigger and run. When using the [baseline configuration](/guides/orchestration/set-up-ci/in-15-minutes), it's possible to kick off the PR creation from inside of the dbt Cloud IDE. Under this paradigm, that button will create PRs targeting your QA branch instead.
+
+To test your new flow, create a new branch in the dbt Cloud IDE then add a new file or modify an existing one. Commit it, then create a new Pull Request (not a draft) against your `qa` branch. You'll see the integration tests begin to run. Once they complete, manually create a PR against `main`, and within a few seconds you’ll see the tests run again but this time incorporating all changes from all code that hasn't been merged to main yet.
diff --git a/website/sidebars.js b/website/sidebars.js
index e9cab8815a9..0faa37afff0 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -246,7 +246,7 @@ const sidebarSettings = {
         {
           type: "category",
           label: "Build your metrics",
-          link: { type: "doc", id: "docs/build/build-metrics-intro"},
+          link: { type: "doc", id: "docs/build/build-metrics-intro" },
           collapsed: true,
           items: [
             {
@@ -274,7 +274,7 @@ const sidebarSettings = {
             {
               type: "category",
               label: "Metrics",
-              link: { type: "doc", id: "docs/build/metrics-overview"},
+              link: { type: "doc", id: "docs/build/metrics-overview" },
               items: [
                 "docs/build/cumulative",
                 "docs/build/derived",
@@ -909,18 +909,6 @@ const sidebarSettings = {
             "guides/best-practices/materializations/materializations-guide-6-examining-builds",
             "guides/best-practices/materializations/materializations-guide-7-conclusion",
           ],
-        }, 
-        {
-          type: "category",
-          label: "dbt Cloud Environment best practices",
-          link: {
-            type: "doc",
-            id: "guides/best-practices/environment-setup/1-env-guide-overview",
-          },
-          items: [
-            "guides/best-practices/environment-setup/2-one-deployment-environment",
-            "guides/best-practices/environment-setup/3-many-deployment-environments",
-          ],
         },
         "guides/best-practices/debugging-errors",
         "guides/best-practices/writing-custom-generic-tests",
@@ -949,16 +937,29 @@ const sidebarSettings = {
             "guides/orchestration/airflow-and-dbt-cloud/3-running-airflow-and-dbt-cloud",
             "guides/orchestration/airflow-and-dbt-cloud/4-airflow-and-dbt-cloud-faqs",
           ],
+        }, 
+        {
+          type: "category",
+          label: "Set up Continuous Integration",
+          link: {
+            type: "doc",
+            id: "guides/orchestration/set-up-ci/introduction",
+          },
+          items: [
+            "guides/orchestration/set-up-ci/quick-setup",
+            "guides/orchestration/set-up-ci/run-dbt-project-evaluator",
+            "guides/orchestration/set-up-ci/lint-on-push",
+            "guides/orchestration/set-up-ci/multiple-checks",
+          ],
         },
         {
           type: "category",
-          label: "Customizing CI/CD",
+          label: "Custom Continuous Deployment Workflows",
           link: {
             type: "doc",
             id: "guides/orchestration/custom-cicd-pipelines/1-cicd-background",
           },
           items: [
-            "guides/orchestration/custom-cicd-pipelines/2-lint-on-push",
             "guides/orchestration/custom-cicd-pipelines/3-dbt-cloud-job-on-merge",
             "guides/orchestration/custom-cicd-pipelines/4-dbt-cloud-job-on-pr",
             "guides/orchestration/custom-cicd-pipelines/5-something-to-consider",
diff --git a/website/static/_redirects b/website/static/_redirects
index 4172475e431..ba9d00d37f6 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -762,6 +762,10 @@ https://tutorial.getdbt.com/* https://docs.getdbt.com/:splat 301!
 /docs/guides/migration-guide/_ /guides/migration/versions/:splat 301!
 /docs/guides/_ /guides/legacy/:splat 301!
 
+/guides/best-practices/environment-setup/1-env-guide-overview /guides/orchestration/set-up-ci/overview 301
+/guides/best-practices/environment-setup/2-one-deployment-environment /guides/orchestration/set-up-ci/in-15-minutes 301
+/guides/best-practices/environment-setup/3-many-deployment-environments /guides/orchestration/set-up-ci/multiple-environments 301
+
 # adapter development docs
 
 /docs/contributing/what-are-adapters /guides/advanced/adapter-development/1-what-are-adapters 301

From d89ddd1aecf9e52320681cfe1e5f038aa6f09629 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Mon, 7 Aug 2023 14:34:38 -0600
Subject: [PATCH 567/703] Example usage of a `foreign_key` and `unique`
 constraints

---
 website/docs/reference/resource-properties/constraints.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index 51e10e028ab..cbc388ee616 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -53,6 +53,9 @@ models:
         # column-level constraints
         constraints:
           - type: not_null
+          - type: unique
+          - type: foreign_key
+            expression: <other_model_schema>.<other_model_name> (<other_model_column>)
           - type: ...
 ```
 

From cd4c456a604730198eed58d7345e73f050cc2620 Mon Sep 17 00:00:00 2001
From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com>
Date: Mon, 7 Aug 2023 14:36:32 -0600
Subject: [PATCH 568/703] Add `unique` the list of supported types

---
 website/docs/reference/resource-properties/constraints.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index cbc388ee616..71d13c0ebb3 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -20,7 +20,7 @@ Constraints require the declaration and enforcement of a model [contract](/refer
 Constraints may be defined for a single column, or at the model level for one or more columns. As a general rule, we recommend defining single-column constraints directly on those columns.
 
 The structure of a constraint is:
-- `type` (required): one of `not_null`, `primary_key`, `foreign_key`, `check`, `custom`
+- `type` (required): one of `not_null`, `unique`, `primary_key`, `foreign_key`, `check`, `custom`
 - `expression`: Free text input to qualify the constraint. Required for certain constraint types, and optional for others.
 - `name` (optional): Human-friendly name for this constraint. Supported by some data platforms.
 - `columns` (model-level only): List of column names to apply the constraint over

From 5f81389e47567a3af7190234e6e0b77027491b8e Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Mon, 7 Aug 2023 13:41:48 -0700
Subject: [PATCH 569/703] Fast-follow for CI improvements beta

---
 website/docs/docs/deploy/ci-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 08fbd5f4225..a1f12d19f84 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -24,7 +24,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 ## Set up CI jobs {#set-up-ci-jobs}
 
-dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schema builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch as part of your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch.
+dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) that's connected to a staging database. Having a separate environment dedicated for CI will provide better isolation between your temporary CI schema builds and your production data builds. Additionally, sometimes teams need their CI jobs to be triggered when a PR is made to a branch other than main. If your team maintains a staging branch as part of your release process, having a separate environment will allow you to set a [custom branch](/faqs/environments/custom-branch-settings) and, accordingly, the CI job in that dedicated environment will be triggered only when PRs are made to the specified custom branch. To learn more, refer to [Get started with CI tests](/guides/orchestration/set-up-ci/overview).
 
 <Tabs queryString="version">
 <TabItem value="current" label="Current version" default>

From 87bb0258a6e17a5cba722cc744cc5f8cb93b8113 Mon Sep 17 00:00:00 2001
From: dave-connors-3 <73915542+dave-connors-3@users.noreply.github.com>
Date: Mon, 7 Aug 2023 16:01:42 -0500
Subject: [PATCH 570/703] Update metrics-overview.md

make table align with sample code
---
 website/docs/docs/build/metrics-overview.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index a1be4969537..a768b18f41a 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -13,7 +13,10 @@ The keys for metrics definitions are:
 | Component | Description | Type |
 | --------- | ----------- | ---- |
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
-| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Optional |
+| `description` | Provide the description for your metric.   | Optional |
+| `configs` | Provide the specific configurations for your metric   | Optional |
+| `label` | PThe display name for your metric. This value will be shown in downstream tools.   | Required |
+| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Required |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
 | `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |

From 9c5c4ea682059dde7ab08d5adfc479c1e4811b40 Mon Sep 17 00:00:00 2001
From: Thomas Lento <tlento@users.noreply.github.com>
Date: Mon, 7 Aug 2023 14:29:27 -0700
Subject: [PATCH 571/703] Update ratio metrics overview to reference metrics,
 not measures

Ratio metrics take other metrics as input, but the documentation was pointing to measures rather than metrics.
---
 website/docs/docs/build/metrics-overview.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index a1be4969537..eaa0b306674 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -98,7 +98,7 @@ metrics:
 
 ### Ratio metrics 
 
-[Ratio metrics](/docs/build/ratio) involve a numerator measure and a denominator measure. A  `constraint` string  can be applied, to both numerator and denominator, or applied separately to the numerator or denominator. 
+[Ratio metrics](/docs/build/ratio) involve a numerator metric and a denominator metric. A  `constraint` string  can be applied, to both numerator and denominator, or applied separately to the numerator or denominator. 
 
 ```yaml
 # Ratio Metric
@@ -106,25 +106,25 @@ metrics:
   - name: cancellation_rate
     owners:
       - support@getdbt.com
-# Ratio metrics create a ratio out of two measures.
-# Define the measures from the semantic model as numerator or denominator
+# Ratio metrics create a ratio out of two metrics.
+# Define the metrics from the semantic manifest as numerator or denominator
     type: ratio
     type_params:
-      numerator: cancellations_usd
-      denominator: transaction_amount_usd
+      numerator: cancellations
+      denominator: transaction_amount
       filter: |     # add optional constraint string. This applies to both the numerator and denominator
         {{ Dimension('customer__country') }} = 'MX'
   - name: enterprise_cancellation_rate
     owners:
       - support@getdbt.com
       # Ratio metrics create a ratio out of two measures. 
-      # Define the measures from the semantic model as numerator or denominator
+      # Define the metrics from the semantic model as numerator or denominator
     type: ratio
     type_params:
       numerator:
-        name: cancellations_usd
+        name: cancellations
         filter: {{ Dimension('company__tier' )}} = 'enterprise'  # constraint only applies to the numerator
-      denominator: transaction_amount_usd
+      denominator: transaction_amount
       filter: |   #  add optional constraint string. This applies to both the numerator and denominator
         {{ Dimension('customer__country') }} = 'MX'  
 ```

From 182faea325ace82450d6052e255e98f4743bb6c5 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 7 Aug 2023 17:30:53 -0400
Subject: [PATCH 572/703] Alpha docs for CLI

---
 .../docs/docs/cloud/cloud-cli-installation.md | 111 ++++++++++++++++++
 1 file changed, 111 insertions(+)
 create mode 100644 website/docs/docs/cloud/cloud-cli-installation.md

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
new file mode 100644
index 00000000000..f6f8fa78d38
--- /dev/null
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -0,0 +1,111 @@
+---
+title: Installing the dbt Cloud CLI (Alpha)
+id: cloud-cli-installation
+description: "Instructions for installing and configuring dbt Cloud CLI"
+---
+
+:::warning Alpha functionality 
+
+The following installation instructions are for the dbt Cloud CLI, currently in alpha. These instructions are not intended for general audiences at this time. 
+
+::: 
+
+## Installing dbt Cloud CLI
+
+### Install and update with Brew (recommended)
+
+Install the CLI: 
+
+```bash
+brew tap dbt-labs/dbt-cli
+brew install dbt-cloud-cli
+```
+
+Verify the installation:
+
+```bash
+which dbt
+dbt --help
+```
+
+Upgrade the CLI:
+
+```bash
+brew update
+brew upgrade dbt-cloud-cli
+```
+
+### Manually install (Windows only)
+
+1. Download the latest darwin release from [Github](https://github.com/dbt-labs/dbti/releases).
+2. Place the `dbt` executable in `/usr/local/bin` or `/usr/.local/bin` if `/usr/local/bin` doesn’t exist.
+3. Navigate to `/usr/local/bin` / `/usr/.local/bin` in Finder and double click on the `dbt` executable:
+    1. You will receive a message that the executable is untrusted and cannot be run. Override this by right-clicking the executable and selecting “Open”. A new prompt will pop up, allowing you to open the executable.
+    2. After clicking “Open”, a terminal window will appear. The window can be closed, and you can now invoke `dbt` from any other terminal*.
+        - You may need to deal with conflicts due to other `dbt` installations. Run `which dbt` to see the location of the `dbt` installation you’re using. If you get `/usr/local/bin/dbt` from `which dbt`, you’re using the one you just installed. If not, you may need to uninstall other installations, directly invoke the executable, or handle path conflicts differently, such as using an alias for the new executable.
+4. Move to a directory with a dbt project, and create a `dbt_cloud.yml` file containing your `project-id` from dbt Cloud.
+5. Invoke `dbt --help` from your terminal to see a list of supported commands.
+
+#### Updating dbt Cloud installation (Windows only)
+
+Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-only) and replace the existing `dbt` executable with the new one. You should not have to go through the security steps again.
+
+## Setting up the CLI
+
+1. Ensure that you have created a project in dbt Cloud.
+
+2. Ensure that your personal [development credentials](#glossary) are set on the project
+
+3. Create an environment variable with your [dbt cloud API key](#glossary):
+
+```bash
+
+   > $ vi ~/.zshrc
+
+   ...
+
+   # dbt Cloud CLI
+    export DBT_CLOUD_API_KEY="1234"
+   
+```
+
+4. Load the new environment variable. Note: you may need to reactivate your python virtual environment after sourcing your shell's dot file. Alternatively, restart your shell instead of sourcing the shell's dot file
+
+```bash
+   > $ source ~/.zshrc
+```
+
+5. Navigate to a dbt project
+
+```bash
+   > $ cd ~/dbt-projects/jaffle_shop
+```
+
+6. Ensure that a `dbt_cloud.yml` file exists in the project directory. The file is required to have a `project-id` field with a valid [project ID](#glossary):
+
+```bash
+> $ pwd
+/Users/user/dbt-projects/jaffle_shop
+
+> $ cat dbt_cloud.yml
+project-id: '123456'
+```
+
+7. The following commands are supported as of now (we will be adding more in the future):
+
+- `dbt run`
+- `dbt build`
+- `dbt deps`
+- `dbt cancel`
+- `dbt reattach`
+- `dbt --help`
+
+## Using dbt Cloud CLI
+
+**More informaiton coming soon**
+
+### Glossary
+
+- **dbt cloud API key:** your API key found by navigating to the **gear icon**, clicking **Profile Settings**, and scrolling down to **API**.
+- **Project ID:** the ID of the dbt project you're working with. Can be retrieved from the dbt cloud URL after a project has been selected, for example, `https://cloud.getdbt.com/deploy/{accountID}/projects/{projectID}`
+- **Development credentials:** your personal warehouse credentials for the project you’re working with. They can be set by selecting the project and entering them in dbt Cloud. Navigate to the **gear icon**, click **Profile Settings**, and click **Credentials** from the left-side menu.

From a0c331d2237a499dc9dc14ac49ac3f97242ddb42 Mon Sep 17 00:00:00 2001
From: Thomas Lento <tlento@users.noreply.github.com>
Date: Mon, 7 Aug 2023 14:38:58 -0700
Subject: [PATCH 573/703] Update ratio-metrics.md to reflect use of metrics as
 inputs

Ratio metrics take metric inputs for the numerator and denominator, rather than measures.
---
 website/docs/docs/build/ratio-metrics.md | 30 +++++++++++++-----------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index e206671d423..db108bd37fb 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -6,7 +6,7 @@ sidebar_label: Ratio
 tags: [Metrics, Semantic Layer]
 ---
 
-Ratio allows you to create a ratio between two measures. You simply specify a numerator and a denominator measure. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. 
+Ratio allows you to create a ratio between two metrics. You simply specify a numerator and a denominator metric. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. 
 
 The following displays the full spec for ratio metrics, along with an example:
 
@@ -17,12 +17,14 @@ metrics:
     type: ratio # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
-      numerator: the measure used for the numerator # Required
-      filter: filter for the numerator# Optional
-      alias: alias for the numerator # Optional
-      denominator: the measure used for the denominator # Required
-      filter: filter for the denominator # Optional
-      alias: alias for the denominator # Optional
+      numerator: the name of the metric used for the numerator, or a struct of properties as below # Required
+        name: name of metric used for the numerator # Required
+        filter: filter for the numerator# Optional
+        alias: alias for the numerator # Optional
+      denominator: the name of the metric used for the denominator, or a struct of properties as below # Required
+        name: name of metric used for the denominator # Required
+        filter: filter for the denominator # Optional
+        alias: alias for the denominator # Optional
 ```
 
 ## Ratio metrics example
@@ -30,13 +32,13 @@ metrics:
 ```yaml
 # Ratio Metric
 metrics:
-  - name: food_order_total_pct
-    description: "The food order total as the % of the total order"
-    label: Food Order Total % 
+  - name: food_order_pct
+    description: "The food order count as a ratio of the total order count"
+    label: Food Order Ratio
     type: ratio
     type_params: 
-      numerator: food_order_total
-      denominator: order_total
+      numerator: food_orders
+      denominator: orders
   
 ```
 ## Ratio metrics using different semantic models
@@ -90,7 +92,7 @@ on
 
 ## Add filter
 
-Users can define constraints on input measures for a metric by applying a filter directly to the measure, like so:
+Users can define constraints on input metrics for a ratio metric by applying a filter directly to the input metric, like so:
 
 ```yaml
 metrics:
@@ -108,4 +110,4 @@ metrics:
         name: distinct_purchasers
 ```
 
-Note the `filter` and `alias` parameters for the measure referenced in the numerator. Use the `filter` parameter to apply a filter to the measure it's attached to. The `alias` parameter is used to avoid naming conflicts in the rendered SQL queries when the same measure is used with different filters. If there are no naming conflicts, the `alias` parameter can be left out.
+Note the `filter` and `alias` parameters for the metric referenced in the numerator. Use the `filter` parameter to apply a filter to the metric it's attached to. The `alias` parameter is used to avoid naming conflicts in the rendered SQL queries when the same metric is used with different filters. If there are no naming conflicts, the `alias` parameter can be left out.

From 62318110d613f38c7c6a1653ea5ddc18fd853dad Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Mon, 7 Aug 2023 15:57:36 -0700
Subject: [PATCH 574/703] add time granularity

---
 website/docs/docs/build/metricflow-cli.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 8687d4e31aa..b58f42859a0 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -367,3 +367,11 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 
 </TabItem>
 </Tabs>
+
+## Time Granularity
+You can optionally specify the time granularity you want your data to be aggregated at by appending double underscore and the unit of granularity you want against your. The granularity options are: day, week, month, quarter year. 
+
+An example for querying metric data at a monthly grain would look like this
+```bash
+mf query --metrics revenue --group-by metric_time__month
+```
\ No newline at end of file

From 7f50ddbedafc2dbaa68c3bc51460893b90f12803 Mon Sep 17 00:00:00 2001
From: Jordan <jordan.stein77@gmail.com>
Date: Mon, 7 Aug 2023 16:07:03 -0700
Subject: [PATCH 575/703] Update website/docs/docs/build/metricflow-cli.md

nice one!

Co-authored-by: winnie <91998347+gwenwindflower@users.noreply.github.com>
---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index b58f42859a0..ed0489c6490 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -369,7 +369,7 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 </Tabs>
 
 ## Time Granularity
-You can optionally specify the time granularity you want your data to be aggregated at by appending double underscore and the unit of granularity you want against your. The granularity options are: day, week, month, quarter year. 
+You can optionally specify the time granularity you want your data to be aggregated at by appending double underscore and the unit of granularity you want to the global time dimension `metric_time`. The granularity options to group by are: day, week, month, quarter year. 
 
 An example for querying metric data at a monthly grain would look like this
 ```bash

From 5a8ccedfeae74cc1e0d6ac7a7733af2b9e55377c Mon Sep 17 00:00:00 2001
From: Jordan <jordan.stein77@gmail.com>
Date: Mon, 7 Aug 2023 16:26:55 -0700
Subject: [PATCH 576/703] Update website/docs/docs/build/metricflow-cli.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index ed0489c6490..5c3bcb5899b 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -368,7 +368,7 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 </TabItem>
 </Tabs>
 
-## Time Granularity
+## Time granularity
 You can optionally specify the time granularity you want your data to be aggregated at by appending double underscore and the unit of granularity you want to the global time dimension `metric_time`. The granularity options to group by are: day, week, month, quarter year. 
 
 An example for querying metric data at a monthly grain would look like this

From 06e4104c1090c012d23df010e2444aa60a6db914 Mon Sep 17 00:00:00 2001
From: Jordan <jordan.stein77@gmail.com>
Date: Mon, 7 Aug 2023 16:27:02 -0700
Subject: [PATCH 577/703] Update website/docs/docs/build/metricflow-cli.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 5c3bcb5899b..69b3a8fda02 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -369,7 +369,7 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 </Tabs>
 
 ## Time granularity
-You can optionally specify the time granularity you want your data to be aggregated at by appending double underscore and the unit of granularity you want to the global time dimension `metric_time`. The granularity options to group by are: day, week, month, quarter year. 
+Optionally, you can specify the time granularity you want your data to be aggregated at by appending two underscores and the unit of granularity you want to `metric_time`, the global time dimension . You can group the granularity by: `day`, `week`, `month`, `quarter`, and `year`. 
 
 An example for querying metric data at a monthly grain would look like this
 ```bash

From 9bfc0092f6d81bd49f9d90f2534f56afad9f96d6 Mon Sep 17 00:00:00 2001
From: Jordan <jordan.stein77@gmail.com>
Date: Mon, 7 Aug 2023 16:27:12 -0700
Subject: [PATCH 578/703] Update website/docs/docs/build/metricflow-cli.md

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
---
 website/docs/docs/build/metricflow-cli.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metricflow-cli.md b/website/docs/docs/build/metricflow-cli.md
index 69b3a8fda02..0f90b2bb03c 100644
--- a/website/docs/docs/build/metricflow-cli.md
+++ b/website/docs/docs/build/metricflow-cli.md
@@ -371,7 +371,7 @@ mf query --metrics order_amount --group-by metric_time,is_food_order --limit 10
 ## Time granularity
 Optionally, you can specify the time granularity you want your data to be aggregated at by appending two underscores and the unit of granularity you want to `metric_time`, the global time dimension . You can group the granularity by: `day`, `week`, `month`, `quarter`, and `year`. 
 
-An example for querying metric data at a monthly grain would look like this
+Below is an example for querying metric data at a monthly grain:
 ```bash
 mf query --metrics revenue --group-by metric_time__month
 ```
\ No newline at end of file

From b24c417ef9c17d2ae9883ed79fb6ade29f07a53d Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Mon, 7 Aug 2023 18:50:36 -0500
Subject: [PATCH 579/703] Update 'How we structure' for SL (#3851)

## What are you changing in this pull request and why?
Adds a page to the 'How we structure' guide covering our recommendations
re the Semantic Layer.
---
 .../how-we-structure/1-guide-overview.md      | 22 ++---
 .../how-we-structure/2-staging.md             | 81 ++++++++++---------
 .../how-we-structure/3-intermediate.md        | 32 ++++----
 .../how-we-structure/4-marts.md               | 24 +++---
 .../5-semantic-layer-marts.md                 | 48 +++++++++++
 ...roject.md => 6-the-rest-of-the-project.md} | 14 ++--
 website/sidebars.js                           |  3 +-
 7 files changed, 137 insertions(+), 87 deletions(-)
 create mode 100644 website/docs/guides/best-practices/how-we-structure/5-semantic-layer-marts.md
 rename website/docs/guides/best-practices/how-we-structure/{5-the-rest-of-the-project.md => 6-the-rest-of-the-project.md} (93%)

diff --git a/website/docs/guides/best-practices/how-we-structure/1-guide-overview.md b/website/docs/guides/best-practices/how-we-structure/1-guide-overview.md
index 1bbb628b73d..d1e78231e57 100644
--- a/website/docs/guides/best-practices/how-we-structure/1-guide-overview.md
+++ b/website/docs/guides/best-practices/how-we-structure/1-guide-overview.md
@@ -14,9 +14,9 @@ Building a great dbt project is an inherently collaborative endeavor, bringing t
 
 Famously, Steve Jobs [wore the same outfit everyday](https://images.squarespace-cdn.com/content/v1/5453c539e4b02ab5398ffc8f/1580381503218-E56FQDNFL1P4OBLQWHWW/ke17ZwdGBToddI8pDm48kJKedFpub2aPqa33K4gNUDwUqsxRUqqbr1mOJYKfIPR7LoDQ9mXPOjoJoqy81S2I8N_N4V1vUb5AoIIIbLZhVYxCRW4BPu10St3TBAUQYVKcxb5ZTIyC_D49_DDQq2Sj8YVGtM7O1i4h5tvKa2lazN4nGUQWMS_WcPM-ztWbVr-c/steve_jobs_outfit.jpg) to reduce decision fatigue. You can think of this guide similarly, as a black turtleneck and New Balance sneakers for your company’s dbt project. A dbt project’s power outfit, or more accurately its structure, is composed not of fabric but of files, folders, naming conventions, and programming patterns. How you label things, group them, split them up, or bring them together — the system you use to organize the [data transformations](https://www.getdbt.com/analytics-engineering/transformation/) encoded in your dbt project — this is your project’s structure.
 
-This guide is just a starting point. You may decide that you prefer Birkenstocks or a purple hoodie for your project over Jobs-ian minimalism. That's fine. What's important is that you think through the reasoning for those changes in your organization, explicitly declare them in a thorough, accessible way for all contributors, and above all *stay consistent*.
+This guide is just a starting point. You may decide that you prefer Birkenstocks or a purple hoodie for your project over Jobs-ian minimalism. That's fine. What's important is that you think through the reasoning for those changes in your organization, explicitly declare them in a thorough, accessible way for all contributors, and above all _stay consistent_.
 
-One foundational principle that applies to all dbt projects though, is the need to establish a cohesive arc moving data from *source-conformed* to *business-conformed*. Source-conformed data is shaped by external systems out of our control, while business-conformed data is shaped by the needs, concepts, and definitions we create. No matter what patterns or conventions you define within your project, this process remains the essential purpose of the transformation layer, and dbt as your tool within it. This guide is an update to a seminal analytics engineering [post of the same name](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355) by the great Claire Carroll, and while some of the details have changed over time (as anticipated in that post) this fundamental trajectory holds true. Moving forward, this guide will be iteratively updated as new tools expand our viewpoints, new experiences sharpen our vision, and new voices strengthen our perspectives, but always in service of that aim.
+One foundational principle that applies to all dbt projects though, is the need to establish a cohesive arc moving data from _source-conformed_ to _business-conformed_. Source-conformed data is shaped by external systems out of our control, while business-conformed data is shaped by the needs, concepts, and definitions we create. No matter what patterns or conventions you define within your project, this process remains the essential purpose of the transformation layer, and dbt as your tool within it. This guide is an update to a seminal analytics engineering [post of the same name](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355) by the great Claire Carroll, and while some of the details have changed over time (as anticipated in that post) this fundamental trajectory holds true. Moving forward, this guide will be iteratively updated as new tools expand our viewpoints, new experiences sharpen our vision, and new voices strengthen our perspectives, but always in service of that aim.
 
 ### Learning goals
 
@@ -24,7 +24,7 @@ This guide has three main goals:
 
 - Thoroughly cover our most up-to-date recommendations on how to structure typical dbt projects
 - Illustrate these recommendations with comprehensive examples
-- At each stage, explain *why* we recommend the approach that we do, so that you're equipped to decide when and where to deviate from these recommendations to better fit your organization’s unique needs
+- At each stage, explain _why_ we recommend the approach that we do, so that you're equipped to decide when and where to deviate from these recommendations to better fit your organization’s unique needs
 
 You should walk away from this guide with a deeper mental model of how the components of a dbt project fit together, such that purpose and principles of analytics engineering feel more clear and intuitive.
 
@@ -33,7 +33,7 @@ By approaching our structure intentionally, we’ll gain a better understanding
 Our hope is that by deepening your sense of the connections between these patterns and the principles they flow from, you'll be able to translate them to fit your specific needs and craft customized documentation for your team to act on.
 
 :::info Example project.
-This guide walks through our recommendations using a very simple dbt project — similar to the one used for the Getting Started guide and many other demos — from a fictional company called the Jaffle Shop. You can read more about [jaffles](https://en.wiktionary.org/wiki/jaffle) if you want (they *are* a real thing), but that context isn’t important to understand the structure. We encourage you to follow along, try things out, make changes, and take notes on what works or doesn't work for you along the way.
+This guide walks through our recommendations using a very simple dbt project — similar to the one used for the Getting Started guide and many other demos — from a fictional company called the Jaffle Shop. You can read more about [jaffles](https://en.wiktionary.org/wiki/jaffle) if you want (they _are_ a real thing), but that context isn’t important to understand the structure. We encourage you to follow along, try things out, make changes, and take notes on what works or doesn't work for you along the way.
 :::
 
 We'll get a deeper sense of our project as we move through the guide, but for now we just need to know that the Jaffle Shop is a restaurant selling jaffles that has two main data sources:
@@ -46,17 +46,17 @@ We'll get a deeper sense of our project as we move through the guide, but for no
 We'll walk through our topics in the same order that our data would move through transformation:
 
 1. Dig into how we structure the files, folders, and models for our three primary layers in the `models` directory, which build on each other:
-    1. **Staging** — creating our atoms, our initial modular building blocks, from source data
-    2. **Intermediate** — stacking layers of logic with clear and specific purposes to prepare our staging models to join into the entities we want
-    3. **Marts** — bringing together our modular pieces into a wide, rich vision of the entities our organization cares about
+   1. **Staging** — creating our atoms, our initial modular building blocks, from source data
+   2. **Intermediate** — stacking layers of logic with clear and specific purposes to prepare our staging models to join into the entities we want
+   3. **Marts** — bringing together our modular pieces into a wide, rich vision of the entities our organization cares about
 2. Explore how these layers fit into the rest of the project:
-    1. Review the overall structure comprehensively
-    2. Expand on YAML configuration in-depth
-    3. Discuss how to use the other folders in a dbt project: `tests`, `seeds`, and `analyses`
+   1. Review the overall structure comprehensively
+   2. Expand on YAML configuration in-depth
+   3. Discuss how to use the other folders in a dbt project: `tests`, `seeds`, and `analyses`
 
 Below is the complete file tree of the project we’ll be working through. Don’t worry if this looks like a lot of information to take in at once - this is just to give you the full vision of what we’re building towards. We’ll focus in on each of the sections one by one as we break down the project’s structure.
 
-```markdown
+```shell
 jaffle_shop
 ├── README.md
 ├── analyses
diff --git a/website/docs/guides/best-practices/how-we-structure/2-staging.md b/website/docs/guides/best-practices/how-we-structure/2-staging.md
index a14c5c8992b..cb46fa19b33 100644
--- a/website/docs/guides/best-practices/how-we-structure/2-staging.md
+++ b/website/docs/guides/best-practices/how-we-structure/2-staging.md
@@ -14,7 +14,7 @@ We'll use an analogy for working with dbt throughout this guide: thinking modula
 
 Let's zoom into the staging directory from our `models` file tree [in the overview](/guides/best-practices/how-we-structure/1-guide-overview) and walk through what's going on here.
 
-```markdown
+```shell
 models/staging
 ├── jaffle_shop
 │   ├── _jaffle_shop__docs.md
@@ -36,7 +36,7 @@ models/staging
   - ❌ **Subdirectories based on loader.** Some people attempt to group by how the data is loaded (Fivetran, Stitch, custom syncs), but this is too broad to be useful on a project of any real size.
   - ❌ **Subdirectories based on business grouping.** Another approach we recommend against is splitting up by business groupings in the staging layer, and creating subdirectories like 'marketing', 'finance', etc. A key goal of any great dbt project should be establishing a single source of truth. By breaking things up too early, we open ourselves up to creating overlap and conflicting definitions (think marketing and financing having different fundamental tables for orders). We want everybody to be building with the same set of atoms, so in our experience, starting our transformations with our staging structure reflecting the source system structures is the best level of grouping for this step.
 - **File names.** Creating a consistent pattern of file naming is [crucial in dbt](https://docs.getdbt.com/blog/on-the-importance-of-naming). File names must be unique and correspond to the name of the model when selected and created in the warehouse. We recommend putting as much clear information into the file name as possible, including a prefix for the layer the model exists in, important grouping information, and specific information about the entity or transformation in the model.
-  - ✅ `stg_[source]__[entity]s.sql` -  the double underscore between source system and entity helps visually distinguish the separate parts in the case of a source name having multiple words. For instance, `google_analytics__campaigns` is always understandable, whereas to somebody unfamiliar `google_analytics_campaigns` could be `analytics_campaigns` from the `google` source system as easily as `campaigns` from the `google_analytics` source system. Think of it like an [oxford comma](https://www.youtube.com/watch?v=P_i1xk07o4g), the extra clarity is very much worth the extra punctuation.
+  - ✅ `stg_[source]__[entity]s.sql` - the double underscore between source system and entity helps visually distinguish the separate parts in the case of a source name having multiple words. For instance, `google_analytics__campaigns` is always understandable, whereas to somebody unfamiliar `google_analytics_campaigns` could be `analytics_campaigns` from the `google` source system as easily as `campaigns` from the `google_analytics` source system. Think of it like an [oxford comma](https://www.youtube.com/watch?v=P_i1xk07o4g), the extra clarity is very much worth the extra punctuation.
   - ❌ `stg_[entity].sql` - might be specific enough at first, but will break down in time. Adding the source system into the file name aids in discoverability, and allows understanding where a component model came from even if you aren't looking at the file tree.
   - ✅ **Plural.** SQL, and particularly SQL in dbt, should read as much like prose as we can achieve. We want to lean into the broad clarity and declarative nature of SQL when possible. As such, unless there’s a single order in your `orders` table, plural is the correct way to describe what is in a table with multiple rows.
 
@@ -77,7 +77,7 @@ renamed as (
         -- numerics
         amount as amount_cents,
         amount / 100.0 as amount,
-        
+
         -- booleans
         case
             when status = 'successful' then true
@@ -104,20 +104,21 @@ select * from renamed
   - ✅ **Categorizing** (using conditional logic to group values into buckets or booleans, such as in the `case when` statements above)
   - ❌ **Joins** — the goal of staging models is to clean and prepare individual source conformed concepts for downstream usage. We're creating the most useful version of a source system table, which we can use as a new modular component for our project. In our experience, joins are almost always a bad idea here — they create immediate duplicated computation and confusing relationships that ripple downstream — there are occasionally exceptions though (see [base models](guides/best-practices/how-we-structure/2-staging#staging-other-considerations) below).
   - ❌ **Aggregations** — aggregations entail grouping, and we're not doing that at this stage. Remember - staging models are your place to create the building blocks you’ll use all throughout the rest of your project — if we start changing the grain of our tables by grouping in this layer, we’ll lose access to source data that we’ll likely need at some point. We just want to get our individual concepts cleaned and ready for use, and will handle aggregating values downstream.
-- ✅ **Materialized as views.** Looking at a partial view of our `dbt_project.yml` below, we can see that we’ve configured the entire staging directory to be materialized as <Term id='view'>views</Term>.  As they’re not intended to be final artifacts themselves, but rather building blocks for later models, staging models should typically be materialized as views for two key reasons:
+- ✅ **Materialized as views.** Looking at a partial view of our `dbt_project.yml` below, we can see that we’ve configured the entire staging directory to be materialized as <Term id='view'>views</Term>. As they’re not intended to be final artifacts themselves, but rather building blocks for later models, staging models should typically be materialized as views for two key reasons:
+
   - Any downstream model (discussed more in [marts](/guides/best-practices/how-we-structure/4-marts)) referencing our staging models will always get the freshest data possible from all of the component views it’s pulling together and materializing
   - It avoids wasting space in the warehouse on models that are not intended to be queried by data consumers, and thus do not need to perform as quickly or efficiently
 
     ```yaml
     # dbt_project.yml
-    
+
     models:
       jaffle_shop:
         staging:
           +materialized: view
     ```
 
-- Staging models are the only place we'll use the [`source` macro](/docs/build/sources), and our staging models should have a 1-to-1 relationship to our source tables. That means for each source system table we’ll have a single staging model referencing it, acting as its entry point — *staging* it — for use downstream.
+- Staging models are the only place we'll use the [`source` macro](/docs/build/sources), and our staging models should have a 1-to-1 relationship to our source tables. That means for each source system table we’ll have a single staging model referencing it, acting as its entry point — _staging_ it — for use downstream.
 
 :::tip Don’t Repeat Yourself.
 Staging models help us keep our code <Term id='dry'>DRY</Term>. dbt's modular, reusable structure means we can, and should, push any transformations that we’ll always want to use for a given component model as far upstream as possible. This saves us from potentially wasting code, complexity, and compute doing the same transformation more than once. For instance, if we know we always want our monetary values as floats in dollars, but the source system is integers and cents, we want to do the division and type casting as early as possible so that we can reference it rather than redo it repeatedly downstream.
@@ -128,94 +129,96 @@ This is a welcome change for many of us who have become used to applying the sam
 ### Staging: Other considerations
 
 - **Base models when joins are necessary to stage concepts.** Sometimes, in order to maintain a clean and <Term id='dry'>DRY</Term> staging layer we do need to implement some joins to create a solid concept for our building blocks. In these cases, we recommend creating a sub-directory in the staging directory for the source system in question and building `base` models. These have all the same properties that would normally be in the staging layer, they will directly source the raw data and do the non-joining transformations, then in the staging models we’ll join the requisite base models. The most common use cases for building a base layer under a staging folder are:
+
   - ✅ **Joining in separate delete tables**. Sometimes a source system might store deletes in a separate table. Typically we’ll want to make sure we can mark or filter out deleted records for all our component models, so we’ll need to join these delete records up to any of our entities that follow this pattern. This is the example shown below to illustrate.
 
     ```sql
     -- base_jaffle_shop__customers.sql
-    
+
     with
-    
+
     source as (
-    
+
         select * from {{ source('jaffle_shop','customers') }}
-    
+
     ),
-    
+
     customers as (
-    
+
         select
             id as customer_id,
             first_name,
             last_name
-    
+
         from source
-    
+
     )
-    
+
     select * from customers
     ```
 
     ```sql
     -- base_jaffle_shop__deleted_customers.sql
-    
+
     with
-    
+
     source as (
-    
+
         select * from {{ source('jaffle_shop','customer_deletes') }}
-    
+
     ),
-    
+
     deleted_customers as (
-    
+
         select
             id as customer_id,
             deleted as deleted_at
-    
+
         from source
-    
+
     )
-    
+
     select * from deleted_customers
     ```
 
     ```sql
     -- stg_jaffle_shop__customers.sql
-    
+
     with
-    
+
     customers as (
-    
+
         select * from {{ ref('base_jaffle_shop__customers') }}
-    
+
     ),
-    
+
     deleted_customers as (
-    
+
         select * from {{ ref('base_jaffle_shop__deleted_customers') }}
-    
+
     ),
-    
+
     join_and_mark_deleted_customers as (
-    
+
         select
             customers.*,
             case
                 when deleted_customers.deleted_at is not null then true
                 else false
             end as is_deleted
-    
+
         from customers
-    
+
         left join deleted_customers on customers.customer_id = deleted_customers.customer_id
-       
+
     )
-    
+
     select * from join_and_mark_deleted_customers
     ```
 
-  - ✅ **Unioning disparate but symmetrical sources**. A typical example here would be if you operate multiple ecommerce platforms in various territories via a SaaS platform like Shopify. You would have perfectly identical schemas, but all loaded separately into your warehouse. In this case, it’s easier to reason about our orders if *all* of our shops are unioned together, so we’d want to handle the unioning in a base model before we carry on with our usual staging model transformations on the (now complete) set — you can dig into [more detail on this use case here](https://discourse.getdbt.com/t/unioning-identically-structured-data-sources/921).
-- **[Codegen](https://github.com/dbt-labs/dbt-codegen) to automate staging table generation.** It’s very good practice to learn to write staging models by hand, they’re straightforward and numerous, so they can be an excellent way to absorb the dbt style of writing SQL. Also, we’ll invariably find ourselves needing to add special elements to specific models at times —  for instance, in one of the situations above that require base models — so it’s helpful to deeply understand how they work. Once that understanding is established though, because staging models are built largely following the same rote patterns and need to be built 1-to-1 for each source table in a source system, it’s preferable to start automating their creation. For this, we have the [codegen](https://github.com/dbt-labs/dbt-codegen) package. This will let you automatically generate all the source YAML and staging model boilerplate to speed up this step, and we recommend using it in every project.
+  - ✅ **Unioning disparate but symmetrical sources**. A typical example here would be if you operate multiple ecommerce platforms in various territories via a SaaS platform like Shopify. You would have perfectly identical schemas, but all loaded separately into your warehouse. In this case, it’s easier to reason about our orders if _all_ of our shops are unioned together, so we’d want to handle the unioning in a base model before we carry on with our usual staging model transformations on the (now complete) set — you can dig into [more detail on this use case here](https://discourse.getdbt.com/t/unioning-identically-structured-data-sources/921).
+
+- **[Codegen](https://github.com/dbt-labs/dbt-codegen) to automate staging table generation.** It’s very good practice to learn to write staging models by hand, they’re straightforward and numerous, so they can be an excellent way to absorb the dbt style of writing SQL. Also, we’ll invariably find ourselves needing to add special elements to specific models at times — for instance, in one of the situations above that require base models — so it’s helpful to deeply understand how they work. Once that understanding is established though, because staging models are built largely following the same rote patterns and need to be built 1-to-1 for each source table in a source system, it’s preferable to start automating their creation. For this, we have the [codegen](https://github.com/dbt-labs/dbt-codegen) package. This will let you automatically generate all the source YAML and staging model boilerplate to speed up this step, and we recommend using it in every project.
 - **Utilities folder.** While this is not in the `staging` folder, it’s useful to consider as part of our fundamental building blocks. The `models/utilities` directory is where we can keep any general purpose models that we generate from macros or based on seeds that provide tools to help us do our modeling, rather than data to model itself. The most common use case is a [date spine](https://github.com/dbt-labs/dbt-utils#date_spine-source) generated with [the dbt utils package](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/).
 
 :::info Development flow versus DAG order.
diff --git a/website/docs/guides/best-practices/how-we-structure/3-intermediate.md b/website/docs/guides/best-practices/how-we-structure/3-intermediate.md
index 5e1db61c49f..0cf44d3cccc 100644
--- a/website/docs/guides/best-practices/how-we-structure/3-intermediate.md
+++ b/website/docs/guides/best-practices/how-we-structure/3-intermediate.md
@@ -12,7 +12,7 @@ Once we’ve got our atoms ready to work with, we’ll set about bringing them t
 
 Let’s take a look at the intermediate layer of our project to understand the purpose of this stage more concretely.
 
-```markdown
+```shell
 models/intermediate
 └── finance
     ├── _int_finance__models.yml
@@ -22,10 +22,10 @@ models/intermediate
 - **Folders**
   - ✅ **Subdirectories based on business groupings.** Much like the staging layer, we’ll house this layer of models inside their own `intermediate` subfolder. Unlike the staging layer, here we shift towards being business-conformed, splitting our models up into subdirectories not by their source system, but by their area of business concern.
 - **File names**
-  - `✅ int_[entity]s_[verb]s.sql` - the variety of transformations that can happen inside of the intermediate layer makes it harder to dictate strictly how to name them. The best guiding principle is to think about *verbs* (e.g. `pivoted`, `aggregated_to_user`, `joined`, `fanned_out_by_quantity`, `funnel_created`, etc.)  in the intermediate layer. In our example project, we use an intermediate model to pivot payments out to the order grain, so we name our model `int_payments_pivoted_to_orders`. It’s easy for anybody to quickly understand what’s happening in that model, even if they don’t know [SQL](https://mode.com/sql-tutorial/). That clarity is worth the long file name. It’s important to note that we’ve dropped the double underscores at this layer. In moving towards business-conformed concepts, we no longer need to separate a system and an entity and simply reference the unified entity if possible. In cases where you need intermediate models to operate at the source system level (e.g. `int_shopify__orders_summed`, `int_core__orders_summed` which you would later union), you’d preserve the double underscores. Some people like to separate the entity and verbs with double underscores as well. That’s a matter of preference, but in our experience, there is often an intrinsic connection between entities and verbs in this layer that make that difficult to maintain.
+  - `✅ int_[entity]s_[verb]s.sql` - the variety of transformations that can happen inside of the intermediate layer makes it harder to dictate strictly how to name them. The best guiding principle is to think about _verbs_ (e.g. `pivoted`, `aggregated_to_user`, `joined`, `fanned_out_by_quantity`, `funnel_created`, etc.) in the intermediate layer. In our example project, we use an intermediate model to pivot payments out to the order grain, so we name our model `int_payments_pivoted_to_orders`. It’s easy for anybody to quickly understand what’s happening in that model, even if they don’t know [SQL](https://mode.com/sql-tutorial/). That clarity is worth the long file name. It’s important to note that we’ve dropped the double underscores at this layer. In moving towards business-conformed concepts, we no longer need to separate a system and an entity and simply reference the unified entity if possible. In cases where you need intermediate models to operate at the source system level (e.g. `int_shopify__orders_summed`, `int_core__orders_summed` which you would later union), you’d preserve the double underscores. Some people like to separate the entity and verbs with double underscores as well. That’s a matter of preference, but in our experience, there is often an intrinsic connection between entities and verbs in this layer that make that difficult to maintain.
 
 :::tip Don’t over-optimize too early!
-The example project is very simple for illustrative purposes. This level of division in our post-staging layers is probably unnecessary when dealing with these few models. Remember, our goal is a *single* *source of truth.* We don’t want finance and marketing operating on separate `orders` models, we want to use our dbt project as a means to bring those definitions together! As such, don’t split and optimize too early. If you have less than 10 marts models and aren’t having problems developing and using them, feel free to forego subdirectories completely (except in the staging layer, where you should always implement them as you add new source systems to your project) until the project has grown to really need them. Using dbt is always about bringing simplicity to complexity.
+The example project is very simple for illustrative purposes. This level of division in our post-staging layers is probably unnecessary when dealing with these few models. Remember, our goal is a _single_ _source of truth._ We don’t want finance and marketing operating on separate `orders` models, we want to use our dbt project as a means to bring those definitions together! As such, don’t split and optimize too early. If you have less than 10 marts models and aren’t having problems developing and using them, feel free to forego subdirectories completely (except in the staging layer, where you should always implement them as you add new source systems to your project) until the project has grown to really need them. Using dbt is always about bringing simplicity to complexity.
 :::
 
 ### Intermediate: Models
@@ -36,27 +36,27 @@ Below is the lone intermediate model from our small example project. This repres
 -- int_payments_pivoted_to_orders.sql
 
 {%- set payment_methods = ['bank_transfer','credit_card','coupon','gift_card'] -%}
- 
-with 
+
+with
 
 payments as (
 
    select * from {{ ref('stg_stripe__payments') }}
 
 ),
- 
+
 pivot_and_aggregate_payments_to_order_grain as (
-   
+
    select
-      order_id,     
+      order_id,
       {% for payment_method in payment_methods -%}
- 
+
          sum(
             case
                when payment_method = '{{ payment_method }}' and
-                    status = 'success' 
-               then amount 
-               else 0 
+                    status = 'success'
+               then amount
+               else 0
             end
          ) as {{ payment_method }}_amount,
 
@@ -68,7 +68,7 @@ pivot_and_aggregate_payments_to_order_grain as (
    group by 1
 
 )
- 
+
 select * from pivot_and_aggregate_payments_to_order_grain
 ```
 
@@ -77,15 +77,15 @@ select * from pivot_and_aggregate_payments_to_order_grain
 - ✅ **Materialized as views in a custom schema with special permissions.** A more robust option is to materialize your intermediate models as views in a specific [custom schema](/docs/build/custom-schemas), outside of your main production schema. This gives you added insight into development and easier troubleshooting as the number and complexity of your models grows, while remaining easy to implement and taking up negligible space.
 
 :::tip Keep your warehouse tidy!
-There are three interfaces to the organizational knowledge graph we’re encoding into dbt: the DAG, the files and folder structure of our codebase, and the output into the warehouse. As such, it’s really important that we consider that output intentionally! Think of the schemas, tables, and views we’re creating in the warehouse as *part of the UX,* in addition to the dashboards, ML, apps, and other use cases you may be targeting for the data. Ensuring that our output is named and grouped well, and that models not intended for broad use are either not materialized or built into special areas with specific permissions is crucial to achieving this.
+There are three interfaces to the organizational knowledge graph we’re encoding into dbt: the DAG, the files and folder structure of our codebase, and the output into the warehouse. As such, it’s really important that we consider that output intentionally! Think of the schemas, tables, and views we’re creating in the warehouse as _part of the UX,_ in addition to the dashboards, ML, apps, and other use cases you may be targeting for the data. Ensuring that our output is named and grouped well, and that models not intended for broad use are either not materialized or built into special areas with specific permissions is crucial to achieving this.
 :::
 
 - Intermediate models’ purposes, as these serve to break up complexity from our marts models, can take as many forms as [data transformation](https://www.getdbt.com/analytics-engineering/transformation/) might require. Some of the most common use cases of intermediate models include:
-  
+
   - ✅ **Structural simplification.** Bringing together a reasonable number (typically 4 to 6) of entities or concepts (staging models, or perhaps other intermediate models) that will be joined with another similarly purposed intermediate model to generate a mart — rather than have 10 joins in our mart, we can join two intermediate models that each house a piece of the complexity, giving us increased readability, flexibility, testing surface area, and insight into our components.
   - ✅ **Re-graining.** Intermediate models are often used to fan out or collapse models to the right composite grain — if we’re building a mart for `order_items` that requires us to fan out our `orders` based on the `quantity` column, creating a new single row for each item, this would be ideal to do in a specific intermediate model to maintain clarity in our mart and more easily view that our grain is correct before we mix it with other components.
   - ✅ **Isolating complex operations.** It’s helpful to move any particularly complex or difficult to understand pieces of logic into their own intermediate models. This not only makes them easier to refine and troubleshoot, but simplifies later models that can reference this concept in a more clearly readable way. For example, in the `quantity` fan out example above, we benefit by isolating this complex piece of logic so we can quickly debug and thoroughly test that transformation, and downstream models can reference `order_items` in a way that’s intuitively easy to grasp.
 
 :::tip Narrow the DAG, widen the tables.
-Until we get to the marts layer and start building our various outputs, we ideally want our DAG to look like an arrowhead pointed right. As we move from source-conformed to business-conformed, we’re also moving from numerous, narrow, isolated concepts to fewer, wider, joined concepts. We’re bringing our components together into wider, richer concepts, and that creates this shape in our DAG. This way when we get to the marts layer we have a robust set of components that can quickly and easily be put into any configuration to answer a variety of questions and serve specific needs. One rule of thumb to ensure you’re following this pattern on an individual model level is allowing multiple *inputs* to a model, but **not** multiple *outputs*. Several arrows going *into* our post-staging models is great and expected, several arrows coming *out* is a red flag. There are absolutely situations where you need to break this rule, but it’s something to be aware of, careful about, and avoid when possible.
+Until we get to the marts layer and start building our various outputs, we ideally want our DAG to look like an arrowhead pointed right. As we move from source-conformed to business-conformed, we’re also moving from numerous, narrow, isolated concepts to fewer, wider, joined concepts. We’re bringing our components together into wider, richer concepts, and that creates this shape in our DAG. This way when we get to the marts layer we have a robust set of components that can quickly and easily be put into any configuration to answer a variety of questions and serve specific needs. One rule of thumb to ensure you’re following this pattern on an individual model level is allowing multiple _inputs_ to a model, but **not** multiple _outputs_. Several arrows going _into_ our post-staging models is great and expected, several arrows coming _out_ is a red flag. There are absolutely situations where you need to break this rule, but it’s something to be aware of, careful about, and avoid when possible.
 :::
diff --git a/website/docs/guides/best-practices/how-we-structure/4-marts.md b/website/docs/guides/best-practices/how-we-structure/4-marts.md
index 0e22d036e58..e7a0d35c342 100644
--- a/website/docs/guides/best-practices/how-we-structure/4-marts.md
+++ b/website/docs/guides/best-practices/how-we-structure/4-marts.md
@@ -3,13 +3,17 @@ title: "Marts: Business-defined entities"
 id: "4-marts"
 ---
 
-This is the layer where everything comes together and we start to arrange all of our atoms (staging models) and molecules (intermediate models) into full-fledged cells that have identity and purpose. We sometimes like to call this the *entity* *layer* or *concept layer*, to emphasize that all our marts are meant to represent a specific entity or concept at its unique grain. For instance, an order, a customer, a territory, a click event, a payment — each of these would be represented with a distinct mart, and each row would represent a discrete instance of these concepts. Unlike in a traditional Kimball star schema though, in modern data warehousing — where storage is cheap and compute is expensive — we’ll happily borrow and add any and all data from other concepts that are relevant to answering questions about the mart’s core entity. Building the same data in multiple places, as we do with `orders` in our `customers` mart example below, is more efficient in this paradigm than repeatedly rejoining these concepts (this is a basic definition of denormalization in this context). Let’s take a look at how we approach this first layer intended expressly for exposure to end users.
+:::info
+Our guidance here diverges if you use the dbt Semantic Layer. In a project without the Semantic Layer we recommend you denormalize heavily, per the best practices below. On the other hand, if you're using the Semantic Layer, we want to stay as normalized as possible to allow MetricFlow the most flexibility. Guidance for marts in a Semantic Layer context is on the next page.
+:::
+
+This is the layer where everything comes together and we start to arrange all of our atoms (staging models) and molecules (intermediate models) into full-fledged cells that have identity and purpose. We sometimes like to call this the _entity_ _layer_ or _concept layer_, to emphasize that all our marts are meant to represent a specific entity or concept at its unique grain. For instance, an order, a customer, a territory, a click event, a payment — each of these would be represented with a distinct mart, and each row would represent a discrete instance of these concepts. Unlike in a traditional Kimball star schema though, in modern data warehousing — where storage is cheap and compute is expensive — we’ll happily borrow and add any and all data from other concepts that are relevant to answering questions about the mart’s core entity. Building the same data in multiple places, as we do with `orders` in our `customers` mart example below, is more efficient in this paradigm than repeatedly rejoining these concepts (this is a basic definition of denormalization in this context). Let’s take a look at how we approach this first layer intended expressly for exposure to end users.
 
 ### Marts: Files and folders
 
 The last layer of our core transformations is below, providing models for both `finance` and `marketing` departments.
 
-```markdown
+```shell
 models/marts
 ├── finance
 │   ├── _finance__models.yml
@@ -24,7 +28,7 @@ models/marts
 
 ✅ **Name by entity.** Use plain English to name the file based on the concept that forms the grain of the mart `customers`, `orders`. Note that for pure marts, there should not be a time dimension (`orders_per_day`) here, that is typically best captured via metrics.
 
-❌ **Build the same concept differently for different teams.** `finance_orders` and `marketing_orders` is typically considered an anti-pattern. There are, as always, exceptions — a common pattern we see is that, finance may have specific needs, for example reporting revenue to the government in a way that diverges from how the company as a whole measures revenue day-to-day. Just make sure that these are clearly designed and understandable as *separate* concepts, not departmental views on the same concept: `tax_revenue` and `revenue` not `finance_revenue` and `marketing_revenue`.
+❌ **Build the same concept differently for different teams.** `finance_orders` and `marketing_orders` is typically considered an anti-pattern. There are, as always, exceptions — a common pattern we see is that, finance may have specific needs, for example reporting revenue to the government in a way that diverges from how the company as a whole measures revenue day-to-day. Just make sure that these are clearly designed and understandable as _separate_ concepts, not departmental views on the same concept: `tax_revenue` and `revenue` not `finance_revenue` and `marketing_revenue`.
 
 ### Marts: Models
 
@@ -33,7 +37,7 @@ Finally we’ll take a look at the best practices for models within the marts di
 ```sql
 -- orders.sql
 
-with 
+with
 
 orders as  (
 
@@ -68,7 +72,7 @@ select * from orders_and_payments_joined
 ```sql
 -- customers.sql
 
-with 
+with
 
 customers as (
 
@@ -117,21 +121,15 @@ customers_and_customer_orders_joined as (
 select * from customers_and_customer_orders_joined
 ```
 
-- ✅ **Materialized as tables or incremental models.** Once we reach the marts layer, it’s time to start building not just our logic into the warehouse, but the data itself. This gives end users much faster performance for these later models that are actually designed for their use, and saves us costs recomputing these entire chains of models every time somebody refreshes a dashboard or runs a regression in python. A good general rule of thumb regarding materialization is to always start with a view (as it takes up essentially no storage and always gives you up-to-date results), once that view takes too long to practically *query*, build it into a table, and finally once that table takes too long to *build* and is slowing down your runs, [configure it as an incremental model](https://docs.getdbt.com/docs/build/incremental-models/). As always, start simple and only add complexity as necessary. The models with the most data and compute-intensive transformations should absolutely take advantage of dbt’s excellent incremental materialization options, but rushing to make all your marts models incremental by default will introduce superfluous difficulty. We recommend reading this [classic post from Tristan on the limits of incremental modeling](https://discourse.getdbt.com/t/on-the-limits-of-incrementality/303).
+- ✅ **Materialized as tables or incremental models.** Once we reach the marts layer, it’s time to start building not just our logic into the warehouse, but the data itself. This gives end users much faster performance for these later models that are actually designed for their use, and saves us costs recomputing these entire chains of models every time somebody refreshes a dashboard or runs a regression in python. A good general rule of thumb regarding materialization is to always start with a view (as it takes up essentially no storage and always gives you up-to-date results), once that view takes too long to practically _query_, build it into a table, and finally once that table takes too long to _build_ and is slowing down your runs, [configure it as an incremental model](https://docs.getdbt.com/docs/build/incremental-models/). As always, start simple and only add complexity as necessary. The models with the most data and compute-intensive transformations should absolutely take advantage of dbt’s excellent incremental materialization options, but rushing to make all your marts models incremental by default will introduce superfluous difficulty. We recommend reading this [classic post from Tristan on the limits of incremental modeling](https://discourse.getdbt.com/t/on-the-limits-of-incrementality/303).
 - ✅ **Wide and denormalized.** Unlike old school warehousing, in the modern data stack storage is cheap and it’s compute that is expensive and must be prioritized as such, packing these into very wide denormalized concepts that can provide everything somebody needs about a concept as a goal.
 - ❌ **Too many joins in one mart.** One good rule of thumb when building dbt transformations is to avoid bringing together too many concepts in a single mart. What constitutes ‘too many’ can vary. If you need to bring 8 staging models together with nothing but simple joins, that might be fine. Conversely, if you have 4 concepts you’re weaving together with some complex and computationally heavy window functions, that could be too much. You need to weigh the number of models you’re joining against the complexity of the logic within the mart, and if it’s too much to read through and build a clear mental model of then look to modularize. While this isn’t a hard rule, if you’re bringing together more than 4 or 5 concepts to create your mart, you may benefit from adding some intermediate models for added clarity. Two intermediate models that bring together three concepts each, and a mart that brings together those two intermediate models, will typically result in a much more readable chain of logic than a single mart with six joins.
 - ✅ **Build on separate marts thoughtfully.** While we strive to preserve a narrowing DAG up to the marts layer, once here things may start to get a little less strict. A common example is passing information between marts at different grains, as we saw above, where we bring our `orders` mart into our `customers` marts to aggregate critical order data into a `customer` grain. Now that we’re really ‘spending’ compute and storage by actually building the data in our outputs, it’s sensible to leverage previously built resources to speed up and save costs on outputs that require similar data, versus recomputing the same views and CTEs from scratch. The right approach here is heavily dependent on your unique DAG, models, and goals — it’s just important to note that using a mart in building another, later mart is okay, but requires careful consideration to avoid wasted resources or circular dependencies.
 
 :::tip Marts are entity-grained.
-The most important aspect of marts is that they contain all of the useful data about a *particular entity* at a granular level. That doesn’t mean we don’t bring in lots of other entities and concepts, like tons of `user` data into our `orders` mart, we do! It just means that individual `orders` remain the core grain of our table. If we start grouping `users` and `orders` along a [date spine](https://github.com/dbt-labs/dbt-utils#date_spine-source), into something like `user_orders_per_day`, we’re moving past marts into *metrics*.
+The most important aspect of marts is that they contain all of the useful data about a _particular entity_ at a granular level. That doesn’t mean we don’t bring in lots of other entities and concepts, like tons of `user` data into our `orders` mart, we do! It just means that individual `orders` remain the core grain of our table. If we start grouping `users` and `orders` along a [date spine](https://github.com/dbt-labs/dbt-utils#date_spine-source), into something like `user_orders_per_day`, we’re moving past marts into _metrics_.
 :::
 
 ### Marts: Other considerations
 
 - **Troubleshoot via tables.** While stacking views and ephemeral models up until our marts — only building data into the warehouse at the end of a chain when we have the models we really want end users to work with — is ideal in production, it can present some difficulties in development. Particularly, certain errors may seem to be surfacing in our later models that actually stem from much earlier dependencies in our model chain (ancestor models in our DAG that are built before the model throws the errors). If you’re having trouble pinning down where or what a database error is telling you, it can be helpful to temporarily build a specific chain of models as tables so that the warehouse will throw the error where it’s actually occurring.
-- **After marts: the activation layer.** In the same way that our staging models are building blocks for our marts, that also offer us direct views into specific source data, our marts are building blocks for our final outputs that also offer direct views into specific ideas. You can use marts directly, but they are equally important as components for building models in the *activation layer* after marts. This is a deep and fast-evolving topic, so we’ll cover this in a separate forthcoming guide that dives into:
-  - Metrics
-  - Reverse ETL
-  - Reporting and dashboards
-  - Data science and ML
-  - [Exposures](https://docs.getdbt.com/docs/build/exposures) (how we tie our dbt DAG into all of the above)
diff --git a/website/docs/guides/best-practices/how-we-structure/5-semantic-layer-marts.md b/website/docs/guides/best-practices/how-we-structure/5-semantic-layer-marts.md
new file mode 100644
index 00000000000..adebc4a63c7
--- /dev/null
+++ b/website/docs/guides/best-practices/how-we-structure/5-semantic-layer-marts.md
@@ -0,0 +1,48 @@
+---
+title: "Marts for the Semantic Layer"
+id: "5-semantic-layer-marts"
+---
+
+The Semantic Layer alters some fundamental principles of how you organize your project. Using dbt without the Semantic Layer necessitates creating the most useful combinations of your building block components into wide, denormalized marts. On the other hand, the Semantic Layer leverages MetricFlow to denormalize every possible combination of components we've encoded dynamically. As such we're better served to bring more normalized models through from the logical layer into the Semantic Layer to maximize flexibility. This section will assume familiarity with the best practices laid out in the [How we build our metrics](https://docs.getdbt.com/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro) guide, so check that out first for a more hands-on introduction to the Semantic Layer.
+
+## Semantic Layer: Files and folders
+
+- 2️⃣ There are two major factors that alter our recommendations for the Semantic Layer:
+  - 📝 There is **more YAML** in the form of **semantic models and metrics**.
+  - ⏫ We may **use a staging model directly** if it forms a complete normalized component, and it will not have a mart at all.
+- 💪 This combination means models at **both the staging and marts layer** may participate in the Semantic Layer and use **more powerful, expansive YAML configuration**.
+- 🔁 Given this, for projects using the Semantic Layer we recommend a **YAML-file-per-model approach**, as below.
+
+```shell
+models
+├── marts
+│   ├── customers.sql
+│   ├── customers.yml
+│   ├── orders.sql
+│   └── orders.yml
+└── staging
+    ├── __sources.yml
+    ├── stg_customers.sql
+    ├── stg_customers.yml
+    ├── stg_locations.sql
+    ├── stg_locations.yml
+    ├── stg_order_items.sql
+    ├── stg_order_items.yml
+    ├── stg_orders.sql
+    ├── stg_orders.yml
+    ├── stg_products.sql
+    ├── stg_products.yml
+    ├── stg_supplies.sql
+    └── stg_supplies.yml
+```
+
+## When to make a mart
+
+- ❓ If we can go directly to staging models and it's better to serve normalized models to the Semantic Layer, then when, where, and why would we make a mart?
+  - 🕰️ We have models that have measures but no time dimension to aggregate against. The details of this are laid out in the [Semantic Layer guide](https://docs.getdbt.com/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro) but in short, we need a time dimension to aggregate against in MetricFlow. Dimensional tables that
+  - 🧱 We want to **materialize** our model in various ways.
+  - 👯 We want to **version** our model.
+  - 🛒 We have various related models that make more sense as **one wider component**.
+  - 1️⃣ We have similar models across multiple data sources that make more sense **unioned together**.
+  - ⌚ We have models in our project we **need to time to refactor** but want to serve up to the Semantic Layer quickly.
+- 🌍 Any of the above and more are great reasons to build a mart. Analytics engineering is about **creativity and problem solving**, so these are not prescriptive rules, **there are many reasons to build marts** in any project. The most important takeaway is that you don't **_have to_** if you're using the Semantic Layer.
diff --git a/website/docs/guides/best-practices/how-we-structure/5-the-rest-of-the-project.md b/website/docs/guides/best-practices/how-we-structure/6-the-rest-of-the-project.md
similarity index 93%
rename from website/docs/guides/best-practices/how-we-structure/5-the-rest-of-the-project.md
rename to website/docs/guides/best-practices/how-we-structure/6-the-rest-of-the-project.md
index 2a6c7399adb..4082f92b932 100644
--- a/website/docs/guides/best-practices/how-we-structure/5-the-rest-of-the-project.md
+++ b/website/docs/guides/best-practices/how-we-structure/6-the-rest-of-the-project.md
@@ -1,6 +1,6 @@
 ---
 title: "The rest of the project"
-id: "5-the-rest-of-the-project"
+id: "6-the-rest-of-the-project"
 description: The rest of the project.
 displayText: The rest of the project.
 hoverSnippet: The rest of the project.
@@ -10,7 +10,7 @@ hoverSnippet: The rest of the project.
 
 So far we’ve focused on the `models` folder, the primary directory of our dbt project. Next, we’ll zoom out and look at how the rest of our project files and folders fit in with this structure, starting with how we approach YAML configuration files.
 
-```markdown
+```shell
 models
 ├── intermediate
 │   └── finance
@@ -51,7 +51,7 @@ When structuring your YAML configuration files in a dbt project, you want to bal
   - YAML files don’t need unique names in the way that SQL model files do, but including the directory (instead of simply `_sources.yml` in each folder), means you can fuzzy find the right file more quickly.
   - We’ve recommended several different naming conventions over the years, most recently calling these `schema.yml` files. We’ve simplified to recommend that these simply be labelled based on the YAML dictionary that they contain.
   - If you utilize [doc blocks](https://docs.getdbt.com/docs/collaborate/documentation#using-docs-blocks) in your project, we recommend following the same pattern, and creating a `_[directory]__docs.md` markdown file per directory containing all your doc blocks for that folder of models.
-- ❌ **Config per project.** Some people put *all* of their source and model YAML into one file.  While you can technically do this, and while it certainly simplifies knowing what file the config you’re looking for will be in (as there is only one file), it makes it much harder to find specific configurations within that file. We recommend balancing those two concerns.
+- ❌ **Config per project.** Some people put _all_ of their source and model YAML into one file. While you can technically do this, and while it certainly simplifies knowing what file the config you’re looking for will be in (as there is only one file), it makes it much harder to find specific configurations within that file. We recommend balancing those two concerns.
 - ⚠️ **Config per model.** On the other end of the spectrum, some people prefer to create one YAML file per model. This presents less of an issue than a single monolith file, as you can quickly search for files, know exactly where specific configurations exist, spot models without configs (and thus without tests) by looking at the file tree, and various other advantages. In our opinion, the extra files, tabs, and windows this requires creating, copying from, pasting to, closing, opening, and managing creates a somewhat slower development experience that outweighs the benefits. Defining config per directory is the most balanced approach for most projects, but if you have compelling reasons to use config per model, there are definitely some great projects that follow this paradigm.
 - ✅ **Cascade configs.** Leverage your `dbt_project.yml` to set default configurations at the directory level. Use the well-organized folder structure we’ve created thus far to define the baseline schemas and materializations, and use dbt’s cascading scope priority to define variations to this. For example, as below, define your marts to be materialized as tables by default, define separate schemas for our separate subfolders, and any models that need to use incremental materialization can be defined at the model level.
 
@@ -73,12 +73,12 @@ models:
 ```
 
 :::tip Define your defaults.
-One of the many benefits this consistent approach to project structure confers to us is this ability to cascade default behavior. Carefully organizing our folders and defining configuration at that level whenever possible frees us from configuring things like schema and materialization in every single model (not very DRY!) — we only need to configure exceptions to our general rules. Tagging is another area this principle comes into play. Many people new to dbt will rely on tags rather than a rigorous folder structure, and quickly find themselves in a place where every model *requires* a tag. This creates unnecessary complexity. We want to lean on our folders as our primary selectors and grouping mechanism, and use tags to define groups that are *exceptions.* A folder-based selection like **`dbt build --select marts.marketing` is much simpler than trying to tag every marketing-related model, hoping all developers remember to add that tag for new models, and using `dbt build --select tag:marketing`.
+One of the many benefits this consistent approach to project structure confers to us is this ability to cascade default behavior. Carefully organizing our folders and defining configuration at that level whenever possible frees us from configuring things like schema and materialization in every single model (not very DRY!) — we only need to configure exceptions to our general rules. Tagging is another area this principle comes into play. Many people new to dbt will rely on tags rather than a rigorous folder structure, and quickly find themselves in a place where every model _requires_ a tag. This creates unnecessary complexity. We want to lean on our folders as our primary selectors and grouping mechanism, and use tags to define groups that are _exceptions._ A folder-based selection like \*\*`dbt build --select marts.marketing` is much simpler than trying to tag every marketing-related model, hoping all developers remember to add that tag for new models, and using `dbt build --select tag:marketing`.
 :::
 
 ### How we use the other folders
 
-```yaml
+```shell
 jaffle_shop
 ├── analyses
 ├── seeds
@@ -88,7 +88,7 @@ jaffle_shop
 │   └── cents_to_dollars.sql
 ├── snapshots
 └── tests
-    └── assert_positive_value_for_total_amount.sql
+└── assert_positive_value_for_total_amount.sql
 ```
 
 We’ve focused heavily thus far on the primary area of action in our dbt project, the `models` folder. As you’ve probably observed though, there are several other folders in our project. While these are, by design, very flexible to your needs, we’ll discuss the most common use cases for these other folders to help get you started.
@@ -111,6 +111,6 @@ One important, growing consideration in the analytics engineering ecosystem is h
 
 ## Final considerations
 
-Overall, consistency is more important than any of these specific conventions. As your project grows and your experience with dbt deepens, you will undoubtedly find aspects of the above structure you want to change. While we recommend this approach for the majority of projects, every organization is unique! The only dogmatic advice we’ll put forward here is that when you find aspects of the above structure you wish to change, think intently about your reasoning and document for your team *how* and *why* you are deviating from these conventions. To that end, we highly encourage you to fork this guide and add it to your project’s README, wiki, or docs so you can quickly create and customize those artifacts.
+Overall, consistency is more important than any of these specific conventions. As your project grows and your experience with dbt deepens, you will undoubtedly find aspects of the above structure you want to change. While we recommend this approach for the majority of projects, every organization is unique! The only dogmatic advice we’ll put forward here is that when you find aspects of the above structure you wish to change, think intently about your reasoning and document for your team _how_ and _why_ you are deviating from these conventions. To that end, we highly encourage you to fork this guide and add it to your project’s README, wiki, or docs so you can quickly create and customize those artifacts.
 
 Finally, we emphasize that this guide is a living document! It will certainly change and grow as dbt and dbt Labs evolve. We invite you to join in — discuss, comment, and contribute regarding suggested changes or new elements to cover.
diff --git a/website/sidebars.js b/website/sidebars.js
index 0faa37afff0..62262703055 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -859,7 +859,8 @@ const sidebarSettings = {
             "guides/best-practices/how-we-structure/2-staging",
             "guides/best-practices/how-we-structure/3-intermediate",
             "guides/best-practices/how-we-structure/4-marts",
-            "guides/best-practices/how-we-structure/5-the-rest-of-the-project",
+            "guides/best-practices/how-we-structure/5-semantic-layer-marts",
+            "guides/best-practices/how-we-structure/6-the-rest-of-the-project",
           ],
         },
         {

From 3851bd7b703c65131cdf713514e9cd863d50d67c Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Mon, 7 Aug 2023 20:06:42 -0400
Subject: [PATCH 580/703] Update
 website/docs/docs/cloud/cloud-cli-installation.md

---
 website/docs/docs/cloud/cloud-cli-installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index f6f8fa78d38..c300f6478a1 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -102,7 +102,7 @@ project-id: '123456'
 
 ## Using dbt Cloud CLI
 
-**More informaiton coming soon**
+**Coming soon**
 
 ### Glossary
 

From 4d2cb3711ebed6d2d3d25df8e5374581dfba6b1b Mon Sep 17 00:00:00 2001
From: Tania <92768464+Tonayya@users.noreply.github.com>
Date: Tue, 8 Aug 2023 13:39:22 +1000
Subject: [PATCH 581/703] Update database.md

cross-database referencing is now available with Redshift since the introduction of setting `ra3_node` to true as default which supports this functionality. GH issue [here](https://github.com/dbt-labs/dbt-redshift/issues/94).
---
 website/docs/reference/resource-configs/database.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/database.md b/website/docs/reference/resource-configs/database.md
index 9b7cd1b39d3..118c72b38e9 100644
--- a/website/docs/reference/resource-configs/database.md
+++ b/website/docs/reference/resource-configs/database.md
@@ -43,4 +43,4 @@ seeds:
 
 ## Warehouse specific information
 * BigQuery: `project` and `database` are interchangeable
-* Redshift: Cross-database queries are not possible in Redshift. As such, dbt will return a Database Error if you use 'Database A' for a seed file and try to `{{ ref() }}` that seed file (or its database object) in a model using 'Database B'. This error would only be found at runtime.
+

From 24f1a07bba746e86dea3fef2f5b8b0861ebf5223 Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Mon, 7 Aug 2023 21:37:46 -0700
Subject: [PATCH 582/703] Update sl-jdbc.md

I realized the examples in the jdbc api query table are missing single quotes! I added them
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 21b5f4f921a..ef10cf7b973 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -135,8 +135,8 @@ To query metric values, here are the following parameters that are available:
 
 | Parameter | Description  | Example    | Type |
 | --------- | -----------| ------------ | -------------------- |
-| `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=[revenue]` | Required    |
-| `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=[user__country, metric_time]`     | Optional   |
+| `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=['revenue']` | Required    |
+| `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=['user__country', 'metric_time']`     | Optional   |
 | `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | ```group_by=[`Dimension('metric_time').``` <br/> ```grain('week\|day\|month\|quarter\|year')]``` | Optional     |
 | `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where="Dimension('customer__country') = 'US')"`   | Optional   |
 | `limit`   | Limit the data returned    | `limit=10` | Optional  |

From 03f08b2b7c3e5f4e74441abe4e0e5fcff8378e99 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 8 Aug 2023 10:04:16 +0100
Subject: [PATCH 583/703] Update sl-jdbc.md

remove excess apostrophe
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index ef10cf7b973..5606efa744b 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -146,7 +146,7 @@ To query metric values, here are the following parameters that are available:
 
 ## Note on time dimensions and `metric_time`
 
-You will notice that in the list of dimensions for all metrics, there is a dimension called `metric_time`'. `Metric_time` is a reserved keyword for the measure-specific aggregation time dimensions. For any time-series metric, the `metric_time` keyword should always be available for use in queries. This is a common dimension across *all* metrics in a semantic graph. 
+You will notice that in the list of dimensions for all metrics, there is a dimension called `metric_time`. `Metric_time` is a reserved keyword for the measure-specific aggregation time dimensions. For any time-series metric, the `metric_time` keyword should always be available for use in queries. This is a common dimension across *all* metrics in a semantic graph. 
 
 You can look at a single metric or hundreds of metrics, and if you group by `metric_time`, it will always give you the correct time series.
 
@@ -157,7 +157,7 @@ Note that `metric_time` should be available in addition to any other time dimens
 
 ## Examples
 
-Use the following examples to help you get started with the JDBC API
+Refer to the following examples to help you get started with the JDBC API.
 
 ### Fetch metadata for metrics
 

From bc956c47dee225580c8f3d564747976ca34e4e4d Mon Sep 17 00:00:00 2001
From: Winslow Powers <91896902+wpowers-dbt@users.noreply.github.com>
Date: Tue, 8 Aug 2023 05:12:00 -0400
Subject: [PATCH 584/703] add additional webhooks timeout info (#3836)

## What are you changing in this pull request and why?
Add some more info to clarify how a webhooks time out is handled by the
webhooks dispatcher.

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [ ] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [ ] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding new pages (delete if not applicable):
- [ ] Add page to `website/sidebars.js`
- [ ] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/deploy/webhooks.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/webhooks.md b/website/docs/docs/deploy/webhooks.md
index 4ce089daa89..b4ce7195363 100644
--- a/website/docs/docs/deploy/webhooks.md
+++ b/website/docs/docs/deploy/webhooks.md
@@ -18,7 +18,7 @@ You can create webhooks for these events from the [dbt Cloud web-based UI](#crea
 
 dbt Cloud retries sending each event five times. dbt Cloud keeps a log of each webhook delivery for 30 days. Every webhook has its own **Recent Deliveries** section, which lists whether a delivery was successful or failed at a glance. 
 
-A webhook in dbt Cloud has a timeout of 10 seconds. This means that if the endpoint doesn't respond within 10 seconds, the webhook processor will time out.
+A webhook in dbt Cloud has a timeout of 10 seconds. This means that if the endpoint doesn't respond within 10 seconds, the webhook processor will time out. This can result in a situation where the client responds successfully after the 10 second timeout and records a success status while the dbt cloud webhooks system will interpret this as a failure.
 
 :::tip Videos 
 If you're interested in course learning with videos, check out the [Webhooks on-demand course](https://courses.getdbt.com/courses/webhooks) from dbt Labs.

From ca2cb7fe99d0fc7ef076b3a6f71a9b1cac108859 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 8 Aug 2023 11:33:23 +0100
Subject: [PATCH 585/703] fix typos, yaml indention, and removed is_primary

---
 website/docs/docs/build/cumulative-metrics.md | 58 ++++++++--------
 website/docs/docs/build/dimensions.md         | 67 ++++---------------
 website/docs/docs/build/join-logic.md         |  1 -
 website/docs/docs/build/measures.md           |  1 -
 website/docs/docs/build/semantic-models.md    | 30 ++++-----
 website/docs/docs/build/validation.md         |  2 +-
 website/snippets/_sl-partner-links.md         | 19 +++---
 7 files changed, 66 insertions(+), 112 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index 9b1434f7f52..08e2c6f0adf 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -23,7 +23,6 @@ metrics:
       grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next. 
       # Optional. Can not be used with window. 
 
-
 ```
 
 :::info MetricFlow time spine required
@@ -75,9 +74,10 @@ Suppose the underlying measure `customers` which is configured to count the uniq
 
 ```yaml
 measures:
-      - name: customers
-        expr: customer_id
-        agg: count_distinct
+  - name: customers
+    expr: customer_id
+    agg: count_distinct
+
 ```
 
 We can write a cumulative metric `weekly_customers` as such: 
@@ -118,31 +118,29 @@ Suppose you (a subscription-based company for the sake of this example) have an
 Using cumulative metrics without specifying a window, you can calculate running totals for metrics like the count of active subscriptions and revenue at any point in time. The following configuration YAML displays creating such cumulative metrics to obtain current revenue or total number of active subscriptions as a cumulative sum:
 
 ```yaml
-measures: 
-  - name: revenue 
-    description: Total revenue 
-    agg: sum 
-    expr: revenue 
-  - name: subscription_count 
-    description: Count of active subscriptions 
-    agg: sum 
+measures:
+  - name: revenue
+    description: Total revenue
+    agg: sum
+    expr: revenue
+  - name: subscription_count
+    description: Count of active subscriptions
+    agg: sum
     expr: event_type
+metrics:
+  - name: current_revenue
+    description: Current revenue
+    label: Current Revenue
+    type: cumulative
+    type_params:
+      measure: revenue
+  - name: active_subscriptions
+    description: Count of active subscriptions
+    label: Active Subscriptions
+    type: cumulative
+    type_params:
+      measure: subscription_count
 
-metrics: 
-- name: current_revenue
-  description: Current revenue 
-  label: Current Revenue
-  type: cumulative 
-  type_params: 
-    measures: 
-      - revenue
-- name: active_subscriptions 
-  description: Count of active subscriptions 
-  label: Active Subscriptions
-  type: cumulative 
-  type_params: 
-    measures: 
-      - subscription_count
 ```
 
 </TabItem>
@@ -165,14 +163,14 @@ We can compare the difference between a 1-month window and a monthly grain to da
 
 ```yaml
 metrics:
- - name: cumulative_order_total_l1m #For this metric, we use a window of 1 month 
-    label: Cumulative Order total (L1M)   
+  - name: cumulative_order_total_l1m  #For this metric, we use a window of 1 month 
+    label: Cumulative Order total (L1M)
     description: Trailing 1 month cumulative order amount
     type: cumulative
     type_params:
       measure: order_total
       window: 1 month
-  - name: cumulative_order_total_mtd #For this metric, we use a monthly grain to date 
+  - name: cumulative_order_total_mtd   #For this metric, we use a monthly grain to date 
     label: Cumulative Order total (MTD)
     description: The month to date value of all orders
     type: cumulative
diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index fe2fa802e17..5b974c908c9 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -61,7 +61,7 @@ semantic_models:
       expr: case when quantity > 10 then true else false end
 ```
 
-Metricflow requires that all dimensions have a primary entity. If your data source does not have a primary entity, you will need to specfy one. 
+MetricFlow requires that all dimensions have a primary entity. If your data source does not have a primary entity, you will need to specify one. 
 
 ```yaml:
 semantic_model:
@@ -76,7 +76,6 @@ semantic_model:
       create_metric: true
   primary_entity: booking_id
   ```
-
 ## Dimensions types
 
 Dimensions have 2 types. This section further explains the definitions and provides examples.
@@ -97,54 +96,20 @@ dimensions:
 
 ### Time
 
-Time has additional parameters specified under the `type_params` section.
-
-:::tip use datetime data type if using BigQuery
-To use BigQuery as your data platform, time dimensions columns need to be in the datetime data type. If they are stored in another type, you can cast them to datetime using the `expr` property. Time dimensions are used to group metrics by different levels of time, such as day, week, month, quarter, and year. MetricFlow supports these granularities, which can be specified using the `time_granularity` parameter.
-:::
+Time has additional parameters specified under the `type_params` section. When you query one or more metrics in MetricFlow using the CLI, the default time dimension for a single metric is the primary time dimension, which you can refer to as `metric_time` or use the dimensions' name. 
 
-<Tabs>
+You can use multiple time groups in separate metrics. For example, the `users_created` metric uses `created_at`, and the `users_deleted` metric uses `deleted_at`:
 
-<TabItem value="is_primary" label="is_primary">
 
-To specify the default time dimensions for a measure or metric in MetricFlow, set the `agg_time_dimension` in the `defaults` section. To override the default and aggregation on diffrent time dimension, set the `agg_time_dimension` parameter on a measure.
-
-In the provided example, the semantic model has two-time groups, `created_at` and `deleted_at`, with `created_at` being the default time dimension. The `users_deleted` measure overrides the default and uses `deleted_at` as its time group. 
-
-```yaml
-dimensions: 
-  - name: created_at
-    type: time
-    expr: date_trunc('day', ts_created) #ts_created is the underlying column name from the table 
-    is_partition: True 
-    type_params:
-      is_primary: True
-      time_granularity: day
-  - name: deleted_at
-    type: time
-    expr: date_trunc('day', ts_deleted) #ts_deleted is the underlying column name from the table 
-    is_partition: True 
-    type_params:
-      is_primary: False
-      time_granularity: day
-
-measures:
-  - name: users_deleted
-    expr: 1
-    agg: sum
-    agg_time_dimension: deleted_at
-  - name: users_created
-    expr: 1
-    agg: sum
+```bash
+mf query --metrics users_created,users_deleted --dimensions metric_time --order metric_time 
 ```
 
-When querying one or more metrics in MetricFlow using the CLI, the default time dimension for a single metric is the primary time dimension, which can be referred to as metric_time or the dimensions' name. Multiple time groups can be used in separate metrics, such as users_created which uses created_at, and users_deleted which uses deleted_at.
-
-      ```
-       mf query --metrics users_created,users_deleted --dimensions metric_time --order metric_time 
-      ```
+:::tip use datetime data type if using BigQuery
+To use BigQuery as your data platform, time dimensions columns need to be in the datetime data type. If they are stored in another type, you can cast them to datetime using the `expr` property. Time dimensions are used to group metrics by different levels of time, such as day, week, month, quarter, and year. MetricFlow supports these granularities, which can be specified using the `time_granularity` parameter.
+:::
 
-</TabItem>
+<Tabs>
 
 <TabItem value="time_gran" label="time_granularity">
 
@@ -161,14 +126,12 @@ dimensions:
     expr: date_trunc('day', ts_created) #ts_created is the underlying column name from the table 
     is_partition: True 
     type_params:
-      is_primary: True
       time_granularity: day
   - name: deleted_at
     type: time
     expr: date_trunc('day', ts_deleted) #ts_deleted is the underlying column name from the table 
     is_partition: True 
     type_params:
-      is_primary: False
       time_granularity: day
 
 measures:
@@ -185,15 +148,16 @@ measures:
 
 <TabItem value="is_partition" label="is_partition">
 
-Use `is_partition: True` to indicate that a dimension exists over a specific time window. For example, a date-partitioned dimensional table. When you query metrics from different tables, the Semantic Layer will use this parameter to ensure that the correct dimensional values are joined to measures. 
+Use `is_partition: True` to show that a dimension exists over a specific time window. For example, a date-partitioned dimensional table. When you query metrics from different tables, the dbt Semantic Layer uses this parameter to ensure that the correct dimensional values are joined to measures. 
 
-In addition, MetricFlow allows for easy aggregation of metrics at query time. For example, you can aggregate the `messages_per_month` measure, where the original `time_granularity` of the time dimensions `metrics_time`, at a yearly granularity by specifying it in the query in the CLI.
+You can also use `is_partition` for `type: categorical` [categorical](#categorical) dimensions as well.
 
-```
+MetricFlow enables metric aggregation during query time. For example, you can aggregate the `messages_per_month` measure. If you originally had a `time_granularity` for the time dimensions `metric_time`, you can specify a yearly granularity for aggregation in your CLI query:
+
+```bash
 mf query --metrics messages_per_month --dimensions metric_time --order metric_time --time-granularity year  
 ```
 
-
 ```yaml
 dimensions: 
   - name: created_at
@@ -201,14 +165,12 @@ dimensions:
     expr: date_trunc('day', ts_created) #ts_created is the underlying column name from the table 
     is_partition: True 
     type_params:
-      is_primary: True
       time_granularity: day
   - name: deleted_at
     type: time
     expr: date_trunc('day', ts_deleted) #ts_deleted is the underlying column name from the table 
     is_partition: True 
     type_params:
-      is_primary: False
       time_granularity: day
 
 measures:
@@ -225,7 +187,6 @@ measures:
 
 </Tabs>
 
-
 ### SCD Type II 
 
 :::caution 
diff --git a/website/docs/docs/build/join-logic.md b/website/docs/docs/build/join-logic.md
index 96426424c87..9039822c9fd 100644
--- a/website/docs/docs/build/join-logic.md
+++ b/website/docs/docs/build/join-logic.md
@@ -114,7 +114,6 @@ semantic_models:
       - name: metric_time
         type: time
         type_params:
-        is_primary: true
   - name: user_signup
     entities:
       - name: user_id
diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index a80ff9f5e3e..4ea96d891cc 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -198,7 +198,6 @@ semantic_models:
         type: time
         expr: date_transaction
         type_params:
-          is_primary: True
           time_granularity: day
 
     measures: 
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index a5d7ef29f7e..bc188eed834 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -30,25 +30,26 @@ Semantic models have 6 components and this page explains the definitions with so
 ## Semantic models components
 
 The complete spec for semantic models is below:
+
 ```yaml
 semantic_models:
-  - name: the_name_of_the_semantic_model      ## Required
-    description: same as always               ## Optional
-    model: ref('some_model')                  ## Required
-    defaults:                                 ## Required
-        agg_time_dimension: dimension_name    ## Required if the model contains dimensions
-    entities:                                 ## Required
-       - see more information in entities
-    measures:                                 ## Optional
-       - see more information in measures section
-    dimensions:                               ## Required
-       - see more information in dimensions section
-    primary_entity: if the semantic model has no primary entity, then this property is required. #Optional if a primary entity exists, otherwise Required
+  - name: the_name_of_the_semantic_model  ## Required
+    description: same as always           ## Optional
+    model: ref('some_model')              ## Required
+    defaults:                             ## Required
+      agg_time_dimension: dimension_name  ## Required if the model contains dimensions
+    entities:                             ## Required
+      - see more information in entities
+    measures:                             ## Optional
+      - see more information in measures section
+    dimensions:                           ## Required
+      - see more information in dimensions section
+    primary_entity: >-
+      if the semantic model has no primary entity, then this property is required. #Optional if a primary entity exists, otherwise Required
 ```
-
 The following example displays a complete configuration and detailed descriptions of each field:
 
-```yml
+```yaml
 semantic_models:
   - name: transaction # A semantic model with the name Transactions
     model: ref('fact_transactions') # References the dbt model named `fact_transactions`
@@ -64,7 +65,6 @@ semantic_models:
         type: foreign
         expr: customer_id
 
-
     dimensions: # dimensions are qualitative values such as names, dates, or geographical data. They provide context to metrics and allow "metric by group" data slicing.
       - name: transaction_date
         type: time
diff --git a/website/docs/docs/build/validation.md b/website/docs/docs/build/validation.md
index 808d054f021..ad485850d23 100644
--- a/website/docs/docs/build/validation.md
+++ b/website/docs/docs/build/validation.md
@@ -18,7 +18,7 @@ The code that handles validation [can be found here](https://github.com/dbt-labs
 
 ## Validations command
 
-You can run validations from the CLI with the following commands:
+You can run validations from the CLI with the following [MetricFlow commands](/docs/build/metricflow-cli):
 
 ```bash
 mf validate-configs
diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index bd91f00ccd8..40456fcfc17 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -1,14 +1,11 @@
+<!-- turn this list into sections once more docs are provided -->
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
 
-- **Hex** &mdash; Public docs are coming soon, however, you can refer to this [Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info on how to integrate with Hex.
-
-- **Mode** &mdash; To connect to Mode, refer to [Mode documentation](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
-
-- **Google Sheets** &mdash; Integration with Google Sheets coming soon
-
-- **Tools that allows you to write SQL** &mdash; They must meet one of the two criteria: 
-  * Supports a generic JDBC driver option (such as DataGrip) or 
-  * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
-
-**Note**, before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only and Metadata Only service token. 
+1. **Hex** &mdash; Public docs are coming soon, refer to [this Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info on how to integrate with Hex. 
+1. **Mode** &mdash; Refer to the [Mode docs](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer) for info on how to integrate with Mode.
+1. **Google Sheets** &mdash; Google Sheets integration coming soon.
+1. **Tools that allows you to write SQL** &mdash; They must meet one of the two criteria: 
+    * Supports a generic JDBC driver option (such as DataGrip) or 
+    * Supports Dremio and uses ArrowFlightSQL driver version 12.0.0 or higher.
 
+Before you connect to these tools, you'll need to first [set up the dbt Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) and [generate a service token](/docs/dbt-cloud-apis/service-tokens) to create a Semantic Layer Only and Metadata Only service token. 

From e3323fadc9106d80c8a06e56cd91b38a616ab1a1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 8 Aug 2023 11:57:20 +0100
Subject: [PATCH 586/703] add intro do parameters

---
 website/docs/docs/build/dimensions.md | 35 +++++++++++++++------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index 5b974c908c9..a73515e30eb 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -96,6 +96,11 @@ dimensions:
 
 ### Time
 
+
+:::tip use datetime data type if using BigQuery
+To use BigQuery as your data platform, time dimensions columns need to be in the datetime data type. If they are stored in another type, you can cast them to datetime using the `expr` property. Time dimensions are used to group metrics by different levels of time, such as day, week, month, quarter, and year. MetricFlow supports these granularities, which can be specified using the `time_granularity` parameter.
+:::
+
 Time has additional parameters specified under the `type_params` section. When you query one or more metrics in MetricFlow using the CLI, the default time dimension for a single metric is the primary time dimension, which you can refer to as `metric_time` or use the dimensions' name. 
 
 You can use multiple time groups in separate metrics. For example, the `users_created` metric uses `created_at`, and the `users_deleted` metric uses `deleted_at`:
@@ -105,19 +110,21 @@ You can use multiple time groups in separate metrics. For example, the `users_cr
 mf query --metrics users_created,users_deleted --dimensions metric_time --order metric_time 
 ```
 
-:::tip use datetime data type if using BigQuery
-To use BigQuery as your data platform, time dimensions columns need to be in the datetime data type. If they are stored in another type, you can cast them to datetime using the `expr` property. Time dimensions are used to group metrics by different levels of time, such as day, week, month, quarter, and year. MetricFlow supports these granularities, which can be specified using the `time_granularity` parameter.
-:::
+You can set `is_partition` for time or categorical dimensions to define specific time spans. Additionally, use the `type_params` section to set `time_granularity` to adjust aggregation detail (like daily, weekly, and so on):
 
 <Tabs>
 
-<TabItem value="time_gran" label="time_granularity">
+<TabItem value="is_partition" label="is_partition">
 
-`time_granularity` specifies the smallest level of detail that a measure or metric should be reported at, such as daily, weekly, monthly, quarterly, or yearly. Different granularity options are available, and each metric must have a specified granularity. For example, a metric that is specified with weekly granularity couldn't be aggregated to a daily grain. 
+Use `is_partition: True` to show that a dimension exists over a specific time window. For example, a date-partitioned dimensional table. When you query metrics from different tables, the dbt Semantic Layer uses this parameter to ensure that the correct dimensional values are joined to measures. 
 
-The current options for time granularity are day, week, month, quarter, and year. 
+You can also use `is_partition` for [categorical](#categorical) dimensions as well.
 
-Aggregation between metrics with different granularities is possible, with the Semantic Layer returning results at the highest granularity by default. For example, when querying two metrics with daily and monthly granularity, the resulting aggregation will be at the monthly level.
+MetricFlow enables metric aggregation during query time. For example, you can aggregate the `messages_per_month` measure. If you originally had a `time_granularity` for the time dimensions `metric_time`, you can specify a yearly granularity for aggregation in your CLI query:
+
+```bash
+mf query --metrics messages_per_month --dimensions metric_time --order metric_time --time-granularity year  
+```
 
 ```yaml
 dimensions: 
@@ -146,17 +153,13 @@ measures:
 
 </TabItem>
 
-<TabItem value="is_partition" label="is_partition">
-
-Use `is_partition: True` to show that a dimension exists over a specific time window. For example, a date-partitioned dimensional table. When you query metrics from different tables, the dbt Semantic Layer uses this parameter to ensure that the correct dimensional values are joined to measures. 
+<TabItem value="time_gran" label="time_granularity">
 
-You can also use `is_partition` for `type: categorical` [categorical](#categorical) dimensions as well.
+`time_granularity` specifies the smallest level of detail that a measure or metric should be reported at, such as daily, weekly, monthly, quarterly, or yearly. Different granularity options are available, and each metric must have a specified granularity. For example, a metric that is specified with weekly granularity couldn't be aggregated to a daily grain. 
 
-MetricFlow enables metric aggregation during query time. For example, you can aggregate the `messages_per_month` measure. If you originally had a `time_granularity` for the time dimensions `metric_time`, you can specify a yearly granularity for aggregation in your CLI query:
+The current options for time granularity are day, week, month, quarter, and year. 
 
-```bash
-mf query --metrics messages_per_month --dimensions metric_time --order metric_time --time-granularity year  
-```
+Aggregation between metrics with different granularities is possible, with the Semantic Layer returning results at the highest granularity by default. For example, when querying two metrics with daily and monthly granularity, the resulting aggregation will be at the monthly level.
 
 ```yaml
 dimensions: 
@@ -176,7 +179,7 @@ dimensions:
 measures:
   - name: users_deleted
     expr: 1
-    agg: sum
+    agg: sum 
     agg_time_dimension: deleted_at
   - name: users_created
     expr: 1

From 576de30a0d91cee417a35766774360a95827ff8a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 8 Aug 2023 12:36:05 +0100
Subject: [PATCH 587/703] remove beta file

---
 website/docs/faqs/Environments/beta-release.md | 7 -------
 website/static/_redirects                      | 2 ++
 2 files changed, 2 insertions(+), 7 deletions(-)
 delete mode 100644 website/docs/faqs/Environments/beta-release.md

diff --git a/website/docs/faqs/Environments/beta-release.md b/website/docs/faqs/Environments/beta-release.md
deleted file mode 100644
index 5eef07d3510..00000000000
--- a/website/docs/faqs/Environments/beta-release.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-title: What is a beta release?
-description: "How to try out beta features"
-sidebar_label: 'What is a beta release?'
-id: beta-release
----
-This is a chance to try out brand-new functionality. You get to start planning for use cases that the next minor version will unlock. We get to hear from you about unexpected behavior and nasty bugs, so that the release candidate has more polish and fewer surprises.
diff --git a/website/static/_redirects b/website/static/_redirects
index ba9d00d37f6..cc72cfc8d47 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,3 +1,5 @@
+/faqs/environments/beta-release  /docs/dbt-versions/product-lifecycles 301
+
 /docs/deploy/slim-ci-jobs /docs/deploy/ci-jobs 301
 
 ## semantic layer

From 34b1d173909e533fe14b3f17e74f5fbfb92bf4cd Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 8 Aug 2023 12:37:58 +0100
Subject: [PATCH 588/703] Revert "remove beta file"

This reverts commit 576de30a0d91cee417a35766774360a95827ff8a.
---
 website/docs/faqs/Environments/beta-release.md | 7 +++++++
 website/static/_redirects                      | 2 --
 2 files changed, 7 insertions(+), 2 deletions(-)
 create mode 100644 website/docs/faqs/Environments/beta-release.md

diff --git a/website/docs/faqs/Environments/beta-release.md b/website/docs/faqs/Environments/beta-release.md
new file mode 100644
index 00000000000..5eef07d3510
--- /dev/null
+++ b/website/docs/faqs/Environments/beta-release.md
@@ -0,0 +1,7 @@
+---
+title: What is a beta release?
+description: "How to try out beta features"
+sidebar_label: 'What is a beta release?'
+id: beta-release
+---
+This is a chance to try out brand-new functionality. You get to start planning for use cases that the next minor version will unlock. We get to hear from you about unexpected behavior and nasty bugs, so that the release candidate has more polish and fewer surprises.
diff --git a/website/static/_redirects b/website/static/_redirects
index cc72cfc8d47..ba9d00d37f6 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,5 +1,3 @@
-/faqs/environments/beta-release  /docs/dbt-versions/product-lifecycles 301
-
 /docs/deploy/slim-ci-jobs /docs/deploy/ci-jobs 301
 
 ## semantic layer

From c30dbe28d64fa3107b4656110e1a84a420e66720 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Tue, 8 Aug 2023 12:40:31 +0100
Subject: [PATCH 589/703] rmove beta file

---
 website/docs/faqs/Environments/beta-release.md | 7 -------
 website/static/_redirects                      | 5 ++++-
 2 files changed, 4 insertions(+), 8 deletions(-)
 delete mode 100644 website/docs/faqs/Environments/beta-release.md

diff --git a/website/docs/faqs/Environments/beta-release.md b/website/docs/faqs/Environments/beta-release.md
deleted file mode 100644
index 5eef07d3510..00000000000
--- a/website/docs/faqs/Environments/beta-release.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-title: What is a beta release?
-description: "How to try out beta features"
-sidebar_label: 'What is a beta release?'
-id: beta-release
----
-This is a chance to try out brand-new functionality. You get to start planning for use cases that the next minor version will unlock. We get to hear from you about unexpected behavior and nasty bugs, so that the release candidate has more polish and fewer surprises.
diff --git a/website/static/_redirects b/website/static/_redirects
index ba9d00d37f6..8e203b6a70a 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,3 +1,5 @@
+/faqs/environments/beta-release  /docs/dbt-versions/product-lifecycles 301
+
 /docs/deploy/slim-ci-jobs /docs/deploy/ci-jobs 301
 
 ## semantic layer
@@ -23,7 +25,8 @@ https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  /docs/use-dbt-
 /docs/quickstarts/dbt-cloud/bigquery         /quickstarts/bigquery 301
 /docs/quickstarts/dbt-cloud/databricks       /quickstarts/databricks 301
 /docs/quickstarts/dbt-cloud/redshift         /quickstarts/redshift 301
-/docs/quickstarts/dbt-cloud/snowflake        /quickstarts/snowflake 301
+/docs/quickstarts/dbt-cloud/snowflake        /
+quickstarts/snowflake 301
 /docs/quickstarts/dbt-cloud/starburst-galaxy /quickstarts/starburst-galaxy 301
 /docs/quickstarts/dbt-core/codespace         /quickstarts/codespace 301
 /docs/quickstarts/dbt-core/manual-install    /quickstarts/manual-install 301

From 13af9c2a77801dc77ea5e4e1571e72caff9dc32c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 8 Aug 2023 12:58:04 +0100
Subject: [PATCH 590/703] Update _redirects

---
 website/static/_redirects | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index 8e203b6a70a..30271898aff 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -26,7 +26,7 @@ https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  /docs/use-dbt-
 /docs/quickstarts/dbt-cloud/databricks       /quickstarts/databricks 301
 /docs/quickstarts/dbt-cloud/redshift         /quickstarts/redshift 301
 /docs/quickstarts/dbt-cloud/snowflake        /
-quickstarts/snowflake 301
+/quickstarts/snowflake 301
 /docs/quickstarts/dbt-cloud/starburst-galaxy /quickstarts/starburst-galaxy 301
 /docs/quickstarts/dbt-core/codespace         /quickstarts/codespace 301
 /docs/quickstarts/dbt-core/manual-install    /quickstarts/manual-install 301

From 96f345ff3838349b361c9e898d8a6384117fcb12 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 8 Aug 2023 12:58:22 +0100
Subject: [PATCH 591/703] Update _redirects

---
 website/static/_redirects | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index 30271898aff..cc72cfc8d47 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -25,8 +25,7 @@ https://docs.getdbt.com/blog/how-to-design-and-structure-metrics  /docs/use-dbt-
 /docs/quickstarts/dbt-cloud/bigquery         /quickstarts/bigquery 301
 /docs/quickstarts/dbt-cloud/databricks       /quickstarts/databricks 301
 /docs/quickstarts/dbt-cloud/redshift         /quickstarts/redshift 301
-/docs/quickstarts/dbt-cloud/snowflake        /
-/quickstarts/snowflake 301
+/docs/quickstarts/dbt-cloud/snowflake        /quickstarts/snowflake 301
 /docs/quickstarts/dbt-cloud/starburst-galaxy /quickstarts/starburst-galaxy 301
 /docs/quickstarts/dbt-core/codespace         /quickstarts/codespace 301
 /docs/quickstarts/dbt-core/manual-install    /quickstarts/manual-install 301

From 62fd02d4ae5c58dd1b99f69f9d80626172a7feda Mon Sep 17 00:00:00 2001
From: winnie <91998347+gwenwindflower@users.noreply.github.com>
Date: Tue, 8 Aug 2023 08:19:48 -0500
Subject: [PATCH 592/703] Update language to be more mf-clear (#3883)

## What are you changing in this pull request and why?

Want to make sure we're really explicit when we're talking about
MetricFlow (as we are in most of this guide) vs the full-on Semantic
Layer. This clears that up!
---
 .../semantic-layer-1-intro.md                 | 19 ++++++++++++-------
 .../semantic-layer-2-setup.md                 |  7 +++----
 .../semantic-layer-3-build-semantic-models.md |  7 +++----
 .../semantic-layer-4-build-metrics.md         |  7 +++----
 .../semantic-layer-5-refactor-a-mart.md       |  5 ++---
 .../semantic-layer-6-advanced-metrics.md      |  5 ++---
 .../semantic-layer-7-conclusion.md            |  5 ++---
 7 files changed, 27 insertions(+), 28 deletions(-)

diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
index 1d63431a73e..19c6717063c 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-1-intro.md
@@ -1,8 +1,7 @@
 ---
-title: "Intro to the Semantic Layer"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+title: "Intro to MetricFlow"
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 :::tip
@@ -13,10 +12,10 @@ Flying cars, hoverboards, and true self-service analytics: this is the future we
 
 ## Learning goals
 
-- ❓ Understand the **purpose and capabilities** of the **dbt Semantic Layer**.
-- 🧱 Familiarity with the core components of the dbt Semantic Layer — **semantic models and metrics** — and how they work together.
+- ❓ Understand the **purpose and capabilities** of the **dbt Semantic Layer**, particularly MetricFlow as the engine that powers it.
+- 🧱 Familiarity with the core components of MetricFlow — **semantic models and metrics** — and how they work together.
 - 🛠️ Hands-on **experience building** semantic models and metrics in dbt Cloud.
-- 🔁 Know how to **refactor** models into the Semantic Layer.
+- 🔁 Know how to **refactor** models for MetricFlow.
 - 🏅 Aware of new **best practices** to take maximum advantage of the Semantic Layer.
 
 ## Guide structure overview
@@ -31,3 +30,9 @@ We'll work through our learning goals via an [example project](https://github.co
 6. Review **best practices**.
 
 If you're ready to ship your users more power with less code, let's dive in!
+
+:::info
+MetricFlow is a new way to define metrics in dbt and one of the key components of the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl). It handles SQL query construction and defines the specification for dbt semantic models and metrics.
+
+To fully experience the dbt Semantic Layer, including the ability to query dbt metrics via external integrations, you'll need a [dbt Cloud Team or Enterprise account](https://www.getdbt.com/pricing/).
+:::
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
index ef67082e73f..7861767e25d 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md
@@ -1,8 +1,7 @@
 ---
-title: "Setup the Semantic Layer"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+title: "Set up MetricFlow"
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## Getting started
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
index ba4d0cab726..2c2122572b8 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models.md
@@ -1,13 +1,12 @@
 ---
 title: "Building semantic models"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## How to build a semantic model
 
-A semantic model is the Semantic Layer equivalent to a logical layer model (what historically has just been called a 'model' in dbt land). Just as configurations for models are defined on the `models:` YAML key, configurations for semantic models are housed under `semantic models:`. A key difference is that while a logical model consists of configuration and SQL or Python code, a **semantic model is defined purely via YAML**. Rather than encoding a specific dataset, a **semantic model describes relationships** that let your end users select and refine their own datasets reliably.
+A semantic model is the MetricFlow equivalent to a logical layer model (what historically has just been called a 'model' in dbt land). Just as configurations for models are defined on the `models:` YAML key, configurations for semantic models are housed under `semantic models:`. A key difference is that while a logical model consists of configuration and SQL or Python code, a **semantic model is defined purely via YAML**. Rather than encoding a specific dataset, a **semantic model describes relationships** that let your end users select and refine their own datasets reliably.
 
 - ⚙️ Semantic models are **comprised of three components**:
   - 🫂 **entities**: these describe the **relationships** between various semantic models (think ids)
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
index 1a572cc03e4..cd0efdc9e64 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics.md
@@ -1,8 +1,7 @@
 ---
 title: "Building metrics"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## How to build metrics
@@ -37,6 +36,6 @@ metrics:
 
 - It's best practice any time we're updating our semantic layer code to run a `dbt parse && mf validate-configs`.
 - If everything passes, we can start querying this metric with `mf query`!
-- `mf query` is not how you would use the tool in production, that's handled by the dbt Semantic Layer's cloud features. It's available for testing results of various metric queries in development, exactly as we're using it now.
+- `mf query` is not how you would use the tool in production, that's handled by the dbt Cloud Semantic Layer's features. It's available for testing results of various metric queries in development, exactly as we're using it now.
 - Try `mf query --metrics revenue --group-by metric_time__day` and see a preview of the data come back.
 - Note the structure of the above query. We select the metric(s) we want and the dimensions to group them by — we use dunders (double underscores e.g.`metric_time__[time bucket]`) to designate time dimensions or other non-unique dimensions that need a specified entity path to resolve (e.g. if you have a orders location dimension and a employee location dimension both named 'location' you would need dunders to specify `orders__location` or `employee__location`).
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
index cc519e1b718..b2efb39e9fc 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart.md
@@ -1,8 +1,7 @@
 ---
 title: "Refactor an existing mart"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## A new approach
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
index cfac469ed7c..fe7438b5800 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-6-advanced-metrics.md
@@ -1,8 +1,7 @@
 ---
 title: "More advanced metrics"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## More advanced metric types
diff --git a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
index 915ab2d9bfe..a1062721177 100644
--- a/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
+++ b/website/docs/guides/best-practices/how-we-build-our-metrics/semantic-layer-7-conclusion.md
@@ -1,8 +1,7 @@
 ---
 title: "Best practices"
-description: Getting started with the dbt Semantic Layer
-displayText: "dbt Cloud Semantic Layer best practices"
-hoverSnippet: Learn how to get started with the dbt Semantic Layer
+description: Getting started with the dbt and MetricFlow
+hoverSnippet: Learn how to get started with the dbt and MetricFlow
 ---
 
 ## Putting it all together

From 96845378223a4848223c6437db64e70881fc0466 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 8 Aug 2023 16:02:44 +0100
Subject: [PATCH 593/703] Update enterprise-permissions.md

fix typo and change title to sentence case
---
 .../docs/docs/cloud/manage-access/enterprise-permissions.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/cloud/manage-access/enterprise-permissions.md b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
index cb338b3dc39..5bf3623b105 100644
--- a/website/docs/docs/cloud/manage-access/enterprise-permissions.md
+++ b/website/docs/docs/cloud/manage-access/enterprise-permissions.md
@@ -21,11 +21,11 @@ The following roles and permission sets are available for assignment in dbt Clou
 
 <Permissions feature={'/snippets/_enterprise-permissions-table.md'} />
 
-## Diagram of the Permission Sets
+## Diagram of the permission sets
 
 <Lightbox src="/img/docs/dbt-cloud/dbt-cloud-enterprise/enterprise-permission-sets-diagram.png" title="Enterprise Permission Sets & Requirements."/>
 
-## How to Set Up RBAC Groups in dbt Cloud
+## How to set up RBAC Groups in dbt Cloud
 
 Role-Based Access Control (RBAC) is helpful for automatically assigning permissions to dbt admins based on their SSO provider group associations.
 
@@ -34,7 +34,7 @@ Role-Based Access Control (RBAC) is helpful for automatically assigning permissi
 <Lightbox src="/img/docs/dbt-cloud/Select-Groups-RBAC.png" title="Navigate to Groups"/>
 
 1. Select an existing group or create a new group to add RBAC. Name the group (this can be any name you like, but it's recommended to keep it consistent with the SSO groups). If you have configured SSO with SAML 2.0, you may have to use the GroupID instead of the name of the group.
-2. Configure the SSO provider groups you want to add RBAC by clicking **Add** in the **SSO** section. These fields are case sensitive and must match the source group formatting.
+2. Configure the SSO provider groups you want to add RBAC by clicking **Add** in the **SSO** section. These fields are case-sensitive and must match the source group formatting.
 3. Configure the permissions for users within those groups by clicking **Add** in the **Access** section of the window.
 <Lightbox src="/img/docs/dbt-cloud/Configure-SSO-Access.png" title="Configure SSO groups and Access permissions"/>
 

From 8fa9f17f9bee6399345066c24283970c00c4ed21 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 8 Aug 2023 16:06:06 +0100
Subject: [PATCH 594/703] Update _enterprise-permissions-table.md

---
 website/snippets/_enterprise-permissions-table.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/snippets/_enterprise-permissions-table.md b/website/snippets/_enterprise-permissions-table.md
index 75ced50dc2b..779c5bcb479 100644
--- a/website/snippets/_enterprise-permissions-table.md
+++ b/website/snippets/_enterprise-permissions-table.md
@@ -56,7 +56,7 @@ The project roles enable you to work within the projects in various capacities.
 
 #### Account permissions for project roles
 
-| Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+| Account-level permission | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webhook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
 | Account settings         |   R   |         |      R         |           |     R     |           |             |          |                |             |     R      |        |
 | Auth provider            |       |         |                |           |           |           |             |          |                |             |            |        |
@@ -70,7 +70,7 @@ The project roles enable you to work within the projects in various capacities.
 
 #### Project permissions for project roles
 
-|Project-level permission  | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webook |
+|Project-level permission  | Admin | Analyst | Database admin | Developer | Git Admin | Job admin | Job viewer  | Metadata | Semantic Layer | Stakeholder | Team admin | Webhook |
 |--------------------------|:-----:|:-------:|:--------------:|:---------:|:---------:|:---------:|:-----------:|:--------:|:--------------:|:-----------:|:----------:|:------:|  
 | Connections              |   W   |    R    |       W        |     R     |     R     |     R     |             |          |                |     R       |     R      |        |
 | Credentials              |   W   |    W    |       W        |     W     |     R     |     W     |             |          |                |     R       |     R      |        |
@@ -87,4 +87,4 @@ The project roles enable you to work within the projects in various capacities.
 | Projects                 |   W   |    W    |       W        |     W     |     W     |     R     |      R      |          |                |     R       |     W      |        |
 | Repositories             |   W   |         |       R        |     R     |     W     |           |             |          |                |     R       |     R      |        |
 | Runs                     |   W   |    R    |       R        |     W     |     R     |     W     |      R      |          |                |     R       |     R      |        |
-| Semantic Layer Config    |   W   |    R    |       W        |     R     |     R     |     R     |             |          |        W       |     R       |     R      |        |
\ No newline at end of file
+| Semantic Layer Config    |   W   |    R    |       W        |     R     |     R     |     R     |             |          |        W       |     R       |     R      |        |

From fa215b424a995f4f8516e71e13181c7cdc6d40a2 Mon Sep 17 00:00:00 2001
From: Josh Devlin <joshuadevlin@gmail.com>
Date: Wed, 9 Aug 2023 01:31:43 +1000
Subject: [PATCH 595/703] Fix error in predicates (#3846)

## What are you changing in this pull request and why?
As pointed out by [this community
member](https://getdbt.slack.com/archives/CJN7XRF1B/p1690926396734599)
the syntax for incremental predicates uses datediff instead of dateadd

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Co-authored-by: winnie <91998347+gwenwindflower@users.noreply.github.com>
---
 website/docs/docs/build/incremental-models.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/build/incremental-models.md b/website/docs/docs/build/incremental-models.md
index dd20ca36a53..89115652a9c 100644
--- a/website/docs/docs/build/incremental-models.md
+++ b/website/docs/docs/build/incremental-models.md
@@ -395,7 +395,7 @@ models:
       cluster_by: ['session_start']  
       incremental_strategy: merge
       # this limits the scan of the existing table to the last 7 days of data
-      incremental_predicates: ["DBT_INTERNAL_DEST.session_start > datediff(day, -7, current_date)"]
+      incremental_predicates: ["DBT_INTERNAL_DEST.session_start > dateadd(day, -7, current_date)"]
       # `incremental_predicates` accepts a list of SQL statements. 
       # `DBT_INTERNAL_DEST` and `DBT_INTERNAL_SOURCE` are the standard aliases for the target table and temporary table, respectively, during an incremental run using the merge strategy. 
 ```
@@ -412,7 +412,7 @@ Alternatively, here are the same same configurations configured within a model f
     cluster_by = ['session_start'],  
     incremental_strategy = 'merge',
     incremental_predicates = [
-      "DBT_INTERNAL_DEST.session_start > datediff(day, -7, current_date)"
+      "DBT_INTERNAL_DEST.session_start > dateadd(day, -7, current_date)"
     ]
   )
 }}
@@ -430,7 +430,7 @@ merge into <existing_table> DBT_INTERNAL_DEST
         DBT_INTERNAL_DEST.id = DBT_INTERNAL_SOURCE.id
         and
         -- custom predicate: limits data scan in the "old" data / existing table
-        DBT_INTERNAL_DEST.session_start > datediff(day, -7, current_date)
+        DBT_INTERNAL_DEST.session_start > dateadd(day, -7, current_date)
     when matched then update ...
     when not matched then insert ...
 ```

From 25c8ce0b1b512867ec624fe7bc4649c3df1fedf3 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 8 Aug 2023 14:17:08 -0400
Subject: [PATCH 596/703] Adding billing page

---
 website/docs/docs/cloud/billing.md | 105 +++++++++++++++++++++++++++++
 website/sidebars.js                |   1 +
 2 files changed, 106 insertions(+)
 create mode 100644 website/docs/docs/cloud/billing.md

diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md
new file mode 100644
index 00000000000..086802d3737
--- /dev/null
+++ b/website/docs/docs/cloud/billing.md
@@ -0,0 +1,105 @@
+--- 
+title: "Billing"
+id: billing 
+description: "dbt Cloud billing information." 
+sidebar_label: Billing
+---
+
+dbt Cloud offers a variety of [plans and pricing](https://www.getdbt.com/pricing/) to fit your organization’s needs. With flexible billing options that appeal to large enterprises and small businesses and [server availability](/docs/cloud/about-cloud/regions-ip-addresses) worldwide, dbt Cloud is the fastest and easiest way to begin transforming your data.
+
+## How does dbt Cloud pricing work?
+
+As a customer, you pay for the number of seats you have and the amount of usage consumed each month. Usage is based on the number of Successful Models Built, and seats are billed primarily on the amount of Developer licenses purchased. All billing computations are conducted in Coordinated Universal Time (UTC).
+
+### What counts as a Successful Model Built?
+
+dbt Cloud considers a Successful Model Built as any model that is successfully built via a run through dbt Cloud’s orchestration functionality in a dbt Cloud deployment environment. Models are counted when built and run. This includes any jobs run via dbt Cloud's scheduler, CI builds (jobs triggered by pull requests), runs kicked off via the dbt Cloud API, and any successor dbt Cloud tools with similar functionality. This also includes models that are successfully built even when a run may fail to complete. For example, you may have a job that contains 100 models and on one of its runs, 51 models are successfully built and then the job fails. In this situation, only 51 models would be counted.
+
+Any models built in a dbt Cloud development environment (for example, via the IDE) do not count towards your usage. Tests, seeds, and snapshots also do not count. 
+
+
+### What counts as a seat license? 
+
+There are three types of possible seat licenses:
+
+* **Developer** &mdash; for roles and permissions that require interaction with the dbt Cloud environment day-to-day.
+* **Read-Only** &mdash; for access to view certain documents and reports. 
+* **IT** &mdash; for access to specific features related to account management (for example, configuring git integration). 
+
+### Viewing usage in the product 
+
+Viewing usage in the product is restricted to specific roles:
+
+* Team plan &mdash; Owner group
+* Enterprise plan &mdash; Account and billing admin roles
+
+For an account-level view of usage, if you have access to the **Billing** and **Usage** pages, you can see an estimate of the usage for the month. In the Billing page of the **Account Settings**, you can see how your account tracks against its usage. You can also see which projects are building the most models. 
+
+As a Team and Developer plan user, you can see how the account is tracking against the included models built. As an Enterprise plan user, you can see how much you have drawn down from your annual commit and how much remains. 
+
+On each Project Home page, any user with access to that project can see how many models are built each month. From there, additional details on top jobs by models built can be found on each Environment page. 
+
+In addition, you can look at the Job Details page's Insights tab to show how many models are being built per month for that particular job and which models are taking the longest to build. 
+
+Any usage data is only an estimate of your usage, and there may be a delay in showing usage data in the product &mdash; your final usage for the month will be visible on your monthly statements (statements applicable to Team and Enterprise plans).
+
+
+## Plans and Billing
+
+dbt Cloud offers several [plans](https://www.getdbt.com/pricing) with different features that meet your needs. We may make changes to our plan details from time to time. We'll always let you know in advance, so you can be prepared. The following section explains how billing works in each plan.
+
+### Developer plan billing
+
+Developer plans are free and include one Developer license and 5,000 models each month. Models are refreshed at the beginning of each calendar month. If you exceed 5,000 models, any subsequent runs will be canceled until models are refreshed or until you upgrade to a paid plan. The rest of the dbt Cloud platform is still accessible, and no work will be lost. 
+
+### Team plan billing 
+
+Team customers pay monthly via credit card for seats and usage, and accounts include 20,000 models monthly. Seats are charged upfront at the beginning of the month. If you add seats during the month, seats will be prorated and charged on the same day. Seats removed during the month will be reflected on the next invoice and are not eligible for refunds. You can change the credit card information and the number of seats from the billings section anytime. Accounts will receive one monthly invoice that includes the upfront charge for the seats and the usage charged in arrears from the previous month. 
+
+Usage is calculated and charged in arrears for the previous month. If you exceed 20,000 models in any month, you will be billed for additional usage on your next invoice. Additional use is billed at the rates on our [pricing page](https://www.getdbt.com/pricing). 
+
+
+Included models that are not consumed do not roll over to future months. You can estimate your bill with a simple formula: 
+
+`($100 x number of developer seats) + ((models built - 20,000) x $0.01)`
+
+If you cancel your plan, the plan will remain active through the end of the current billing period and may continue to accrue usage. Please review the Managing usage section [#managing-usage] below to avoid further usage accruing. If your account has exceeded the 20,000 included successful models built during the month of cancellation, a usage invoice will be sent the following month.
+
+:::note Legacy pricing plans
+ 
+Customers who purchased the dbt Cloud Team plan before August 8, 2023, may be on a legacy pricing plan that only includes seat-based charges. Legacy plan pricing will be supported through at least July 2024, although we may not release all new features to legacy plan subscribers. Any changes to your current plan pricing will be communicated in advance according to our Terms of Use. To ensure you have access to new features for each plan, you can upgrade now or [contact us](mailto:support@getdbt.com) with questions.
+
+:::
+
+### Enterprise plan billing
+
+As an Enterprise customer, you pay annually via invoice, monthly in arrears for additional usage (if applicable), and may benefit from negotiated usage rates. Please refer to your order form or contract for your specific pricing details, or [contact the account team](https://www.getdbt.com/contact-demo) with any questions. 
+
+## Managing usage
+
+From anywhere in the dbt Cloud account, click the **gear icon** and click **Account settings**. The **Billing** option will be on the left side menu under the **Account Settings** heading. Here, you can view individual available plans and the features provided for each. 
+
+### Usage notifications 
+
+Every plan automatically sends email alerts when 75%, 90%, and 100% of usage estimates have been reached. In the Team plan, all users within the Owner group will receive alerts. In Enterprise plans, all users with the Account Admin and Billing Admin permission sets will receive alerts. Users cannot opt out of these emails. If you would like additional users to receive these alert emails, please provide them with the applicable permissions mentioned above. Note that your usage may already be higher than the percentage indicated in the alert due to your usage pattern and minor latency times. 
+
+### How do I stop usage from accruing?
+
+There are 2 options to disable models from being built and charged:
+
+1. Open the **Job Settings** of every job and navigate to the **Triggers** section. Disable the **Run on Schedule** and set the **Continuous Integration** feature **Run on Pull Requests?**  to **No**. Check your workflows to ensure that you are not triggering any runs via the dbt Cloud API. This option will enable you to keep your dbt Cloud jobs without building more models. 
+2. Alternatively, you can delete some or all of your dbt Cloud jobs. This will ensure that no runs are kicked off, but you will permanently lose your job(s). 
+
+
+## FAQ
+* What happens if I need more than 8 seats on the Team plan? 
+_If you need more than 8 developer seats, select the Contact Sales option from the billing settings to talk to our sales team about an Enterprise plan._  
+
+* What if I go significantly over my included free models on the Team or Developer plan?
+_Consider upgrading to a Team or Enterprise plan. Team plans include more models and allow you to exceed the monthly usage limit. Enterprise accounts are supported by a dedicated account management team and offer annual plans, custom configurations, and negotiated usage rates. 
+
+* I want to upgrade my plan. Will all of my work carry over?
+_Yes. Your dbt Cloud account will be upgraded without impacting your existing projects and account settings._
+
+* How do I determine the right plan for me?
+ _The best option is to consult with our sales team. They'll help you figure out what is right for your needs. We also offer a free two-week trial on the Team plan._
diff --git a/website/sidebars.js b/website/sidebars.js
index 62262703055..1ffaea7af7f 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -137,6 +137,7 @@ const sidebarSettings = {
                 "docs/cloud/secure/ip-restrictions",
               ],
             }, // PrivateLink
+            "docs/cloud/billing",
           ],
         },
         {

From 404c524317a30f8474b2057132275893da63dd27 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 8 Aug 2023 14:35:48 -0400
Subject: [PATCH 597/703] Update website/docs/docs/cloud/billing.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/billing.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md
index 086802d3737..d2f1596259b 100644
--- a/website/docs/docs/cloud/billing.md
+++ b/website/docs/docs/cloud/billing.md
@@ -91,7 +91,7 @@ There are 2 options to disable models from being built and charged:
 2. Alternatively, you can delete some or all of your dbt Cloud jobs. This will ensure that no runs are kicked off, but you will permanently lose your job(s). 
 
 
-## FAQ
+## FAQs
 * What happens if I need more than 8 seats on the Team plan? 
 _If you need more than 8 developer seats, select the Contact Sales option from the billing settings to talk to our sales team about an Enterprise plan._  
 

From def6c173dd9dc2137246a75a09e8dcbf0747fa79 Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Wed, 9 Aug 2023 08:14:05 +1200
Subject: [PATCH 598/703] Remove dbt updates tag from devblog (#3884)

## What are you changing in this pull request and why?
We experimented with product updates in the dev blog for a lil while,
but it's now better implemented elsewhere. So that it doesn't look like
we just stopped improving the product 18 months ago, we're going to
remove this tag but keep the articles around so they don't 404.

NB that a handful of new product feature articles were incorrectly
tagged (using the label not its ID) so I've just removed those tags
altogether as well.

Before and after:

![Screenshot 2023-08-08 at 4 12 03
PM](https://github.com/dbt-labs/docs.getdbt.com/assets/7335046/868daa42-8982-497e-a48e-056207e023a3)
![Screenshot 2023-08-08 at 4 12 43
PM](https://github.com/dbt-labs/docs.getdbt.com/assets/7335046/d29c2e4c-b97d-4de7-b3ac-dbc2629cc124)

---------

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/blog/2021-09-15-september-21-product-email.md         | 1 -
 website/blog/2021-10-15-october-21-product-update-email.md    | 1 -
 website/blog/2021-11-15-november-21-product-email.md          | 1 -
 website/blog/2022-08-31-august-product-update.md              | 1 -
 .../blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md | 3 +--
 website/blog/2023-04-26-deprecating-dbt-metrics.md            | 1 -
 website/blog/2023-05-01-evolving-data-engineer-craft.md       | 1 -
 website/blog/categories.yml                                   | 4 ----
 8 files changed, 1 insertion(+), 12 deletions(-)

diff --git a/website/blog/2021-09-15-september-21-product-email.md b/website/blog/2021-09-15-september-21-product-email.md
index c18f59a9be5..a3c9993befa 100644
--- a/website/blog/2021-09-15-september-21-product-email.md
+++ b/website/blog/2021-09-15-september-21-product-email.md
@@ -4,7 +4,6 @@ description: "dbt v1.0 is coming up! Don't forget to update your projects to the
 slug: dbt-product-update-2021-september
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-09-15
diff --git a/website/blog/2021-10-15-october-21-product-update-email.md b/website/blog/2021-10-15-october-21-product-update-email.md
index 9e58514c50e..c235e43bf43 100644
--- a/website/blog/2021-10-15-october-21-product-update-email.md
+++ b/website/blog/2021-10-15-october-21-product-update-email.md
@@ -4,7 +4,6 @@ description: "Stay up-to-date with the latest features in dbt. Read about our Oc
 slug: dbt-product-update-2021-october
 authors: [lauren_craigie]
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-10-15
diff --git a/website/blog/2021-11-15-november-21-product-email.md b/website/blog/2021-11-15-november-21-product-email.md
index d38685aad53..dd5d2b63956 100644
--- a/website/blog/2021-11-15-november-21-product-email.md
+++ b/website/blog/2021-11-15-november-21-product-email.md
@@ -4,7 +4,6 @@ description: "Stay up-to-date with the latest features in dbt. Read about our No
 slug: dbt-product-update-2021-november
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2021-11-15
diff --git a/website/blog/2022-08-31-august-product-update.md b/website/blog/2022-08-31-august-product-update.md
index 143d46a37d3..cb4077f3a06 100644
--- a/website/blog/2022-08-31-august-product-update.md
+++ b/website/blog/2022-08-31-august-product-update.md
@@ -4,7 +4,6 @@ description: "Coalesce is less than 2 months away!"
 slug: dbt-product-update-2022-august
 authors: [lauren_craigie] 
 
-tags: [dbt updates]
 hide_table_of_contents: false
 
 date: 2022-08-31
diff --git a/website/blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md b/website/blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md
index 95988e75f04..bab92000a16 100644
--- a/website/blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md
+++ b/website/blog/2022-10-19-polyglot-dbt-python-dataframes-and-sql.md
@@ -4,8 +4,7 @@ description: "Going polyglot is a major next step in the journey of dbt Core. It
 slug: polyglot-dbt-python-dataframes-sql
 
 authors: [doug_beatty]
-
-tags: [dbt product updates]
+tags: [dbt tutorials]
 hide_table_of_contents: false
 
 date: 2022-10-18
diff --git a/website/blog/2023-04-26-deprecating-dbt-metrics.md b/website/blog/2023-04-26-deprecating-dbt-metrics.md
index 1041f75eb2b..bf23bb992ad 100644
--- a/website/blog/2023-04-26-deprecating-dbt-metrics.md
+++ b/website/blog/2023-04-26-deprecating-dbt-metrics.md
@@ -5,7 +5,6 @@ slug: deprecating-dbt-metrics
 
 authors: [callum_mccann]
 
-tags: [dbt product updates]
 hide_table_of_contents: false
 
 date: 2023-04-26
diff --git a/website/blog/2023-05-01-evolving-data-engineer-craft.md b/website/blog/2023-05-01-evolving-data-engineer-craft.md
index 339d0ac380e..a3113240227 100644
--- a/website/blog/2023-05-01-evolving-data-engineer-craft.md
+++ b/website/blog/2023-05-01-evolving-data-engineer-craft.md
@@ -5,7 +5,6 @@ slug: evolving-data-engineer-craft
 
 authors: [sung_chung, kira_furuichi]
 
-tags: [dbt product updates]
 hide_table_of_contents: false
 
 date: 2023-05-01
diff --git a/website/blog/categories.yml b/website/blog/categories.yml
index 2a45e6529e2..8103f58cc33 100644
--- a/website/blog/categories.yml
+++ b/website/blog/categories.yml
@@ -15,10 +15,6 @@
   display_title: dbt tutorials
   description: Best practices in the usage of our favorite data transformation tool.
   is_featured: true    
-- name: dbt updates
-  display_title: dbt product updates
-  description: An archive of monthly product updates from the dbt Labs team.
-  is_featured: true
 - name: SQL magic
   display_title: SQL magic
   description: Stories of dbt developers making SQL sing across warehouses.

From b82a98b8937c936958cbf1b12d9a19689b714623 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Tue, 8 Aug 2023 16:36:21 -0400
Subject: [PATCH 599/703] Fixing italics on Billing FAQ

---
 website/docs/docs/cloud/billing.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md
index d2f1596259b..76b37560be3 100644
--- a/website/docs/docs/cloud/billing.md
+++ b/website/docs/docs/cloud/billing.md
@@ -92,11 +92,12 @@ There are 2 options to disable models from being built and charged:
 
 
 ## FAQs
+
 * What happens if I need more than 8 seats on the Team plan? 
 _If you need more than 8 developer seats, select the Contact Sales option from the billing settings to talk to our sales team about an Enterprise plan._  
 
 * What if I go significantly over my included free models on the Team or Developer plan?
-_Consider upgrading to a Team or Enterprise plan. Team plans include more models and allow you to exceed the monthly usage limit. Enterprise accounts are supported by a dedicated account management team and offer annual plans, custom configurations, and negotiated usage rates. 
+_Consider upgrading to a Team or Enterprise plan. Team plans include more models and allow you to exceed the monthly usage limit. Enterprise accounts are supported by a dedicated account management team and offer annual plans, custom configurations, and negotiated usage rates._ 
 
 * I want to upgrade my plan. Will all of my work carry over?
 _Yes. Your dbt Cloud account will be upgraded without impacting your existing projects and account settings._

From 2a6ec18763cc1bb71ab8b8c89454e4307553583a Mon Sep 17 00:00:00 2001
From: Adam Tworkiewicz <adamtwo@users.noreply.github.com>
Date: Tue, 8 Aug 2023 16:42:22 -0500
Subject: [PATCH 600/703] Update docs to reflect Teradata as a verified adapter
 (#3873)

## What are you changing in this pull request and why?

Teradata has achieved the Verified Connector status. This PR changes
`docs/supported-data-platforms` page to reflect that.


## Checklist

- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Co-authored-by: Adam Tworkiewicz <adam.tworkiewicz@teradata.com>
---
 website/docs/docs/supported-data-platforms.md | 5 +++++
 website/static/img/icons/teradata.svg         | 1 +
 website/static/img/icons/white/teradata.svg   | 1 +
 3 files changed, 7 insertions(+)
 create mode 100644 website/static/img/icons/teradata.svg
 create mode 100644 website/static/img/icons/white/teradata.svg

diff --git a/website/docs/docs/supported-data-platforms.md b/website/docs/docs/supported-data-platforms.md
index a8ae33a7e0c..d1f88536693 100644
--- a/website/docs/docs/supported-data-platforms.md
+++ b/website/docs/docs/supported-data-platforms.md
@@ -72,6 +72,11 @@ The following are **Verified adapters** ✓ you can connect to either in dbt Clo
     body="<a href='/docs/core/connect-data-platform/azuresynapse-setup'><img src='/img/icons/dbt-bit.svg' width='7%'/>Install using the CLI  </a> <br /><br /> <a href=https://badge.fury.io/py/dbt-synapse><img src=https://badge.fury.io/py/dbt-synapse.svg/><br /> 🚧 Verification in progress"
     icon="rocket"/>
 
+<Card
+    title="Teradata*"
+    body="<a href='/docs/core/connect-data-platform/teradata-setup'><img src='/img/icons/dbt-bit.svg' width='7%'/>Install using the CLI  </a> <br /><br /> <a href=https://badge.fury.io/py/dbt-teradata><img src=https://badge.fury.io/py/dbt-teradata.svg/><br />"
+    icon="teradata"/>    
+
 </div>
 
 <br />
diff --git a/website/static/img/icons/teradata.svg b/website/static/img/icons/teradata.svg
new file mode 100644
index 00000000000..cbbfab92d66
--- /dev/null
+++ b/website/static/img/icons/teradata.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="48px" height="48px"><path fill="#f27241" d="M24 4A20 20 0 1 0 24 44A20 20 0 1 0 24 4Z"/><path fill="#fff" d="M30.515,31.366c-1.446,0.609-3.478,1.137-4.935,0.192c-1.159-0.752-1.58-2.116-1.58-2.85V21h5v-5h-5 v-5h-5v17.707c0,2.064,1.002,5.124,3.828,6.956c1.08,0.7,2.623,1.336,4.665,1.336c1.419,0,3.08-0.306,4.993-1.113L30.515,31.366z"/></svg>
\ No newline at end of file
diff --git a/website/static/img/icons/white/teradata.svg b/website/static/img/icons/white/teradata.svg
new file mode 100644
index 00000000000..cbbfab92d66
--- /dev/null
+++ b/website/static/img/icons/white/teradata.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg"  viewBox="0 0 48 48" width="48px" height="48px"><path fill="#f27241" d="M24 4A20 20 0 1 0 24 44A20 20 0 1 0 24 4Z"/><path fill="#fff" d="M30.515,31.366c-1.446,0.609-3.478,1.137-4.935,0.192c-1.159-0.752-1.58-2.116-1.58-2.85V21h5v-5h-5 v-5h-5v17.707c0,2.064,1.002,5.124,3.828,6.956c1.08,0.7,2.623,1.336,4.665,1.336c1.419,0,3.08-0.306,4.993-1.113L30.515,31.366z"/></svg>
\ No newline at end of file

From b7901a484cdfe0c5c0149e93063319c6e4dee9f4 Mon Sep 17 00:00:00 2001
From: Jordan Stein <jordan.stein77@gmail.com>
Date: Tue, 8 Aug 2023 17:16:52 -0700
Subject: [PATCH 601/703] fix dropbox feedback

---
 website/docs/docs/build/cumulative-metrics.md |  5 ++---
 website/docs/docs/build/derived-metrics.md    |  2 +-
 website/docs/docs/build/metrics-overview.md   | 20 ++++++++++---------
 website/docs/docs/build/ratio-metrics.md      |  3 ++-
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md   |  2 +-
 website/snippets/_sl-define-metrics.md        |  2 +-
 6 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index 08e2c6f0adf..e9c0dc6126e 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -19,9 +19,8 @@ metrics:
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
       measure: the measure you are referencing # Required
-      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Cannot be used with grain_to_date
-      grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next. 
-      # Optional. Can not be used with window. 
+      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Can not be used with window. 
+      grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next.  # Optional. Cannot be used with grain_to_date
 
 ```
 
diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index acfd6935208..bf1d267c96e 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -70,7 +70,7 @@ metrics:
 
 ## Derived metric offset
 
-You may want to use an offset value of a metric in the definition of a derived metric. For example, you can model the retention rate by using a derived metric with an offset, which involves calculating (active customers at the end of the month/active customers at the beginning of the month) - 1.
+You may want to use an offset value of a metric in the definition of a derived metric. For example, you can model the retention rate by using a derived metric with an offset, which involves calculating (active customers at the end of the month/active customers at the beginning of the month).
 
 ```yaml
 metrics:
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 4a926589de2..9000af14fc2 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -13,7 +13,7 @@ The keys for metrics definitions are:
 | Component | Description | Type |
 | --------- | ----------- | ---- |
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
-| `type` | Define the type of metric, which can be a measure (`simple`) or ratio (`ratio`)).  | Required |
+| `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative` or `derived`.  | Required |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
 | `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity, or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |
@@ -68,15 +68,17 @@ metrics:
 
 ```yaml
 metrics:
-  - name: net_sales_per_user
+  - name: order_gross_profit
+    description: Gross profit from each order.
     type: derived
-    type_params: null
-    metrics:
-      - name: gross_sales # these are all metrics (can be a derived metric, meaning building a derived metric with derived metrics)
-      - name: cogs
-      - name: users
-        filter: {{ Dimension('is_active')}} # Optional additional constraint
-        alias: active_users # Optional alias to use in the expr
+    label: Order Gross Profit
+    type_params:
+      expr: revenue - cost
+      metrics:
+        - name: order_total
+          alias: revenue
+        - name: order_cost
+          alias: cost
 ```
 <!-- not supported
 ### Expression metrics
diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index db108bd37fb..00c0309c792 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -104,7 +104,8 @@ metrics:
     type_params:
       numerator:
         name: distinct_purchasers
-        filter: {{Dimension('customer__is_frequent_purchaser')}}
+        filter: |
+          {{Dimension('customer__is_frequent_purchaser')}}
         alias: frequent_purchasers
       denominator:
         name: distinct_purchasers
diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 21b5f4f921a..79ac5256b23 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -30,7 +30,7 @@ dbt Labs partners can use the JDBC API to build integrations in their tools with
 
 If you are a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
 
-Refer to [Get started with the dbt Semantic Layer](docs/use-dbt-semantic-layer/quickstart-sl) for more info.
+Refer to [Get started with the dbt Semantic Layer](../use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ## Authentication
 
diff --git a/website/snippets/_sl-define-metrics.md b/website/snippets/_sl-define-metrics.md
index 77d3a51c873..cf9ac59f5ee 100644
--- a/website/snippets/_sl-define-metrics.md
+++ b/website/snippets/_sl-define-metrics.md
@@ -7,7 +7,7 @@ Now that you've created your first semantic model, it's time to define your firs
 ```yaml
 metrics:
   - name: order_total
-    description: Sum of total order amonunt. Includes tax + revenue.
+    description: Sum of total order amount. Includes tax + revenue.
     type: simple
     label: Order Total
     type_params:

From 8540e8bb0e0b0956954349328115f013bd39b727 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 10:36:13 +0100
Subject: [PATCH 602/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 79ac5256b23..051878fd57a 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -28,7 +28,7 @@ dbt Labs partners can use the JDBC API to build integrations in their tools with
 
 ## Using the JDBC API
 
-If you are a dbt user or partner with access to dbt Cloud and the[dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
+If you are a dbt user or partner with access to dbt Cloud and the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
 
 Refer to [Get started with the dbt Semantic Layer](../use-dbt-semantic-layer/quickstart-sl) for more info.
 

From 4152df7a30312bdf9e464697506b8a86de2c3170 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 10:36:21 +0100
Subject: [PATCH 603/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 051878fd57a..ecc237d4b8c 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -30,7 +30,7 @@ dbt Labs partners can use the JDBC API to build integrations in their tools with
 
 If you are a dbt user or partner with access to dbt Cloud and the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), you can [setup](/docs/use-dbt-semantic-layer/setup-sl) and test this API with data from your own instance by configuring the Semantic Layer and obtaining the right JDBC connection parameters described in this document.
 
-Refer to [Get started with the dbt Semantic Layer](../use-dbt-semantic-layer/quickstart-sl) for more info.
+Refer to [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl) for more info.
 
 ## Authentication
 

From 59f699559fbe81c5e216192ee618b8fe92323b92 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 10:41:24 +0100
Subject: [PATCH 604/703] Update website/docs/docs/build/metrics-overview.md

---
 website/docs/docs/build/metrics-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 9000af14fc2..11c893ed4b4 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -13,7 +13,7 @@ The keys for metrics definitions are:
 | Component | Description | Type |
 | --------- | ----------- | ---- |
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
-| `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative` or `derived`.  | Required |
+| `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative`, or `derived`.  | Required |
 | `type_params` | Additional parameters used to configure metrics. `type_params` are different for each metric type. | Required |
 | `filter` | For any type of metric, you may optionally include a filter string, which applies a filter for a dimension, entity, or time dimension when computing the metric. You can think of this as your WHERE clause.   | Optional |
 |  `meta` | Additional metadata you want to add to your metric. |

From 94c89d8114463e6ac886eaf23a5ee8d8d4d953ad Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 9 Aug 2023 11:37:52 +0100
Subject: [PATCH 605/703] add consistency and fix typos

---
 website/docs/docs/build/cumulative-metrics.md | 33 +++++++++++------
 website/docs/docs/build/derived-metrics.md    | 17 ++++++++-
 website/docs/docs/build/dimensions.md         |  8 ++---
 website/docs/docs/build/measures.md           |  4 +--
 website/docs/docs/build/metrics-overview.md   |  2 +-
 website/docs/docs/build/ratio-metrics.md      | 35 +++++++++++++------
 website/docs/docs/build/simple.md             | 18 ++++++++--
 7 files changed, 85 insertions(+), 32 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index e9c0dc6126e..bdc6c1fc233 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -6,32 +6,45 @@ sidebar_label: Cumulative
 tags: [Metrics, Semantic Layer]
 ---
 
-Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. 
+Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
-This metric is common for calculating things like weekly active users, or month to date revenue. The following displays the full metrics spec, along with an example:
+This metric is common for calculating things like weekly active users, or month to date revenue.  The parameters, description, and type for cumulative metrics are: 
 
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `measure` | The measure you are referencing. | Required |
+| `window` | The accumulation window, such as 1 month, 7 days, 1 year. This can't be used with `window`. | Optional  |
+| `grain_to_date` | Sets the accumulation grain, such as month will accumulate data for one month. Then restart at the beginning of the next. This can't be used with window. | Optional |
+
+The following displays the complete specification for cumulative metrics, along with an example:
 
 ```yaml
 metrics:
-  - name: the metric name # Required
-    description: the metric description # Optional
+  - name: The metric name # Required
+    description: The metric description # Optional
     type: simple # Required
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
-      measure: the measure you are referencing # Required
-      window: the accumulation window i.e 1 month, 7 days, 1 year. # Optional. Can not be used with window. 
-      grain_to_date: sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next.  # Optional. Cannot be used with grain_to_date
+      measure: The measure you are referencing # Required
+      window: The accumulation window, such as 1 month, 7 days, 1 year. # Optional. Can not be used with window. 
+      grain_to_date: Sets the accumulation grain, such as month will accumulate data for one month, then restart at the beginning of the next.  # Optional. Cannot be used with grain_to_date
 
 ```
 
-:::info MetricFlow time spine required
+## Cumulative metrics example
+
+
+:::tip MetricFlow time spine required
 
 You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
 :::
 
-## Cumulative metrics example
-
 Cumulative metrics measure data over a given window and consider the window infinite when no window parameter is passed, accumulating the data over all time.
 
 ```yaml
diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index bf1d267c96e..53d567f0867 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -8,7 +8,22 @@ tags: [Metrics, Semantic Layer]
 
 In MetricFlow, derived metrics are metrics created by defining an expression using other metrics. They allow performing calculations on top of existing metrics. This proves useful for combining metrics and applying arithmetic functions to aggregated columns, such as, you can define a profit metric. 
 
-The following example presents the complete specification for derived metrics, along with an example.
+ The parameters, description, and type for derived metrics are: 
+
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `expr` | The derived expression. | Required |
+| `metrics` |  The list of metrics used in the derived metrics. | Required  |
+| `alias` | Optional alias for the metric that you can use in the expr. | Optional |
+| `filter` | Optional filter to apply to the metric. | Optional |
+| `offset_window` | Set the period for the offset window, such as 1 month. This will return the value of the metric one month from the metric time. | Required |
+
+The following displays the complete specification for derived metrics, along with an example.
 
 ```yaml
 metrics:
diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index a73515e30eb..2e2776e5588 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -17,12 +17,12 @@ Groups are defined within semantic models, alongside entities and measures, and
 
 All dimensions require a `name`, `type` and in some cases, an `expr` parameter. 
 
-| Name | Parameter | Field type |
-| --- | --- | --- |
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
 | `name` |  Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the `expr` parameter. <br /><br /> Dimension names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses [joins](/docs/build/join-logic) to identify the right dimension. | Required |
-| `type` | Specifies the type of group created in the semantic model. There are three types:<br /><br />&mdash; Categorical: Group rows in a table by categories like geography, product type, color, and so on. <br />&mdash; Time: Point to a date field in the data platform, and must be of type TIMESTAMP or equivalent in the data platform engine. <br />&mdash; Slowly-changing dimensions: Analyze metrics over time and slice them by groups that change over time, like sales trends by a customer's country. | Required |
+| `type` | Specifies the type of group created in the semantic model. There are three types:<br /><br />- **Categorical**: Group rows in a table by categories like geography, color, and so on. <br />- **Time**: Point to a date field in the data platform. Must be of type TIMESTAMP or equivalent in the data platform engine. <br />- **Slowly-changing dimensions**: Analyze metrics over time and slice them by groups that change over time, like sales trends by a customer's country. | Required |
 | `type_params` | Specific type params such as if the time is primary or used as a partition | Required |
-| `description` | Description of the dimension | Optional |
+| `description` | A clear description of the dimension | Optional |
 | `expr` | Defines the underlying column or SQL query for a dimension. If no `expr` is specified, MetricFlow will use the column with the same name as the group. You can use column name itself to input a SQL expression. | Optional |
 
 Refer to the following for the complete specification for dimensions:
diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index 4ea96d891cc..ba82a4aa4a5 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -8,8 +8,8 @@ tags: [Metrics, Semantic Layer]
 
 Measures are aggregations performed on columns in your model. They can be used as final metrics or serve as building blocks for more complex metrics. Measures have several inputs, which are described in the following table along with their field types.
 
-| Parameter | Description | Field type |
-| --- | --- | --- |
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
 | [`name`](#name) | Provide a name for the measure, which must be unique and can't be repeated across all semantic models in your dbt project. | Required |
 | [`description`](#description) | Describes the calculated measure. | Optional |
 | [`agg`](#aggregation) | dbt supports aggregations such as `sum`, `min`, `max`, and more. Refer to [Aggregation](/docs/build/measures#aggregation) for the full list of supported aggregation types. | Required |
diff --git a/website/docs/docs/build/metrics-overview.md b/website/docs/docs/build/metrics-overview.md
index 11c893ed4b4..0cb82800d84 100644
--- a/website/docs/docs/build/metrics-overview.md
+++ b/website/docs/docs/build/metrics-overview.md
@@ -10,7 +10,7 @@ Once you've created your semantic models, it's time to start adding metrics! Met
 
 The keys for metrics definitions are: 
 
-| Component | Description | Type |
+| Parameter | Description | Type |
 | --------- | ----------- | ---- |
 | `name` | Provide the reference name for the metric. This name must be unique amongst all metrics.   | Required |
 | `type` | Define the type of metric, which can be `simple`, `ratio`, `cumulative`, or `derived`.  | Required |
diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index 00c0309c792..d2640d09016 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -8,29 +8,42 @@ tags: [Metrics, Semantic Layer]
 
 Ratio allows you to create a ratio between two metrics. You simply specify a numerator and a denominator metric. Additionally, you can apply a dimensional filter to both the numerator and denominator using a constraint string when computing the metric. 
 
-The following displays the full spec for ratio metrics, along with an example:
+ The parameters, description, and type for ratio metrics are: 
+
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `numerator` | The name of the metric used for the numerator, or structure of properties. | Required |
+| `denominator` |  The name of the metric used for the denominator, or structure of properties. | Required  |
+| `filter` | Optional filter for the numerator or denominator. | Optional |
+| `alias` | Optional alias for the numerator or denominator. | Optional |
+
+The following displays the complete specification for ratio metrics, along with an example.
 
 ```yaml
 metrics:
-  - name: the metric name # Required
+  - name: The metric name # Required
     description: the metric description # Optional
     type: ratio # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
-      numerator: the name of the metric used for the numerator, or a struct of properties as below # Required
-        name: name of metric used for the numerator # Required
-        filter: filter for the numerator# Optional
-        alias: alias for the numerator # Optional
-      denominator: the name of the metric used for the denominator, or a struct of properties as below # Required
-        name: name of metric used for the denominator # Required
-        filter: filter for the denominator # Optional
-        alias: alias for the denominator # Optional
+      numerator: The name of the metric used for the numerator, or structure of properties # Required
+        name: Name of metric used for the numerator # Required
+        filter: Filter for the numerator # Optional
+        alias: Alias for the numerator # Optional
+      denominator: The name of the metric used for the denominator, or structure of properties # Required
+        name: Name of metric used for the denominator # Required
+        filter: Filter for the denominator # Optional
+        alias: Alias for the denominator # Optional
 ```
 
 ## Ratio metrics example
 
 ```yaml
-# Ratio Metric
 metrics:
   - name: food_order_pct
     description: "The food order count as a ratio of the total order count"
diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md
index efc0c24ffbd..bebd7c4589a 100644
--- a/website/docs/docs/build/simple.md
+++ b/website/docs/docs/build/simple.md
@@ -8,16 +8,28 @@ tags: [Metrics, Semantic Layer]
 
 Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your data platform, and can be filtered by one or multiple dimensions.
 
-The following displays the full spec for ratio metrics, along with an example:
+ The parameters, description, and type for simple metrics are: 
+
+| Parameter | Description | Type |
+| --------- | ----------- | ---- |
+| `name` | The name of the metric. | Required |
+| `description` | The description of the metric. | Optional |
+| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `label` | The value that will be displayed in downstream tools. | Required |
+| `type_params` | The type parameters of the metric. | Required |
+| `measure` | The measure you're referencing. | Required |
+
+The following displays the complete specification for simple metrics, along with an example.
+
 
 ```yaml
 metrics:
-  - name: the metric name # Required
+  - name: The metric name # Required
     description: the metric description # Optional
     type: simple # Required
     label: The value that will be displayed in downstream tools #Required
     type_params: # Required
-      measure: the measure you're referencing # Required
+      measure: The measure you're referencing # Required
 
 ```
 

From 5f6bac8695e612b8c968f350218f63699b068cc7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 9 Aug 2023 13:22:30 +0100
Subject: [PATCH 606/703] add sql query syntax info, other nits

---
 website/docs/docs/build/sl-getting-started.md |  21 +++++----
 .../avail-sl-integrations.md                  |   6 +--
 .../docs/use-dbt-semantic-layer/dbt-sl.md     |   2 +-
 .../use-dbt-semantic-layer/quickstart-sl.md   |  29 ++++++-------
 .../use-dbt-semantic-layer/sl-architecture.md |   7 ++-
 website/docs/guides/migration/sl-migration.md |  41 ++++++++++--------
 website/snippets/_new-sl-setup.md             |  18 ++++----
 .../snippets/_sl-test-and-query-metrics.md    |   8 ++--
 .../semantic-layer/new-sl-configure.jpg       | Bin 0 -> 26524 bytes
 .../semantic-layer/sl-configure-example.jpg   | Bin 0 -> 51854 bytes
 .../semantic-layer/sl-initial-page.jpg        | Bin 29742 -> 0 bytes
 11 files changed, 75 insertions(+), 57 deletions(-)
 create mode 100644 website/static/img/docs/dbt-cloud/semantic-layer/new-sl-configure.jpg
 create mode 100644 website/static/img/docs/dbt-cloud/semantic-layer/sl-configure-example.jpg
 delete mode 100644 website/static/img/docs/dbt-cloud/semantic-layer/sl-initial-page.jpg

diff --git a/website/docs/docs/build/sl-getting-started.md b/website/docs/docs/build/sl-getting-started.md
index bfe8eb00f4d..b5c7f5d2810 100644
--- a/website/docs/docs/build/sl-getting-started.md
+++ b/website/docs/docs/build/sl-getting-started.md
@@ -20,7 +20,7 @@ To fully experience the power of a universal dbt Semantic Layer, take the follow
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
 - [Define metrics](#define-metrics) with MetricFlow
-- [Test and query metrics](#test-and-query-metrics) with MetricFlow
+- [Test and query metrics locally](#test-and-query-metrics) with MetricFlow
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#set-up-dbt-semantic-layer) in dbt Cloud
 - [Connect to and query the API](#connect-and-query-api) with dbt Cloud
@@ -82,14 +82,14 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query API
 
-You must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America. You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer.
+You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer. In order to do so, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America. 
 
-To connect and query your metrics using the dbt Semantic Layer and its API:
+- <span>To learn how to use the JDBC API and what tools you can query it with, refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>.<br />
 
-1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC. 
-2. Once you've connected to the API, you should then set up and query metrics in your downstream tool of choice.
+    * To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) with Semantic Layer Only and Metadata Only permissions.
+    * Refer to the [SQL query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) to query metrics using the API.  
 
-Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
+- To learn more about the sophisticated integrations that connect to the dbt Semantic Layer, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
 ## FAQs
 
@@ -109,8 +109,11 @@ User data passes through the Semantic Layer on its way back from the warehouse.
 <summary>Is the dbt Semantic Layer open source?</summary>
 The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 
-## Related docs
+## Next steps
+
+Review the following documents to learn more and get started:
 
-- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
-- [The dbt Semantic Layer: what’s next](https://www.getdbt.com/blog/dbt-semantic-layer-whats-next/) blog post
 - [About MetricFlow](/docs/build/about-metricflow)
+- [Build your metrics](/docs/build/build-metrics-intro)
+- [Get started with the dbt Semantic Layer](/docs/use-dbt-semantic-layer/quickstart-sl)
+- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
diff --git a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
index 267942155c4..27cb83977de 100644
--- a/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
+++ b/website/docs/docs/use-dbt-semantic-layer/avail-sl-integrations.md
@@ -15,12 +15,11 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 <NewSLChanges />
 
 
-
 There are a number of data applications that seamlessly integrate with the dbt Semantic Layer, powered by MetricFlow, from business intelligence tools to notebooks, spreadsheets, data catalogs, and more. These integrations allow you to query and unlock valuable insights from your data ecosystem.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="95%" title="dbt Semantic Layer diagram displays the variety of different integration tools it supports."/>
+<span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. The API enables you to query metrics, avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.<br /><br />
 
-<span>Query dbt metrics with external integrations using the sophisticated <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>. Use the API to query metrics to avoid duplicative coding, optimize your development workflow, ensure data governance for company metrics, and guarantee consistency for data consumers.<br /><br />
+<!-- turn these into cards for ga -->
 
 import AvailIntegrations from '/snippets/_sl-partner-links.md';
 
@@ -29,6 +28,7 @@ import AvailIntegrations from '/snippets/_sl-partner-links.md';
 ## Related docs
 
 - <span><a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to integrate with the JDBC to query your metrics in downstream tools.
+- [dbt Semantic Layer API query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) 
 
 </VersionBlock>
 
diff --git a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
index 97fc1700634..c10feded83a 100644
--- a/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/dbt-sl.md
@@ -53,7 +53,7 @@ instance="hosted in North America"
     icon="dbt-bit"/>
 
 <Card
-    title="Product architecture"
+    title="Architecture"
     body="Learn about the powerful components that make up the dbt Semantic Layer."
     link="/docs/use-dbt-semantic-layer/sl-architecture"
     icon="dbt-bit"/>
diff --git a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
index f70433cd10a..13b7b99d350 100644
--- a/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
+++ b/website/docs/docs/use-dbt-semantic-layer/quickstart-sl.md
@@ -29,7 +29,7 @@ Use this guide to fully experience the power of a universal dbt Semantic Layer.
 
 - [Create a semantic model](#create-a-semantic-model) with MetricFlow
 - [Define metrics](#define-metrics) with MetricFlow
-- [Test metrics](#test-metrics) with MetricFlow 
+- [Test and query metrics locally](#test-and-query-metrics) with MetricFlow 
 - [Run a production job](#run-a-production-job) in dbt Cloud
 - [Set up dbt Semantic Layer](#setup) in dbt Cloud 
 - [Connect and query API](#connect-and-query-api) with dbt Cloud
@@ -88,24 +88,15 @@ import SlSetUp from '/snippets/_new-sl-setup.md';
 
 ## Connect and query API
 
-To connect and query your metrics using the dbt Semantic Layer and its API, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/deploy/regions) deployment, hosted in North America.
+You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer. In order to do so, you must have a dbt Cloud Team or Enterprise [multi-tenant](/docs/cloud/about-cloud/regions-ip-addresses) deployment, hosted in North America. 
 
-You can query your metrics in a JDBC-enabled tool or use existing first-class integrations with the dbt Semantic Layer. 
+- <span>To learn how to use the JDBC API and what tools you can query it with, refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span>.<br />
 
-1. <span>Refer to the  <a href="https://docs.getdbt.com/docs/dbt-cloud-apis/sl-api-overview" target="_self">{frontMatter.meta.api_name}</a></span> to learn how to use the JDBC API and what tools you can query it with.
+    * To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) with Semantic Layer Only and Metadata Only permissions.
+    * Refer to the [SQL query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) to query metrics using the API.  
 
-    * **Note** To authenticate, you need to [generate a service token](/docs/dbt-cloud-apis/service-tokens) with Semantic Layer Only and Metadata Only permissions. 
+- To learn more about the sophisticated integrations that connect to the dbt Semantic Layer, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
 
-2. Refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations) for more info.
-
-## Next steps
-
-Ready to define your own metrics and bring consistency to data consumers?  Review the following documents to get started:
-
-- [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
-- [Build your metrics](/docs/build/build-metrics-intro)
-- [About MetricFlow](/docs/build/about-metricflow)
-- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
 
 ## FAQs
 
@@ -126,6 +117,14 @@ User data passes through the Semantic Layer on its way back from the warehouse.
 The dbt Semantic Layer is proprietary, however, some components of the dbt Semantic Layer are open source, like dbt-core and MetricFlow. <br /><br />The universal dbt Semantic Layer is available to all Team and Enterprise Plans during public beta. Users on dbt Cloud Developer plans or dbt Core users can use MetricFlow to only define and test metrics locally.</details>
 <br></br> 
 
+## Next steps
+
+Review the following documents to learn more and get started:
+
+- [Build your metrics](/docs/build/build-metrics-intro)
+- [Set up dbt Semantic Layer](docs/use-dbt-semantic-layer/setup-dbt-sl)
+- [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations)
+
 </VersionBlock>
 
 <VersionBlock lastVersion="1.5">
diff --git a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
index f09066a50f0..89cd9bc6ddc 100644
--- a/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
+++ b/website/docs/docs/use-dbt-semantic-layer/sl-architecture.md
@@ -15,7 +15,11 @@ import NewSLChanges from '/snippets/_new-sl-changes.md';
 
 The dbt Semantic Layer allows you to define metrics and use various interfaces to query them. The Semantic Layer does the heavy lifting to find where the queried data exists in your data platform and generates the SQL to make the request (including performing joins). 
 
-The Semantic Layer includes these components:
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-architecture.jpg" width="85%" title="The diagram displays how your data flows using the dbt Semantic Layer and the variety of integration tools it supports."/>
+
+## dbt Semantic Layer components
+
+The dbt Semantic Layer includes the following components:
 
 
 | Components | Information | Developer plans | Team plans | Enterprise plans | License |
@@ -25,6 +29,7 @@ The Semantic Layer includes these components:
 | **Semantic Layer Gateway** | A service that passes queries to MetricFlow server and executes the SQL generated by MetricFlow against the data platform| <br></br>❌| ✅ | ✅ | Proprietary, Cloud (Team & Enterprise) |
 | **Semantic Layer API** | The interfaces that allow users to submit metric queries include the MetricFlow CLI and JDBC API. They also serve as the foundation for building first-class integrations with various tools. | ❌ | ✅ | ✅ | Proprietary, Cloud (Team & Enterprise)|
 
+
 ## Related questions
 
 <details>
diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 9f8a9ac3cfa..2eb9cd6e30b 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -10,19 +10,19 @@ The legacy Semantic Layer will be deprecated in H2 2023. Additionally, the `dbt_
 
 ## Step 1: Migrate metric configs to the new spec
 
-The metrics specification in dbt core is changed in v1.6 to support the integration of MetricFlow. It's strongly recommended that you refer to [Build your metrics](/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer. 
+The metrics specification in dbt Core is changed in v1.6 to support the integration of MetricFlow. It's strongly recommended that you refer to [Build your metrics](/docs/build/build-metrics-intro) and before getting started so you understand the core concepts of the Semantic Layer. 
 
 dbt Labs recommends completing these steps in a local dev environment instead of the IDE: 
 
 1. Create new Semantic Model configs as YAML files in your dbt project.*
 1. Upgrade the metrics configs in your project to the new spec.* 
 1. Delete your old metrics file or remove the `.yml` file extension so they're ignored at parse time. Remove the `dbt-metrics` package from your project. Remove any macros that reference `dbt-metrics`, like `metrics.calculate()`. Make sure that any packages you’re using don't have references to the old metrics spec. 
-1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]`. For example: 
+1. Install the CLI with `pip install "dbt-metricflow[your_adapter_name]"`. For example: 
 
     ```bash
     pip install "dbt-metricflow[snowflake]"
     ```
-    The MetricFlow CLI is not available in the IDE at this time. Support for it is coming soon. 
+    **Note** - The MetricFlow CLI is not available in the IDE at this time. Support is coming soon. 
 
 1. Run `dbt parse`. This parses your project and creates a `semantic_manifest.json` file in your target directory. MetricFlow needs this file to query metrics. If you make changes to your configs, you will need to parse your project again. 
 1. Run `mf list metrics` to view the metrics in your project.
@@ -58,13 +58,13 @@ You might need to audit metric values during the migration to ensure that the hi
 
 1. Run the [dbt-audit](https://github.com/dbt-labs/dbt-audit-helper) helper on both models to compare the metric values.
 
-## Step 3: **Setup the Semantic Layer in a new environment**
+## Step 3: Setup the Semantic Layer in a new environment
 
 This step is only relevant to users who want the legacy and new semantic layer to run in parallel for a short time. This will let you recreate content in downstream tools like Hex and Mode with minimal downtime. If you do not need to recreate assets in these tools skip to step 5.
 
-1. Create a new deployment environment in dbt Cloud and set the dbt version to 1.6.
+1. Create a new deployment environment in dbt Cloud and set the dbt version to 1.6 or higher.
 2. Choose `Only run on a custom branch` and point to the branch that has the updated metric definition
-3. Set the deployment schema to a temporary migration schema i.e `tmp_sl_migration`. Optional, you can create a new database for the migration. 
+3. Set the deployment schema to a temporary migration schema, such as `tmp_sl_migration`. Optional, you can create a new database for the migration. 
 4. Create a job to parse your project, such as `dbt parse`, and run it. Make sure this job succeeds,  There needs to be a successful job in your environment in order to set up the semantic layer
 5. In Account Settings > Projects > Project details click `Configure the Semantic Layer`. Under  **Environment**select the deployment environment you created in the previous step. Save your configuration.
 6. In the Project details page, click `Generate service token` and grant it `Semantic Layer Only` and `Metadata Only` permissions. Save this token securely - you will need it to connect to the semantic layer. 
@@ -75,32 +75,38 @@ At this point, both the new semantic layer and the old semantic layer will be ru
 
 Now that your Semantic Layer is set up, you will need to update any downstream integrations that used the legacy Semantic Layer. 
 
-**Migration guide for Hex**
+### Migration guide for Hex
 
-1. Setup a new connection for the semantic layer for your account. Note your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
+1. Set up a new connection for the Semantic Layer for your account. Something to note is that your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
 
-<LoomVideo id="752e85aabfbf4fa585008a5598f3517a"/>
+<LoomVideo id="752e85aabfbf4fa585008a5598f3517a" />
 
-2. Re-create the dashboards/reports that you’re using the legacy dbt Semantic Layer. 
+2. Re-create the dashboards or reports that use the legacy dbt Semantic Layer. 
 
+3. For specific SQL syntax details, refer to [Querying the API for metric metadata](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) to query metrics using the API.
 
-NOTE: You will need to update your connection to your production environment once you merge your changes to main. Currently, this connection will be pointing at the semantic layer migration environment
+   * **Note** &mdash; You will need to update your connection to your production environment once you merge your changes to main. Currently, this connection will be pointing at the semantic layer migration environment
 
-**Migration guide for Mode**
+### Migration guide for Mode
 
-1. Setup a new connection for the semantic layer for your account. Follow [Mode's docs to setup your connect](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
+1. Set up a new connection for the semantic layer for your account. Follow [Mode's docs to setup your connection](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer).
 
-2. Re-create the dashboards/reports that you’re using in the legacy semantic layer.
+2. Re-create the dashboards or reports that use the legacy dbt Semantic Layer. 
+
+3. For specific SQL syntax details, refer to [Querying the API for metric metadata](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) to query metrics using the API.
    
 ## Step 5: Merge your metrics migration branch to main, and upgrade your production environment to 1.6.
 
-1. Upgrade your production environment to 1.6. Note the old metrics definitions are no longer valid so your dbt jobs will not pass. 
+1. Upgrade your production environment to 1.6 or higher. 
+   * **Note** &mdash; The old metrics definitions are no longer valid so your dbt jobs will not pass. 
+
 2. Merge your updated metrics definitions to main. **At this point the legacy semantic layer will no longer work.**
 
 If you created a new environment in [Step 3](#step-3-setup-the-semantic-layer-in-a-new-environment):
 
-1. Update your Environment in Account Settings > Project Details > Edit Semantic Layer Configuration to point to your production environment
-2. Delete your migration environment. Be sure to update your connections details in any downstream tools to account for the environment change.
+3. Update your Environment in Account Settings > Project Details > Edit Semantic Layer Configuration to point to your production environment
+
+4. Delete your migration environment. Be sure to update your connection details in any downstream tools to account for the environment change.
 
 ## Related docs 
 
@@ -108,3 +114,4 @@ If you created a new environment in [Step 3](#step-3-setup-the-semantic-layer-in
 - [Example dbt project](https://github.com/dbt-labs/jaffle-sl-template)
 - [dbt metrics converter](https://github.com/dbt-labs/dbt-converter)
 - [Why we're deprecating the dbt_metrics package](/blog/deprecating-dbt-metrics) blog post
+- [dbt Semantic Layer API query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) 
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index 54a2f280295..9e89b411795 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -13,21 +13,23 @@ If you're using the legacy Semantic Layer, we **highly** recommend you [upgrade
 
 3. In the **Project Details** page, navigate to the **Semantic Layer** section, and select **Configure Semantic Layer**.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-initial-page.jpg" width="60%" title="Semantic Layer section in the Project Details page"/>
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/new-sl-configure.jpg" width="60%" title="Semantic Layer section in the Project Details page"/>
 
-4. In the **Set Up Semantic Layer Configuration** page, enter the credentials you want the Semantic Layer to use specific to your data platform. 
-**Note:** We recommend credentials have the least privileges required because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
+4. In the **Set Up Semantic Layer Configuration** page, enter the credentials you want the Semantic Layer to use specific to your data platform. We recommend credentials have the least privileges required because your Semantic Layer users will be querying it in downstream applications. At a minimum, the Semantic Layer needs to have read access to the schema(s) that contains the dbt models that you used to build your semantic models.
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="45%" title="Enter the credentials you want the Semantic Layer to use specific to your data platform, and select the deployment environment."/>
 
 5. Select the deployment environment you want for the Semantic Layer and click **Save**.
 
-<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-sl.jpg" width="50%" title="To set up your Semantic Layer configuration, enter your credentials and select the deployment environment. This example selects a deployment environment named 'Production.'"/>
+6. After saving it, you'll be provided with the connection information that allows you to connect to downstream tools. If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
+
+<Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-configure-example.jpg" width="50%" title="After you configure the Semantic Layer, you'll be provided with the connection information to connect to you downstream tools." />
+
+7. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools. For more info on how to integrate with partner integrations, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
-6. You should see connection information that allows you to connect to downstream tools. 
-**Note:** If your tool supports JDBC, save the JDBC URL or individual components (like environment id and host). <!--If it uses the Semantic Layer GraphQL API, save the GraphQL API host information instead.-->
+8. Return to the **Project Details** page, then select **Generate Service Token**.  You will need Semantic Layer Only and Metadata Only [service token](/docs/dbt-cloud-apis/service-tokens) permissions.
 
-7. Return to the **Project Details** page, then  select **Generate Service Token** to create a Semantic Layer Only and Metadata Only [service token](/docs/dbt-cloud-apis/service-tokens).  
 
-8. Save and copy your environment ID, service token, and host, which you'll need to use downstream tools. For more info on how to integrate with partner integrations, refer to [Available integrations](/docs/use-dbt-semantic-layer/avail-sl-integrations).
 
 Great job, you've configured the Semantic Layer 🎉! 
 
diff --git a/website/snippets/_sl-test-and-query-metrics.md b/website/snippets/_sl-test-and-query-metrics.md
index 8f859c1c1f6..7d309466f26 100644
--- a/website/snippets/_sl-test-and-query-metrics.md
+++ b/website/snippets/_sl-test-and-query-metrics.md
@@ -1,6 +1,6 @@
-This section will explain how you can test and query metrics locally. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
+This section will explain how you can locally test and query metrics. Before you begin, refer to [MetricFlow CLI](/docs/build/metricflow-cli) for instructions on installing it and a reference for the CLI commands.
 
-:::tip
+:::important
 
 - dbt Cloud Team or Enterprise &mdash; For public beta, querying metrics in the dbt Cloud IDE isn't yet supported (Coming soon). You'll still be able to run semantic validation on your metrics in the IDE to ensure they are defined correctly. You can also use the MetricFlow CLI to test and query metrics locally. Alternatively, you can test using SQL client tools like DataGrip, DBeaver, or RazorSQL.
 
@@ -9,7 +9,7 @@ This section will explain how you can test and query metrics locally. Before you
 
 **Query and commit your metrics using the CLI:**
 
-MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before preceding to ensure the data exists in your warehouse.
+MetricFlow needs a `semantic_manifest.json` in order to build a semantic graph. To generate a semantic_manifest.json artifact run `dbt parse`. This will create the file in your `/target` directory. If you're working from the Jaffle shop example, run `dbt seed && dbt run` before preceding to ensure the data exists in your warehouse.
 
 1. Make sure you have the MetricFlow CLI installed and up to date.
 2. Run `mf --help` to confirm you have MetricFlow installed and view the available commands.
@@ -17,3 +17,5 @@ MetricFlow needs a semantic_manifest.json in order to build a semantic graph. To
 4. Verify that the metric values are what you expect. To further understand how the metric is being generated, you can view the generated SQL if you type `--explain` in the CLI.
 5. Run `mf validate-configs` to run validation on your semantic models and metrics.
 6. Commit and merge the code changes that contain the metric definitions.
+
+To streamline your metric querying process, you can connect to the [dbt Semantic Layer API](/docs/dbt-cloud-apis/sl-api-overview) to access your metrics programmatically. For SQL syntax, refer to [Querying the API for metric metadata](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) to query metrics using the API.
diff --git a/website/static/img/docs/dbt-cloud/semantic-layer/new-sl-configure.jpg b/website/static/img/docs/dbt-cloud/semantic-layer/new-sl-configure.jpg
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IgmK`10pNd$m;e9(


From 72b2bddd6b835d4f6e08c2a73233b3a0d56be9d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 9 Aug 2023 13:40:35 +0100
Subject: [PATCH 607/703] fix typos and add clarification on primary entity

---
 website/docs/docs/build/dimensions.md      |  7 ++++---
 website/docs/docs/build/semantic-models.md | 19 ++++++++++++-------
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/website/docs/docs/build/dimensions.md b/website/docs/docs/build/dimensions.md
index 2e2776e5588..49ae9045021 100644
--- a/website/docs/docs/build/dimensions.md
+++ b/website/docs/docs/build/dimensions.md
@@ -61,9 +61,9 @@ semantic_models:
       expr: case when quantity > 10 then true else false end
 ```
 
-MetricFlow requires that all dimensions have a primary entity. If your data source does not have a primary entity, you will need to specify one. 
+MetricFlow requires that all dimensions have a primary entity. This is to guarantee unique dimension names. If your data source doesn't have a primary entity, you need to assign the entity a name using the `primary_entity: entity_name` key. It doesn't necessarily have to map to a column in that table and assigning the name doesn't affect query generation.
 
-```yaml:
+```yaml
 semantic_model:
   name: bookings_monthly_source
   description: bookings_monthly_source
@@ -75,7 +75,8 @@ semantic_model:
       agg: sum
       create_metric: true
   primary_entity: booking_id
-  ```
+```
+
 ## Dimensions types
 
 Dimensions have 2 types. This section further explains the definitions and provides examples.
diff --git a/website/docs/docs/build/semantic-models.md b/website/docs/docs/build/semantic-models.md
index bc188eed834..bb56bd212e6 100644
--- a/website/docs/docs/build/semantic-models.md
+++ b/website/docs/docs/build/semantic-models.md
@@ -23,9 +23,9 @@ Semantic models have 6 components and this page explains the definitions with so
 | [Model](#model) | Specifies the dbt model for the semantic model using the `ref` function | Required |
 | [Defaults](#defaults) | The defaults for the model, currently only `agg_time_dimension` is supported.  | Required |
 | [Entities](#entities) | Uses the columns from entities as join keys and indicate their type as primary, foreign, or unique keys with the `type` parameter | Required |
+| [Primary Entity](#primary-entity) | If a primary entity exists, this component is Optional. If the semantic model has no primary entity, then this property is required. | Optional |
 | [Dimensions](#dimensions) | Different ways to group or slice data for a metric, they can be `time` or `categorical` | Required |
 | [Measures](#measures) | Aggregations applied to columns in your data model. They can be the final metric or used as building blocks for more complex metrics | Optional |
-| [Primary Entity](#primary) | If the semantic model has no primary entity, then this property is required. | Optional |
 
 ## Semantic models components
 
@@ -47,6 +47,7 @@ semantic_models:
     primary_entity: >-
       if the semantic model has no primary entity, then this property is required. #Optional if a primary entity exists, otherwise Required
 ```
+
 The following example displays a complete configuration and detailed descriptions of each field:
 
 ```yaml
@@ -117,15 +118,19 @@ Includes important details in the description of the semantic model. This descri
 Specify the dbt model for the semantic model using the [`ref` function](/reference/dbt-jinja-functions/ref).
 
 ### Defaults
-Defaults for the semantic model. Currently only `agg_time_dimension`. `agg_time_dimension` represents the default time dimensions for measures. This can be overriden by adding the `agg_time_dimension` key directly to a measure - see [Dimensions](/docs/build/dimensions) for examples. 
+
+Defaults for the semantic model. Currently only `agg_time_dimension`. `agg_time_dimension` represents the default time dimensions for measures. This can be overridden by adding the `agg_time_dimension` key directly to a measure - see [Dimensions](/docs/build/dimensions) for examples. 
 ### Entities 
 
 To specify the [entities](/docs/build/entities) in your model, use their columns as join keys and indicate their `type` as primary, foreign, or unique keys with the type parameter.
 
-### Primary Entity
-If your data source does not have a primary entity, you need to manually specify one. Metricflow requers that all dimensions be tied to an entity. You can define a primary entity using the following configs:
+### Primary entity
+
+MetricFlow requires that all dimensions be tied to an entity. This is to guarantee unique dimension names. If your data source doesn't have a primary entity, you need to assign the entity a name using the `primary_entity: entity_name` key. It doesn't necessarily have to map to a column in that table and assigning the name doesn't affect query generation. 
+
+You can define a primary entity using the following configs:
 
-```yaml:
+```yaml
 semantic_model:
   name: bookings_monthly_source
   description: bookings_monthly_source
@@ -137,7 +142,7 @@ semantic_model:
       agg: sum
       create_metric: true
   primary_entity: booking_id
-```
+  ```
 
 <Tabs>
 
@@ -158,7 +163,7 @@ This example shows a semantic model with three entities and their entity types:
 To reference a desired column, use the actual column name from the model in the `name` parameter. You can also use `name` as an alias to rename the column, and the `expr` parameter to refer to the original column name or a SQL expression of the column.
 
 
-```yml
+```yaml
 entity:
   - name: transaction
     type: primary

From d0f4f70828d8735357dd2d99b61002753c146bc2 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 14:22:31 +0100
Subject: [PATCH 608/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 41c45873982..a3fb5e755e0 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -137,7 +137,7 @@ To query metric values, here are the following parameters that are available:
 | --------- | -----------| ------------ | -------------------- |
 | `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=['revenue']` | Required    |
 | `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=['user__country', 'metric_time']`     | Optional   |
-| `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | ```group_by=[`Dimension('metric_time').``` <br/> ```grain('week\|day\|month\|quarter\|year')]``` | Optional     |
+| `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | `group_by=[Dimension('metric_time')` <br/> `grain('week\|day\|month\|quarter\|year')]` | Optional     |
 | `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where="Dimension('customer__country') = 'US')"`   | Optional   |
 | `limit`   | Limit the data returned    | `limit=10` | Optional  |
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |

From e91361c470f115af95cc337aecbcfd25eddd0b3b Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 14:22:37 +0100
Subject: [PATCH 609/703] Update website/docs/docs/dbt-cloud-apis/sl-jdbc.md

---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index a3fb5e755e0..1497dd61a63 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -138,7 +138,7 @@ To query metric values, here are the following parameters that are available:
 | `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=['revenue']` | Required    |
 | `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=['user__country', 'metric_time']`     | Optional   |
 | `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | `group_by=[Dimension('metric_time')` <br/> `grain('week\|day\|month\|quarter\|year')]` | Optional     |
-| `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where="Dimension('customer__country') = 'US')"`   | Optional   |
+| `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where=Dimension('customer__country') = 'US')"`   | Optional   |
 | `limit`   | Limit the data returned    | `limit=10` | Optional  |
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
 | `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |

From 2ba8bf22c05a89621e5e52961640f48f6d625078 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 17:19:11 +0100
Subject: [PATCH 610/703] Update cumulative-metrics.md

update type to cumulative
---
 website/docs/docs/build/cumulative-metrics.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index bdc6c1fc233..3b4d2327e8a 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Cumulative metrics aggregate a measure over a given accumulation window. If no window is specified, the window is considered infinite and accumulates values over all time. You will need to create the [time spine model](/docs/build/metricflow-time-spine) before you add cumulative metrics.
 
-This metric is common for calculating things like weekly active users, or month to date revenue.  The parameters, description, and type for cumulative metrics are: 
+This metric is common for calculating things like weekly active users, or month-to-date revenue.  The parameters, description, and type for cumulative metrics are: 
 
 | Parameter | Description | Type |
 | --------- | ----------- | ---- |
@@ -27,7 +27,7 @@ The following displays the complete specification for cumulative metrics, along
 metrics:
   - name: The metric name # Required
     description: The metric description # Optional
-    type: simple # Required
+    type: cumulative # Required
     label: The value that will be displayed in downstream tools # Required
     type_params: # Required
       measure: The measure you are referencing # Required
@@ -82,7 +82,7 @@ This section details examples of when you specify and don't specify window optio
 
 If a window option is specified, the MetricFlow framework applies a sliding window to the underlying measure. 
 
-Suppose the underlying measure `customers` which is configured to count the unique customers making orders at the Jaffle shop.
+Suppose the underlying measure `customers` is configured to count the unique customers making orders at the Jaffle shop.
 
 ```yaml
 measures:
@@ -127,7 +127,7 @@ Suppose you (a subscription-based company for the sake of this example) have an
 * `event_type`: (integer) a column that populates with +1 to indicate an added subscription, or -1 to indicate a deleted subscription. 
 * `revenue`: (integer) a column that multiplies `event_type` and `subscription_revenue` to depict the amount of revenue added or lost for a specific date. 
 
-Using cumulative metrics without specifying a window, you can calculate running totals for metrics like the count of active subscriptions and revenue at any point in time. The following configuration YAML displays creating such cumulative metrics to obtain current revenue or total number of active subscriptions as a cumulative sum:
+Using cumulative metrics without specifying a window, you can calculate running totals for metrics like the count of active subscriptions and revenue at any point in time. The following configuration YAML displays creating such cumulative metrics to obtain current revenue or the total number of active subscriptions as a cumulative sum:
 
 ```yaml
 measures:

From 328af29a297fb9a483002875a1cd0908e18dd24b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 9 Aug 2023 18:51:45 +0100
Subject: [PATCH 611/703] fix docs to reflect upcoming partner integrations

---
 website/docs/guides/migration/sl-migration.md | 5 +++++
 website/snippets/_sl-partner-links.md         | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 2eb9cd6e30b..357c1248a77 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -77,6 +77,11 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 ### Migration guide for Hex
 
+:::important Hex integration coming soon
+
+Hex is currently working on an integration with the new dbt Semantic Layer. This section will have updated instructions once the integration is available.
+:::
+
 1. Set up a new connection for the Semantic Layer for your account. Something to note is that your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:
 
 <LoomVideo id="752e85aabfbf4fa585008a5598f3517a" />
diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index 40456fcfc17..cd92f6ca861 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -1,8 +1,8 @@
 <!-- turn this list into sections once more docs are provided -->
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
 
-1. **Hex** &mdash; Public docs are coming soon, refer to [this Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info on how to integrate with Hex. 
 1. **Mode** &mdash; Refer to the [Mode docs](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer) for info on how to integrate with Mode.
+1. **Hex** &mdash; Hex is currently working on an integration with the new dbt Semantic Layer. Until then, refer to [this Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info. 
 1. **Google Sheets** &mdash; Google Sheets integration coming soon.
 1. **Tools that allows you to write SQL** &mdash; They must meet one of the two criteria: 
     * Supports a generic JDBC driver option (such as DataGrip) or 

From 90b04b14388a1af464dc37faaf421d8a2c3b2ff7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 20:06:26 +0100
Subject: [PATCH 612/703] Update website/docs/guides/migration/sl-migration.md

---
 website/docs/guides/migration/sl-migration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/migration/sl-migration.md b/website/docs/guides/migration/sl-migration.md
index 357c1248a77..baa7ae4a567 100644
--- a/website/docs/guides/migration/sl-migration.md
+++ b/website/docs/guides/migration/sl-migration.md
@@ -79,7 +79,7 @@ Now that your Semantic Layer is set up, you will need to update any downstream i
 
 :::important Hex integration coming soon
 
-Hex is currently working on an integration with the new dbt Semantic Layer. This section will have updated instructions once the integration is available.
+Hex’s Semantic Layer integration will be available for use in the coming weeks. This section will have updated instructions once the integration is available.
 :::
 
 1. Set up a new connection for the Semantic Layer for your account. Something to note is that your old connection will still work. The following Loom video guides you in setting up your Semantic Layer with Hex:

From 9ee477cc25a6cece0bde168931ce9569e21cb651 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 9 Aug 2023 20:06:32 +0100
Subject: [PATCH 613/703] Update website/snippets/_sl-partner-links.md

---
 website/snippets/_sl-partner-links.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index cd92f6ca861..3e4173a6ae3 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -2,7 +2,7 @@
 The dbt Semantic Layer integrations are capable of querying dbt metrics, importing definitions, surfacing the underlying data in partner tools, and more.  These are the following tools that integrate with the dbt Semantic Layer:
 
 1. **Mode** &mdash; Refer to the [Mode docs](https://mode.com/help/articles/supported-databases/#dbt-semantic-layer) for info on how to integrate with Mode.
-1. **Hex** &mdash; Hex is currently working on an integration with the new dbt Semantic Layer. Until then, refer to [this Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info. 
+1. **Hex** &mdash; Hex’s Semantic Layer integration will be available in the coming weeks. Until then, refer to [this Loom video](https://www.loom.com/share/752e85aabfbf4fa585008a5598f3517a) for more info. 
 1. **Google Sheets** &mdash; Google Sheets integration coming soon.
 1. **Tools that allows you to write SQL** &mdash; They must meet one of the two criteria: 
     * Supports a generic JDBC driver option (such as DataGrip) or 

From 3842a0dd466520134242a59998017cec1c10104c Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Wed, 9 Aug 2023 15:25:29 -0400
Subject: [PATCH 614/703] Update
 website/docs/docs/cloud/cloud-cli-installation.md

---
 website/docs/docs/cloud/cloud-cli-installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index c300f6478a1..ecd48abae42 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -12,7 +12,7 @@ The following installation instructions are for the dbt Cloud CLI, currently in
 
 ## Installing dbt Cloud CLI
 
-### Install and update with Brew (recommended)
+### Install and update with Brew on MacOS (recommended)
 
 Install the CLI: 
 

From 215b0aac42757a7d082f12ab55d974517f03bdac Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Wed, 9 Aug 2023 15:28:37 -0400
Subject: [PATCH 615/703] Update
 website/docs/docs/cloud/cloud-cli-installation.md

---
 website/docs/docs/cloud/cloud-cli-installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index ecd48abae42..933a1b5f04d 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -35,7 +35,7 @@ brew update
 brew upgrade dbt-cloud-cli
 ```
 
-### Manually install (Windows only)
+### Manually install (Windows and Linux)
 
 1. Download the latest darwin release from [Github](https://github.com/dbt-labs/dbti/releases).
 2. Place the `dbt` executable in `/usr/local/bin` or `/usr/.local/bin` if `/usr/local/bin` doesn’t exist.

From fee8c5f75e90d0c86f077f54633d97621cae0ef5 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Wed, 9 Aug 2023 16:32:50 -0400
Subject: [PATCH 616/703] Apply suggestions from code review

---
 .../docs/docs/cloud/cloud-cli-installation.md  | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index 933a1b5f04d..4ed5e57665b 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -28,7 +28,7 @@ which dbt
 dbt --help
 ```
 
-Upgrade the CLI:
+### Upgrade the CLI
 
 ```bash
 brew update
@@ -37,26 +37,26 @@ brew upgrade dbt-cloud-cli
 
 ### Manually install (Windows and Linux)
 
-1. Download the latest darwin release from [Github](https://github.com/dbt-labs/dbti/releases).
-2. Place the `dbt` executable in `/usr/local/bin` or `/usr/.local/bin` if `/usr/local/bin` doesn’t exist.
-3. Navigate to `/usr/local/bin` / `/usr/.local/bin` in Finder and double click on the `dbt` executable:
+1. Download the latest darwin release from [Github](https://github.com/dbt-labs/dbt-cli/releases).
+2. Add the `dbt` executable to your path.
+3. Navigate to your `dbt` path in Finder and double-click on the `dbt` executable:
     1. You will receive a message that the executable is untrusted and cannot be run. Override this by right-clicking the executable and selecting “Open”. A new prompt will pop up, allowing you to open the executable.
     2. After clicking “Open”, a terminal window will appear. The window can be closed, and you can now invoke `dbt` from any other terminal*.
         - You may need to deal with conflicts due to other `dbt` installations. Run `which dbt` to see the location of the `dbt` installation you’re using. If you get `/usr/local/bin/dbt` from `which dbt`, you’re using the one you just installed. If not, you may need to uninstall other installations, directly invoke the executable, or handle path conflicts differently, such as using an alias for the new executable.
 4. Move to a directory with a dbt project, and create a `dbt_cloud.yml` file containing your `project-id` from dbt Cloud.
 5. Invoke `dbt --help` from your terminal to see a list of supported commands.
 
-#### Updating dbt Cloud installation (Windows only)
+#### Updating dbt Cloud installation (Windows + Linux)
 
 Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-only) and replace the existing `dbt` executable with the new one. You should not have to go through the security steps again.
 
 ## Setting up the CLI
 
-1. Ensure that you have created a project in dbt Cloud.
+1. Ensure that you have created a project in [dbt Cloud](https://cloud.getdbt.com/).
 
-2. Ensure that your personal [development credentials](#glossary) are set on the project
+2. Ensure that your personal [development credentials](https://cloud.getdbt.com/settings/profile/credentials) are set on the project.
 
-3. Create an environment variable with your [dbt cloud API key](#glossary):
+3. Create an environment variable with your [dbt cloud API key](https://cloud.getdbt.com/settings/profile#api-access):
 
 ```bash
 
@@ -82,7 +82,7 @@ Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-
 ```
 
 6. Ensure that a `dbt_cloud.yml` file exists in the project directory. The file is required to have a `project-id` field with a valid [project ID](#glossary):
-
+You can find your project ID by selecting your project and clicking on **documentation** in the navigation bar. Your project ID is the number in the URL: https://cloud.getdbt.com/documentation/1/projects/{project id}.
 ```bash
 > $ pwd
 /Users/user/dbt-projects/jaffle_shop

From f4cc0be61f98b77ebb8a4a44ec3601dc493c48bd Mon Sep 17 00:00:00 2001
From: Greg McKeon <greg-mckeon@users.noreply.github.com>
Date: Wed, 9 Aug 2023 17:16:16 -0400
Subject: [PATCH 617/703] Update cloud-cli-installation.md

---
 website/docs/docs/cloud/cloud-cli-installation.md | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index 4ed5e57665b..383f1dc364d 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -28,7 +28,7 @@ which dbt
 dbt --help
 ```
 
-### Upgrade the CLI
+#### Upgrade the CLI with Brew
 
 ```bash
 brew update
@@ -37,14 +37,10 @@ brew upgrade dbt-cloud-cli
 
 ### Manually install (Windows and Linux)
 
-1. Download the latest darwin release from [Github](https://github.com/dbt-labs/dbt-cli/releases).
+1. Download the latest release for your platform from [Github](https://github.com/dbt-labs/dbt-cli/releases).
 2. Add the `dbt` executable to your path.
-3. Navigate to your `dbt` path in Finder and double-click on the `dbt` executable:
-    1. You will receive a message that the executable is untrusted and cannot be run. Override this by right-clicking the executable and selecting “Open”. A new prompt will pop up, allowing you to open the executable.
-    2. After clicking “Open”, a terminal window will appear. The window can be closed, and you can now invoke `dbt` from any other terminal*.
-        - You may need to deal with conflicts due to other `dbt` installations. Run `which dbt` to see the location of the `dbt` installation you’re using. If you get `/usr/local/bin/dbt` from `which dbt`, you’re using the one you just installed. If not, you may need to uninstall other installations, directly invoke the executable, or handle path conflicts differently, such as using an alias for the new executable.
-4. Move to a directory with a dbt project, and create a `dbt_cloud.yml` file containing your `project-id` from dbt Cloud.
-5. Invoke `dbt --help` from your terminal to see a list of supported commands.
+3. Move to a directory with a dbt project, and create a `dbt_cloud.yml` file containing your `project-id` from dbt Cloud.
+4. Invoke `dbt --help` from your terminal to see a list of supported commands.
 
 #### Updating dbt Cloud installation (Windows + Linux)
 

From f5f2a64bf1d73d392d68ca48e0286977cab3dd8b Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Wed, 9 Aug 2023 16:37:48 -0700
Subject: [PATCH 618/703] Update sidebar, landing page, and add new pages

---
 ...023-07-03-data-vault-2-0-with-dbt-cloud.md |  2 +-
 .../07-June-2023/product-docs-jun.md          |  4 +-
 .../09-April-2023/product-docs.md             |  2 +-
 .../23-Dec-2022/new-jobs-default-as-off.md    |  2 +-
 website/docs/docs/deploy/ci-jobs.md           |  2 +-
 website/docs/docs/deploy/dbt-cloud-job.md     | 26 -------
 .../docs/docs/deploy/deploy-environments.md   |  6 +-
 .../{job-settings.md => deploy-jobs.md}       | 61 ++++++++++++++---
 .../docs/docs/deploy/deployment-overview.md   | 26 ++-----
 website/docs/docs/deploy/deployment-tools.md  |  2 +-
 website/docs/docs/deploy/job-commands.md      |  1 -
 website/docs/docs/deploy/job-scheduler.md     |  2 +-
 website/docs/docs/deploy/job-triggers.md      | 68 -------------------
 website/docs/docs/deploy/jobs.md              | 21 ++++++
 ...uctionizing-your-dbt-databricks-project.md |  8 +--
 ...abricks-workflows-to-run-dbt-cloud-jobs.md |  4 +-
 website/docs/quickstarts/bigquery-qs.md       |  4 +-
 website/docs/quickstarts/databricks-qs.md     |  4 +-
 website/docs/quickstarts/redshift-qs.md       |  4 +-
 website/docs/quickstarts/snowflake-qs.md      |  4 +-
 website/sidebars.js                           | 13 +---
 website/static/_redirects                     |  2 +
 22 files changed, 111 insertions(+), 157 deletions(-)
 delete mode 100644 website/docs/docs/deploy/dbt-cloud-job.md
 rename website/docs/docs/deploy/{job-settings.md => deploy-jobs.md} (68%)
 delete mode 100644 website/docs/docs/deploy/job-triggers.md
 create mode 100644 website/docs/docs/deploy/jobs.md

diff --git a/website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md b/website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md
index e1351034f66..a6f3682f9e9 100644
--- a/website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md
+++ b/website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md
@@ -97,7 +97,7 @@ dbt Cloud includes **built-in Git** with accessible features directly from its I
 
 The biggest boon to Data Vault developer productivity in dbt Cloud are the **DataOps** and **Data Warehouse Automation** features of dbt Cloud. Each Data Vault developer gets their own development environment to work in and there is no complicated set up process to go through.
 
-Commit your work, create a pull request, and have automated code review enabled by dbt Cloud [**jobs**](https://docs.getdbt.com/docs/deploy/dbt-cloud-job) that can be defined for each environment separately (e.g., testing, QA, production). Together with dbt [**tags**](https://docs.getdbt.com/reference/resource-configs/tags), the feature allows you to orchestrate your project in an efficient and powerful way.
+Commit your work, create a pull request, and have automated code review enabled by dbt Cloud [**jobs**](https://docs.getdbt.com/docs/deploy/jobs) that can be defined for each environment separately (e.g., testing, QA, production). Together with dbt [**tags**](https://docs.getdbt.com/reference/resource-configs/tags), the feature allows you to orchestrate your project in an efficient and powerful way.
 
 ### Auditable data
 
diff --git a/website/docs/docs/dbt-versions/release-notes/07-June-2023/product-docs-jun.md b/website/docs/docs/dbt-versions/release-notes/07-June-2023/product-docs-jun.md
index 286b271b05d..469d2ac362b 100644
--- a/website/docs/docs/dbt-versions/release-notes/07-June-2023/product-docs-jun.md
+++ b/website/docs/docs/dbt-versions/release-notes/07-June-2023/product-docs-jun.md
@@ -13,11 +13,11 @@ Here's what's new to [docs.getdbt.com](http://docs.getdbt.com/) in June:
 
 ## ☁ Cloud projects
 
-- We clarified the nuances of [CI and CI jobs](/docs/deploy/continuous-integration), updated the [Scheduler content](/docs/deploy/job-scheduler), added two new pages for the job settings and run visibility, moved the project state page to the [Syntax page](/reference/node-selection/syntax), and provided a landing page for [Deploying with Cloud](/docs/deploy/dbt-cloud-job) to help readers navigate the content better.
+- We clarified the nuances of [CI and CI jobs](/docs/deploy/continuous-integration), updated the [Scheduler content](/docs/deploy/job-scheduler), added two new pages for the job settings and run visibility, moved the project state page to the [Syntax page](/reference/node-selection/syntax), and provided a landing page for [Deploying with Cloud](/docs/deploy/jobs) to help readers navigate the content better.
 - We reformatted the [Supported data platforms page](/docs/supported-data-platforms) by adding dbt Cloud to the page, splitting it into multiple pages, using cards to display verified adapters, and moving the [Warehouse setup pages](/docs/core/connect-data-platform/about-core-connections) to the Docs section. 
 - We launched a new [Lint and format page](/docs/cloud/dbt-cloud-ide/lint-format), which highlights the awesome new dbt Cloud IDE linting/formatting function.
 - We enabled a connection between [dbt Cloud release notes](/docs/dbt-versions/dbt-cloud-release-notes) and the dbt Slack community. This means new dbt Cloud release notes are automatically sent to the slack community [#dbt-cloud channel](https://getdbt.slack.com/archives/CMZ2V0X8V) via RSS feed, keeping users up to date with changes that may affect them. 
-- We’ve added two new docs links in the dbt Cloud Job settings user interface (UI). This will provide additional guidance and help users succeed when setting up a dbt Cloud job: [job commands](/docs/deploy/job-commands) and [job triggers](/docs/deploy/job-triggers).    
+- We’ve added two new docs links in the dbt Cloud Job settings user interface (UI). This will provide additional guidance and help users succeed when setting up a dbt Cloud job: [job commands](/docs/deploy/job-commands) and job triggers.    
 - We added information related to the newly created [IT license](/docs/cloud/manage-access/about-user-access#license-based-access-control), available for Team and Enterprise plans. 
 - We added a new [Supported browser page](/docs/cloud/about-cloud/browsers), which lists the recommended browsers for dbt Cloud.
 - We launched a new page informing users of [new Experimental features option](/docs/dbt-versions/experimental-features) in dbt Cloud.
diff --git a/website/docs/docs/dbt-versions/release-notes/09-April-2023/product-docs.md b/website/docs/docs/dbt-versions/release-notes/09-April-2023/product-docs.md
index 991fc9be1f4..d30bcf85b99 100644
--- a/website/docs/docs/dbt-versions/release-notes/09-April-2023/product-docs.md
+++ b/website/docs/docs/dbt-versions/release-notes/09-April-2023/product-docs.md
@@ -20,7 +20,7 @@ Hello from the dbt Docs team: @mirnawong1, @matthewshaver, @nghi-ly, and @runleo
   * [dbt Cloud quickstart guide](/quickstarts/starburst-galaxy), 
   * [connection page](/docs/cloud/connect-data-platform/connect-starburst-trino), 
   * [set up page](/docs/core/connect-data-platform/trino-setup), and [config page](/reference/resource-configs/trino-configs). 
-- Enhanced [dbt Cloud jobs page](/docs/deploy/dbt-cloud-job) and section to include conceptual info on the queue time, improvements made around it, and about failed jobs. 
+- Enhanced [dbt Cloud jobs page](/docs/deploy/jobs) and section to include conceptual info on the queue time, improvements made around it, and about failed jobs. 
 - Check out the April dbt [Cloud release notes](/docs/dbt-versions/dbt-cloud-release-notes)
 
 ## 🎯 Core projects 
diff --git a/website/docs/docs/dbt-versions/release-notes/23-Dec-2022/new-jobs-default-as-off.md b/website/docs/docs/dbt-versions/release-notes/23-Dec-2022/new-jobs-default-as-off.md
index 0e26d8dc628..bdc89b4abde 100644
--- a/website/docs/docs/dbt-versions/release-notes/23-Dec-2022/new-jobs-default-as-off.md
+++ b/website/docs/docs/dbt-versions/release-notes/23-Dec-2022/new-jobs-default-as-off.md
@@ -8,7 +8,7 @@ tags: [Dec-2022]
 
 To help save compute time, new jobs will no longer be triggered to run by default. When you create a new job in dbt Cloud, you can trigger the job to run by selecting **Run on schedule** and completing the desired schedule and timing information.
 
-For more information, refer to [Job triggers](/docs/deploy/job-triggers).
+For more information, refer to [Deploy jobs](/docs/deploy/deploy-jobs).
 
 <Lightbox src="/img/docs/release-notes/new-jobs-default-as-off.png" title="Default for newly created jobs"/>
 
diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index a1f12d19f84..74a39dd7828 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -58,7 +58,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
     - **Compare changes against an environment (Deferral)** &mdash; By default, it’s set to the **Production** environment if you created one. This option allows dbt Cloud to check the state of the code in the PR against the code running in the deferred environment, so as to only check the modified code, instead of building the full table or the entire DAG.
 
     :::info
-    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ [deploy jobs](/docs/deploy/job-settings) that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
+    Older versions of dbt Cloud only allow you to defer to a specific job instead of an environment. Deferral to a job compares state against the project code that was run in the deferred job's last successful run. While deferral to an environment is more efficient as dbt Cloud will compare against the project representation (which is stored in the `manifest.json`) of the last successful deploy job run that executed in the deferred environment. By considering _all_ [deploy jobs](/docs/deploy/deploy-jobs) that run in the deferred environment, dbt Cloud will get a more accurate, latest project representation state.
     :::
 
     - **Generate docs on run** &mdash; Enable this option if you want to [generate project docs](/docs/collaborate/build-and-view-your-docs) when this job runs. This option is disabled by default since most teams do not want to test doc generation on every CI check.
diff --git a/website/docs/docs/deploy/dbt-cloud-job.md b/website/docs/docs/deploy/dbt-cloud-job.md
deleted file mode 100644
index fa9eead2d3b..00000000000
--- a/website/docs/docs/deploy/dbt-cloud-job.md
+++ /dev/null
@@ -1,26 +0,0 @@
----
-title: "dbt Cloud jobs"
-id: "dbt-cloud-job"
-description: "Manage, setup, and configure your dbt Cloud job using elegant job commands and triggers."
-hide_table_of_contents: true
-tags: ["scheduler"]
----
-
-Manage, set up, and automate your dbt jobs using robust custom job settings. You can use the job scheduler to configure when and how your jobs run, helping you keep production data fresh on a timely basis.
-
-This portion of our documentation will go over dbt Cloud's various job settings using:
-
-- [Job settings](/docs/deploy/job-settings) &mdash; Intuitively navigate the user interface to create new dbt jobs or edit existing ones.
-- [Job commands](/docs/deploy/job-commands) &mdash; Use job commands to configure dbt commands on a schedule.
-- [Job triggers](/docs/deploy/job-triggers) &mdash; You can configure when and how dbt should run your job, such as:
-	* Running on scheduled days or cron schedules 
-	* Setting up continuous integration (CI) to run when someone opens a new pull request in your dbt repository
-	* Using the API to trigger jobs
-
-<DocCarousel slidesPerView={1}>
-
-<Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/job-commands.gif" width="75%" title="Setting up a job and configuring checkbox and dbt commands"/>
-
-<Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
-
-</DocCarousel>
diff --git a/website/docs/docs/deploy/deploy-environments.md b/website/docs/docs/deploy/deploy-environments.md
index 9421d664739..bdcf36b7a30 100644
--- a/website/docs/docs/deploy/deploy-environments.md
+++ b/website/docs/docs/deploy/deploy-environments.md
@@ -10,7 +10,7 @@ Deployment environments in dbt Cloud are crucial for deploying dbt jobs in produ
 - The warehouse connection information (including the target database/schema settings)
 - The version of your code to execute
 
-A dbt Cloud project can have multiple deployment environments, providing you the flexibility and customization to tailor the execution of dbt jobs. You can use deployment environments to [create and schedule jobs](/docs/deploy/job-settings#create-and-schedule-jobs), [enable continuous integration](/docs/deploy/continuous-integration), or more based on your specific needs or requirements.
+A dbt Cloud project can have multiple deployment environments, providing you the flexibility and customization to tailor the execution of dbt jobs. You can use deployment environments to [create and schedule jobs](/docs/deploy/deploy-jobs#create-and-schedule-jobs), [enable continuous integration](/docs/deploy/continuous-integration), or more based on your specific needs or requirements.
 
 :::tip Learn how to manage dbt Cloud environments
 To learn different approaches to managing dbt Cloud environments and recommendations for your organization's unique needs, read [dbt Cloud environment best practices](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview).
@@ -191,7 +191,7 @@ This section allows you to determine the credentials that should be used when co
 ## Related docs
 
 - [dbt Cloud environment best practices](https://docs.getdbt.com/guides/best-practices/environment-setup/1-env-guide-overview)
-- [Deploy dbt jobs](/docs/deploy/dbt-cloud-job)
-- [Deploy CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
+- [CI jobs](/docs/deploy/continuous-integration)
 - [Delete a job or environment in dbt Cloud](/faqs/Environments/delete-environment-job)
 
diff --git a/website/docs/docs/deploy/job-settings.md b/website/docs/docs/deploy/deploy-jobs.md
similarity index 68%
rename from website/docs/docs/deploy/job-settings.md
rename to website/docs/docs/deploy/deploy-jobs.md
index 8df0f48925d..9f903dfe850 100644
--- a/website/docs/docs/deploy/job-settings.md
+++ b/website/docs/docs/deploy/deploy-jobs.md
@@ -1,10 +1,10 @@
 ---
-title: "Job settings"
-description: "Learn how to create and schedule jobs in dbt Cloud for the scheduler to run. When you run with dbt Cloud, you get built-in observability, logging, and alerting." 
+title: "Deploy jobs"
+description: "Learn how to create and schedule deploy jobs in dbt Cloud for the scheduler to run. When you run with dbt Cloud, you get built-in observability, logging, and alerting." 
 tags: [scheduler]
 ---
 
-Jobs make it easy to run dbt commands against a project in your cloud data platform, triggered either by schedule or events. Each job run in dbt Cloud will have an entry in the job's run history and a detailed run overview, which provides you with:
+You can use deploy jobs to build production data assets. Deploy jobs make it easy to run dbt commands against a project in your cloud data platform, triggered either by schedule or events. Each job run in dbt Cloud will have an entry in the job's run history and a detailed run overview, which provides you with:
 
 - Job trigger type
 - Commit SHA
@@ -13,11 +13,11 @@ Jobs make it easy to run dbt commands against a project in your cloud data platf
 - Job run details, including run timing, [model timing data](#model-timing), and [artifacts](/docs/deploy/artifacts)
 - Detailed run steps with logs and their run step statuses
 
-You can create a deploy job and configure it to run on [scheduled days and times](/docs/deploy/job-triggers#schedule-days) or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules). 
+You can create a deploy job and configure it to run on [scheduled days and times](#schedule-days) or enter a [custom cron schedule](#custom-cron-schedules). 
 
 :::tip Join our beta 
 
-dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs, refer to [Create and schedule jobs (Beta version)](/docs/deploy/job-settings?version=beta#create-and-schedule-jobs) on this page.
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs. For docs, refer to [Create and schedule jobs (Beta version)](/docs/deploy/deploy-jobs?version=beta#create-and-schedule-jobs) on this page.
 
 If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
 
@@ -57,9 +57,9 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 
 <Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/execution-settings.jpg" width="85%" title="Configuring your execution job settings"/>
 
-6. Under the **Triggers** section, you can configure when and how dbt will trigger the deploy job. Refer to [job triggers](/docs/deploy/job-triggers) for more details.
+6. Under the **Triggers** section, you can configure when and how dbt will trigger the deploy job.
 
-    * **Schedule** tab &mdash; Use the **Run on schedule** toggle to configure your job to run on [scheduled](/docs/deploy/job-triggers#schedule-days) days and time, or enter a [custom cron schedule](/docs/deploy/job-triggers#custom-cron-schedules).
+    * **Schedule** tab &mdash; Enable the **Run on schedule** option. Use either the [scheduled days](#schedule-days) or the [custom cron schedule](#custom-cron-schedule) method to configure your desired days, times, and intervals for running your deploy job.
     * **Continuous Integration** tab &mdash; Configure [continuous integration (CI)](/docs/deploy/continuous-integration) to run when someone opens a new pull request in your dbt repository.
     * **API** tab &mdash; Use the [dbt API](/docs/dbt-cloud-apis/overview) to trigger a job.
 
@@ -81,7 +81,7 @@ If you're interested in joining our beta, please fill out our Google Form to [si
     - **Run source freshness** &mdash; Enable this option to invoke the `dbt source freshness` command before running the deploy job. Refer to [Source freshness](/docs/deploy/source-freshness) for more details.
 4. Options in the **Schedule** section:
     - **Run on schedule** &mdash; Enable this option to run the deploy job on a set schedule.
-    - **Timing** &mdash; Specify whether to [schedule](/docs/deploy/job-triggers#schedule-days) the deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](/docs/deploy/job-triggers#custom-cron-schedule).
+    - **Timing** &mdash; Specify whether to [schedule](#schedule-days) the deploy job using **Frequency** that runs the job at specific times of day, **Specific Intervals** that runs the job every specified number of hours, or **Cron Schedule** that runs the job specified using [cron syntax](#custom-cron-schedule).
     - **Days of the Week** &mdash; By default, it’s set to every day when **Frequency** or **Specific Intervals** is chosen for **Timing**.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/create-deploy-job.png" width="90%" title="Example of Deploy Job page in dbt Cloud UI"/>
@@ -104,3 +104,48 @@ If you're interested in joining our beta, please fill out our Google Form to [si
 </TabItem>
 
 </Tabs>
+
+### Schedule days
+
+To set your job's schedule, use the **Schedule Days** option to choose specific days of the week, and select customized hours or intervals.
+
+Under **Timing**, you can either use customizable hours for jobs that need to run frequently throughout the day or exact intervals for jobs that need to run at specific times:
+
+- **Every n hours** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to "every 2 hours", the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
+
+- **At exact intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
+
+:::info
+
+dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
+
+- 0 means 12am (midnight) UTC
+- 12 means 12pm (afternoon) UTC
+- 23 means 11pm UTC
+
+:::
+
+### Custom cron schedule
+
+To fully customize the scheduling of your job, choose the **Custom cron schedule** option and use the cron syntax. With this syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
+
+
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
+
+Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
+
+Refer to the following example snippets:
+
+
+- `0 * * * *`: Every hour, at minute 0
+- `*/5 * * * *`: Every 5 minutes
+- `5 4 * * *`: At exactly 4:05 AM UTC
+- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
+- `0 0 */2 * *`: At midnight UTC every other day
+- `0 0 * * 1`: At midnight UTC every Monday.
+
+## Related docs
+
+- [Artifacts](/docs/deploy/artifacts)
+- [Job commands](/docs/deploy/job-commands)
+- [Webhooks](/docs/deploy/webhooks)
\ No newline at end of file
diff --git a/website/docs/docs/deploy/deployment-overview.md b/website/docs/docs/deploy/deployment-overview.md
index ccc01738f6e..5883ecaa3f1 100644
--- a/website/docs/docs/deploy/deployment-overview.md
+++ b/website/docs/docs/deploy/deployment-overview.md
@@ -29,21 +29,15 @@ Learn how to use dbt Cloud's features to help your team ship timely and quality
     icon="dbt-bit"/>
 
 <Card
-    title="Continuous integration"
-    body="Set up CI checks so you can build and test any modified code in a staging environment when you open PRs and push new commits to your dbt repository."
-    link="/docs/deploy/continuous-integration"
+    title="Deploy jobs"
+    body="Create and schedule jobs for the dbt Cloud scheduler to run."
+    link="/docs/deploy/deploy-jobs"
     icon="dbt-bit"/>
 
-</div> <br /> 
-
-## dbt Cloud jobs
-
-<div className="grid--3-col">
-
 <Card
-    title="Job settings"
-    body="Create and schedule jobs for the dbt Cloud scheduler to run."
-    link="/docs/deploy/job-settings"
+    title="Continuous integration"
+    body="Set up CI checks so you can build and test any modified code in a staging environment when you open PRs and push new commits to your dbt repository."
+    link="/docs/deploy/continuous-integration"
     icon="dbt-bit"/>
 
 <Card
@@ -52,12 +46,6 @@ Learn how to use dbt Cloud's features to help your team ship timely and quality
     link="/docs/deploy/job-commands"
     icon="dbt-bit"/>
 
-<Card
-    title="Job triggers"
-    body="Set up a cron-based schedule or an event-driven trigger by API or on pull requests for CI jobs."
-    link="/docs/deploy/job-triggers"
-    icon="dbt-bit"/>
-
 </div> <br />
 
 ## Monitor jobs and alerts
@@ -77,7 +65,7 @@ Learn how to use dbt Cloud's features to help your team ship timely and quality
     icon="dbt-bit"/>
 
 <Card
-    title="Webhooks for your jobs"
+    title="Webhooks"
     body="Create outbound webhooks to send events about your dbt jobs' statuses to other systems in your organization."
     link="/docs/deploy/webhooks"
     icon="dbt-bit"/>
diff --git a/website/docs/docs/deploy/deployment-tools.md b/website/docs/docs/deploy/deployment-tools.md
index 26e9e4ea317..e642e4b95e2 100644
--- a/website/docs/docs/deploy/deployment-tools.md
+++ b/website/docs/docs/deploy/deployment-tools.md
@@ -4,7 +4,7 @@ id: "deployment-tools"
 sidebar_label: "Integrate with other tools"
 ---
 
-Alongside [dbt Cloud](/docs/deploy/dbt-cloud-job), discover other ways to schedule and run your dbt jobs with the help of tools such as Airflow, Prefect, Dagster, automation server, Cron, and Azure Data Factory (ADF), 
+Alongside [dbt Cloud](/docs/deploy/jobs), discover other ways to schedule and run your dbt jobs with the help of tools such as Airflow, Prefect, Dagster, automation server, Cron, and Azure Data Factory (ADF), 
 
 Build and install these tools to automate your data workflows, trigger dbt jobs (including those hosted on dbt Cloud), and enjoy a hassle-free experience, saving time and increasing efficiency.
 
diff --git a/website/docs/docs/deploy/job-commands.md b/website/docs/docs/deploy/job-commands.md
index acdc3a00228..ca26182fc7b 100644
--- a/website/docs/docs/deploy/job-commands.md
+++ b/website/docs/docs/deploy/job-commands.md
@@ -77,7 +77,6 @@ Job command failures can mean different things for different commands. Some comm
 ## Related docs
 - [Job creation best practices](https://discourse.getdbt.com/t/job-creation-best-practices-in-dbt-cloud-feat-my-moms-lasagna/2980)
 - [dbt Command reference](/reference/dbt-commands)
-- [Job triggers](/docs/deploy/job-triggers)
 - [Job notifications](/docs/deploy/job-notifications)
 - [Source freshness](/docs/deploy/source-freshness)
 - [Build and view your docs](/docs/collaborate/build-and-view-your-docs)
diff --git a/website/docs/docs/deploy/job-scheduler.md b/website/docs/docs/deploy/job-scheduler.md
index 03eeb6fb377..8ade670f1cc 100644
--- a/website/docs/docs/deploy/job-scheduler.md
+++ b/website/docs/docs/deploy/job-scheduler.md
@@ -82,7 +82,7 @@ The scheduler prevents queue clog by canceling runs that aren't needed, ensuring
 
 <Lightbox src="/img/docs/dbt-cloud/deployment/run-error-message.jpg" width="85%" title="The cancelled runs display an error message explaining why the run was cancelled and recommendations"/>
 
-To prevent over-scheduling, users will need to take action by either refactoring the job so it runs faster or modifying its [schedule](/docs/deploy/job-triggers).
+To prevent over-scheduling, users will need to take action by either refactoring the job so it runs faster or modifying its [schedule](/docs/deploy/deploy-jobs#schedule-days).
 
 ## Related docs
 - [dbt Cloud architecture](/docs/cloud/about-cloud/architecture#about-dbt-cloud-architecture)
diff --git a/website/docs/docs/deploy/job-triggers.md b/website/docs/docs/deploy/job-triggers.md
deleted file mode 100644
index cb7a1a48088..00000000000
--- a/website/docs/docs/deploy/job-triggers.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-title: "Job triggers"
-id: "job-triggers"
-description: "You can configure when and how dbt should run your job"
----
-
-In dbt Cloud, you can use the options under **Triggers** to configure when and how dbt should [run your job](/docs/deploy/job-triggers#schedule-job):
-
-- **Schedule** tab &mdash; Use the **Run on schedule** toggle to configure your job to run on either [scheduled days](#schedule-days) or [custom cron-powered schedule](#custom-cron-schedule)
-- **Continuous Integration (CI)** tab &mdash; Configure [continuous integration](/docs/deploy/continuous-integration) to run when someone opens a new pull request in your dbt repository
-- **API** tab &mdash; Use the [API](/docs/dbt-cloud-apis/admin-cloud-api) to trigger a job or send events to other systems
-
-<Lightbox src ="/img/docs/dbt-cloud/using-dbt-cloud/triggers.jpg" width="85%" title="Configuring your job triggers"/>
-
-## Schedule jobs
-
-To schedule your job to run at specific days, times, and intervals:
-1. Go to the specific job settings, click **Edit**, then go to the **Triggers** section
-2. Go to the **Schedule** tab, and toggle **Run on schedule**
-3. Use either the [scheduled days](#schedule-days) or the [custom cron-powered schedule](#custom-cron-schedule) method to customize your desired days, times, and intervals.
-
-### Schedule days
-
-To set your job's schedule, use the **Schedule Days** option to choose specific days of the week, and select customized hours or intervals.
-
-Under **Timing**, you can either use customizable hours for jobs that need to run frequently throughout the day or exact intervals for jobs that need to run at specific times:
-
-- **Every n hours** &mdash; Use this option to set how often your job runs, in hours. Enter a number between 1 and 23 to represent the interval between job runs. For example, if you set it to "every 2 hours", the job will run every 2 hours from midnight UTC. This option is useful if you need to run jobs multiple times per day at regular intervals.
-
-- **At exact intervals** &mdash; Use this option to set specific times when your job should run. You can enter a comma-separated list of hours (in UTC) when you want the job to run. For example, if you set it to `0,12,23,` the job will run at midnight, noon, and 11 PM UTC. This option is useful if you want your jobs to run at specific times of day and don't need them to run more frequently than once a day.
-
-:::info
-
-dbt Cloud uses [Coordinated Universal Time](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) (UTC) and does not account for translations to your specific timezone or take into consideration daylight savings time. For example:
-
-- 0 means 12am (midnight) UTC
-- 12 means 12pm (afternoon) UTC
-- 23 means 11pm UTC
-
-:::
-
-### Custom cron schedule
-
-To fully customize the scheduling of your job, choose the **Custom cron schedule** option and use the "cron" syntax. With this syntax, you can specify the minute, hour, day of the month, month, and day of the week, allowing you to set up complex schedules like running a job on the first Monday of each month.
-
-
-<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/job-schedule.png" title="Schedule your dbt job"/>
-
-Use tools such as [crontab.guru](https://crontab.guru/) to generate the correct cron syntax. This tool allows you to input cron snippets and returns their plain English translations.
-
-Refer to the following example snippets:
-
-
-- `0 * * * *`: Every hour, at minute 0
-- `*/5 * * * *`: Every 5 minutes
-- `5 4 * * *`: At exactly 4:05 AM UTC
-- `30 */4 * * *`: At minute 30 past every 4th hour (e.g. 4:30AM, 8:30AM, 12:30PM, etc., all UTC)
-- `0 0 */2 * *`: At midnight UTC every other day
-- `0 0 * * 1`: At midnight UTC every Monday.
-
-
-## Related docs
-
-- [Artifacts](/docs/deploy/artifacts)
-- [Build and view your docs with dbt Cloud](/docs/collaborate/build-and-view-your-docs)
-- [Source freshness](/docs/deploy/source-freshness)
-- [Job commands](/docs/deploy/job-commands)
-- [Webhooks for your jobs](/docs/deploy/webhooks)
\ No newline at end of file
diff --git a/website/docs/docs/deploy/jobs.md b/website/docs/docs/deploy/jobs.md
new file mode 100644
index 00000000000..b4eec18de12
--- /dev/null
+++ b/website/docs/docs/deploy/jobs.md
@@ -0,0 +1,21 @@
+---
+title: "Jobs in dbt Cloud"
+sidebar_label: "Jobs"
+description: "Learn about deploy jobs and continuous integration (CI) jobs in dbt Cloud and what their differences are." 
+tags: [scheduler]
+---
+
+In dbt Cloud, you can create and set up triggers for these jobs:
+- [Deploy jobs](/docs/deploy/deploy-jobs)
+- [Continuous integration (CI) jobs](/docs/deploy/continuous-integration)  
+
+Below is a comparison table that describes how deploy jobs and CI jobs behave differently:
+
+|  | Deploy Jobs | CI Jobs |
+| --- | --- | --- |
+| Purpose | Building production data assets | Building and testing new code before merging changes into production |
+| Trigger types | Triggered by a schedule or by API | Triggered by a webhook from a commit to a PR or by API |
+| Destination | Builds into a production database and schema | Builds into a staging database and ephemeral schema, lived for the lifetime of the PR |
+| Execution Mode | Runs execute sequentially, so as to not have collisions on the underlying DAG. | Runs execute in parallel to promote team velocity. |
+| Efficiency run savings | Over scheduled jobs are detected and unnecessary runs are canceled to avoid queue clog. | Run cancellation happens when an in-flight run becomes stale due to a new commit of the PR being pushed. |
+| State comparison | Only sometimes needs to detect state | Almost always needs to compare state against the production environment to build on modified code and its dependents. |
diff --git a/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md b/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
index 990e2825443..2f052ae47d2 100644
--- a/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
+++ b/website/docs/guides/dbt-ecosystem/databricks-guides/productionizing-your-dbt-databricks-project.md
@@ -35,11 +35,11 @@ Each dbt Cloud project can have multiple deployment environments, but only one d
 
 With your deployment environment set up, it's time to create a production job to run in your *prod* environment.
 
-To deploy our data transformation workflows, we will utilize [dbt Cloud’s built-in job scheduler](/docs/deploy/dbt-cloud-job). The job scheduler is designed specifically to streamline your dbt project deployments and runs, ensuring that your data pipelines are easy to create, monitor, and modify efficiently.
+To deploy our data transformation workflows, we will utilize [dbt Cloud’s built-in job scheduler](/docs/deploy/deploy-jobs). The job scheduler is designed specifically to streamline your dbt project deployments and runs, ensuring that your data pipelines are easy to create, monitor, and modify efficiently.
 
 Leveraging dbt Cloud's job scheduler allows data teams to own the entire transformation workflow. You don't need to learn and maintain additional tools for orchestration or rely on another team to schedule code written by your team. This end-to-end ownership simplifies the deployment process and accelerates the delivery of new data products.
 
-Let’s [create a job](/docs/deploy/dbt-cloud-job#create-and-schedule-jobs) in dbt Cloud that will transform data in our Databricks *prod* catalog.
+Let’s [create a job](/docs/deploy/deploy-jobs#create-and-schedule-jobs) in dbt Cloud that will transform data in our Databricks *prod* catalog.
 
 1. Create a new job by clicking **Deploy** in the header, click **Jobs** and then **Create job**.
 2. **Name** the job “Daily refresh”.
@@ -58,7 +58,7 @@ Let’s [create a job](/docs/deploy/dbt-cloud-job#create-and-schedule-jobs) in d
         - dbt build is more efficient than issuing separate commands for dbt run and dbt test separately because it will run then test each model before continuing.
         - We are excluding source data because we already tested it in step 2.
         - The fail-fast flag will make dbt exit immediately if a single resource fails to build. If other models are in-progress when the first model fails, then dbt will terminate the connections for these still-running models.
-5. Under **Triggers**, use the toggle to configure your job to [run on a schedule](/docs/deploy/job-triggers). You can enter specific days and timing or create a custom cron schedule. 
+5. Under **Triggers**, use the toggle to configure your job to [run on a schedule](/docs/deploy/deploy-jobs#schedule-days). You can enter specific days and timing or create a custom cron schedule. 
     - If you want your dbt Cloud job scheduled by another orchestrator, like Databricks Workflows, see the [Advanced Considerations](#advanced-considerations) section below.
 
 This is just one example of an all-or-nothing command list designed to minimize wasted computing. The [job command list](/docs/deploy/job-commands) and [selectors](/reference/node-selection/syntax) provide a lot of flexibility on how your DAG will execute. You may want to design yours to continue running certain models if others fail. You may want to set up multiple jobs to refresh models at different frequencies. See our [Job Creation Best Practices discourse](https://discourse.getdbt.com/t/job-creation-best-practices-in-dbt-cloud-feat-my-moms-lasagna/2980) for more job design suggestions.
@@ -93,7 +93,7 @@ With dbt tests and SlimCI, you can feel confident that your production data will
 
 Keeping a close eye on your dbt Cloud jobs is crucial for maintaining a robust and efficient data pipeline. By monitoring job performance and quickly identifying potential issues, you can ensure that your data transformations run smoothly. dbt Cloud provides three entry points to monitor the health of your project: run history, deployment monitor, and status tiles.
 
-The [run history](/docs/deploy/dbt-cloud-job) dashboard in dbt Cloud provides a detailed view of all your project's job runs, offering various filters to help you focus on specific aspects. This is an excellent tool for developers who want to check recent runs, verify overnight results, or track the progress of running jobs. To access it, select **Run History** from the **Deploy** menu.
+The [run history](/docs/deploy/run-visibility#run-history) dashboard in dbt Cloud provides a detailed view of all your project's job runs, offering various filters to help you focus on specific aspects. This is an excellent tool for developers who want to check recent runs, verify overnight results, or track the progress of running jobs. To access it, select **Run History** from the **Deploy** menu.
 
 The deployment monitor in dbt Cloud offers a higher-level view of your run history, enabling you to gauge the health of your data pipeline over an extended period of time. This feature includes information on run durations and success rates, allowing you to identify trends in job performance, such as increasing run times or more frequent failures. The deployment monitor also highlights jobs in progress, queued, and recent failures. To access the deployment monitor click on the dbt logo in the top left corner of the dbt Cloud UI.
 
diff --git a/website/docs/guides/orchestration/how-to-use-databricks-workflows-to-run-dbt-cloud-jobs.md b/website/docs/guides/orchestration/how-to-use-databricks-workflows-to-run-dbt-cloud-jobs.md
index 692106655ac..bb1045b3d2f 100644
--- a/website/docs/guides/orchestration/how-to-use-databricks-workflows-to-run-dbt-cloud-jobs.md
+++ b/website/docs/guides/orchestration/how-to-use-databricks-workflows-to-run-dbt-cloud-jobs.md
@@ -9,14 +9,14 @@ hoverSnippet: Learn how to use Databricks workflows to run dbt Cloud jobs
 Using Databricks workflows to call the dbt Cloud job API can be useful for several reasons:
 
 1. **Integration with other ETL processes** &mdash; If you're already running other ETL processes in Databricks, you can use a Databricks workflow to trigger a dbt Cloud job after those processes are done.
-2. **Utilizes dbt Cloud jobs features &mdash;** dbt Cloud gives the ability to monitor job progress, manage historical logs and documentation, optimize model timing, and much [more](/docs/deploy/dbt-cloud-job).
+2. **Utilizes dbt Cloud jobs features &mdash;** dbt Cloud gives the ability to monitor job progress, manage historical logs and documentation, optimize model timing, and much [more](/docs/deploy/deploy-jobs).
 3. [**Separation of concerns &mdash;**](https://en.wikipedia.org/wiki/Separation_of_concerns) Detailed logs for dbt jobs in the dbt Cloud environment can lead to more modularity and efficient debugging. By doing so, it becomes easier to isolate bugs quickly while still being able to see the overall status in Databricks.
 4. **Custom job triggering &mdash;** Use a Databricks workflow to trigger dbt Cloud jobs based on custom conditions or logic that aren't natively supported by dbt Cloud's scheduling feature. This can give you more flexibility in terms of when and how your dbt Cloud jobs run.
 
 ## Prerequisites
 
 - Active [Teams or Enterprise dbt Cloud account](https://www.getdbt.com/pricing/)
-- You must have a configured and existing [dbt Cloud job](/docs/deploy/dbt-cloud-job)
+- You must have a configured and existing [dbt Cloud deploy job](/docs/deploy/deploy-jobs)
 - Active Databricks account with access to [Data Science and Engineering workspace](https://docs.databricks.com/workspace-index.html) and [Manage secrets](https://docs.databricks.com/security/secrets/index.html)
 - [Databricks CLI](https://docs.databricks.com/dev-tools/cli/index.html)
   - **Note**: You only need to set up your authentication. Once you have set up your Host and Token and are able to run `databricks workspace ls /Users/<someone@example.com>`, you can proceed with the rest of this guide.
diff --git a/website/docs/quickstarts/bigquery-qs.md b/website/docs/quickstarts/bigquery-qs.md
index 84e3b3ae545..7f7f9aa7655 100644
--- a/website/docs/quickstarts/bigquery-qs.md
+++ b/website/docs/quickstarts/bigquery-qs.md
@@ -33,8 +33,8 @@ You can check out [dbt Fundamentals](https://courses.getdbt.com/courses/fundamen
 ### Related content
 
 - Learn more with [dbt Courses](https://courses.getdbt.com/collections)
-- [dbt Cloud CI job](/docs/deploy/continuous-integration)
-- [Job triggers](/docs/deploy/job-triggers)
+- [CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
 - [Job notifications](/docs/deploy/job-notifications)
 - [Source freshness](/docs/deploy/source-freshness)
 
diff --git a/website/docs/quickstarts/databricks-qs.md b/website/docs/quickstarts/databricks-qs.md
index 1222ef2a7d5..08334862517 100644
--- a/website/docs/quickstarts/databricks-qs.md
+++ b/website/docs/quickstarts/databricks-qs.md
@@ -30,8 +30,8 @@ You can check out [dbt Fundamentals](https://courses.getdbt.com/courses/fundamen
 ### Related content
 
 - Learn more with [dbt Courses](https://courses.getdbt.com/collections)
-- [dbt Cloud CI job](/docs/deploy/continuous-integration)
-- [Job triggers](/docs/deploy/job-triggers)
+- [CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
 - [Job notifications](/docs/deploy/job-notifications)
 - [Source freshness](/docs/deploy/source-freshness)
 
diff --git a/website/docs/quickstarts/redshift-qs.md b/website/docs/quickstarts/redshift-qs.md
index fc7e178f163..67f66d6e275 100644
--- a/website/docs/quickstarts/redshift-qs.md
+++ b/website/docs/quickstarts/redshift-qs.md
@@ -31,8 +31,8 @@ You can check out [dbt Fundamentals](https://courses.getdbt.com/courses/fundamen
 ### Related content
 
 - Learn more with [dbt Courses](https://courses.getdbt.com/collections)
-- [dbt Cloud CI job](/docs/deploy/continuous-integration)
-- [Job triggers](/docs/deploy/job-triggers)
+- [CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
 - [Job notifications](/docs/deploy/job-notifications)
 - [Source freshness](/docs/deploy/source-freshness)
 
diff --git a/website/docs/quickstarts/snowflake-qs.md b/website/docs/quickstarts/snowflake-qs.md
index 0561ea13410..33e253e8c15 100644
--- a/website/docs/quickstarts/snowflake-qs.md
+++ b/website/docs/quickstarts/snowflake-qs.md
@@ -35,8 +35,8 @@ You can also watch the [YouTube video on dbt and Snowflake](https://www.youtube.
 
 - Learn more with [dbt Courses](https://courses.getdbt.com/collections)
 - [How we configure Snowflake](https://blog.getdbt.com/how-we-configure-snowflake/)
-- [dbt Cloud CI job](/docs/deploy/continuous-integration)
-- [Job triggers](/docs/deploy/job-triggers)
+- [CI jobs](/docs/deploy/continuous-integration)
+- [Deploy jobs](/docs/deploy/deploy-jobs)
 - [Job notifications](/docs/deploy/job-notifications)
 - [Source freshness](/docs/deploy/source-freshness)
 
diff --git a/website/sidebars.js b/website/sidebars.js
index 1ffaea7af7f..c51903d31d8 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -331,16 +331,8 @@ const sidebarSettings = {
       items: [
         "docs/deploy/job-scheduler",
         "docs/deploy/deploy-environments",
-        {
-          type: "category",
-          label: "dbt Cloud jobs",
-          link: { type: "doc", id: "docs/deploy/dbt-cloud-job" },
-          items: [
-            "docs/deploy/job-settings",
-            "docs/deploy/job-commands",
-            "docs/deploy/job-triggers",
-          ],
-        },
+        "docs/deploy/jobs",
+        "docs/deploy/deploy-jobs",
         {
           type: "category",
           label: "Continuous integration",
@@ -349,6 +341,7 @@ const sidebarSettings = {
             "docs/deploy/ci-jobs",
           ],
         },
+        "docs/deploy/job-commands",
         {
           type: "category",
           label: "Monitor jobs and alerts",
diff --git a/website/static/_redirects b/website/static/_redirects
index ba9d00d37f6..aba7f20132b 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,3 +1,5 @@
+/docs/deploy/job-triggers /docs/deploy/deploy-jobs 301
+
 /docs/deploy/slim-ci-jobs /docs/deploy/ci-jobs 301
 
 ## semantic layer

From e023ce802f0a43cdf0aced15c52f46f3e906676e Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Wed, 9 Aug 2023 16:44:45 -0700
Subject: [PATCH 619/703] Update website/static/_redirects

---
 website/static/_redirects | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index aba7f20132b..fe6cda820d4 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -1,5 +1,6 @@
 /docs/deploy/job-triggers /docs/deploy/deploy-jobs 301
-
+/docs/deploy/job-settings /docs/deploy/deploy-jobs 301
+/docs/deploy/dbt-cloud-job /docs/deploy/deploy-jobs 301
 /docs/deploy/slim-ci-jobs /docs/deploy/ci-jobs 301
 
 ## semantic layer

From 1e74b197c8edc758957b40024a9cb963adbc127a Mon Sep 17 00:00:00 2001
From: rpourzand <rpourzan@gmail.com>
Date: Thu, 10 Aug 2023 00:39:45 -0700
Subject: [PATCH 620/703] Found another typo in the table and we had a
 duplicate query

Typo on the where filter, and we also had two of the same query under the "WHERE" examples
---
 website/docs/docs/dbt-cloud-apis/sl-jdbc.md | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
index 1497dd61a63..f981537f530 100644
--- a/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
+++ b/website/docs/docs/dbt-cloud-apis/sl-jdbc.md
@@ -138,7 +138,7 @@ To query metric values, here are the following parameters that are available:
 | `metrics`   | The metric name as defined in your dbt metric configuration   | `metrics=['revenue']` | Required    |
 | `group_by`  | Dimension names or entities to group by. We require a reference to the entity of the dimension (other than for the primary time dimension), which is pre-appended to the front of the dimension name with a double underscore. | `group_by=['user__country', 'metric_time']`     | Optional   |
 | `grain`   | A parameter specific to any time dimension and changes the grain of the data from the default for the metric. | `group_by=[Dimension('metric_time')` <br/> `grain('week\|day\|month\|quarter\|year')]` | Optional     |
-| `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where=Dimension('customer__country') = 'US')"`   | Optional   |
+| `where`     | A where clause that allows you to filter on dimensions and entities using parameters  - comes with `TimeDimension`, `Dimension`, and `Entity` objects. Granularity is required with `TimeDimension`  | `"{{ where=Dimension('customer__country') }} = 'US')"`   | Optional   |
 | `limit`   | Limit the data returned    | `limit=10` | Optional  |
 |`order`  | Order the data returned     | `order_by=['-order_gross_profit']` (remove `-` for ascending order)  | Optional   |
 | `explain`   | If true, returns generated SQL for the data platform but does not execute | `explain=True`   | Optional |
@@ -233,16 +233,6 @@ where="{{ TimeDimension('metric_time', 'MONTH') }} >= '2017-03-09' AND {{ Dimens
 }}
 ```
 
-Use the following example to query using a `where` filter:
-
-```bash
-select * from {{
-semantic_layer.query(metrics=['food_order_amount', 'order_gross_profit'],
-group_by=[Dimension('metric_time').grain('month'),'customer__customer_type'],
-where="{{ TimeDimension('metric_time', 'MONTH') }} >= '2017-03-09' AND {{ Dimension('customer__customer_type' }} in ('new')")
-}}
-``` 
-
 ### Query with a limit and order_by
 
 Use the following example to query using a `limit` or `order_by` clauses:

From e283fd09cb2ccabd2586dbba618d9d4b41c2f596 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 11:00:40 +0100
Subject: [PATCH 621/703] Update
 website/docs/docs/dbt-cloud-apis/discovery-querying.md

---
 website/docs/docs/dbt-cloud-apis/discovery-querying.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/discovery-querying.md b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
index 9791ee5af3d..40836203faa 100644
--- a/website/docs/docs/dbt-cloud-apis/discovery-querying.md
+++ b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
@@ -64,7 +64,7 @@ There are several illustrative example queries on this page. For more examples,
 
 Discovery (GraphQL) API usage is subject to request rate and response size limits to maintain the performance and stability of the metadata platform and prevent abuse.
 
-- Job-level endpoints are subject to query complexity limits. Nested nodes (like parents), code (like rawCode), and catalog columns are considered as most complex. Overly complex queries should be broken up into separate queries with only necessary fields included. dbt Labs recommends using the environment endpoint instead for most use cases to get the latest descriptive and result metadata for a dbt Cloud project.
+Job-level endpoints are subject to query complexity limits. Nested nodes (like parents), code (like rawCode), and catalog columns are considered as most complex. Overly complex queries should be broken up into separate queries with only necessary fields included. dbt Labs recommends using the environment endpoint instead for most use cases to get the latest descriptive and result metadata for a dbt Cloud project.
 
 ## Retention limits
 You can use the Discovery API to query data from the previous three months. For example, if today was April 1st, you could query data back to January 1st.

From 1ff6c3e66ba02c91b58d1d4d4249169667465af1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 10 Aug 2023 13:11:47 +0100
Subject: [PATCH 622/703] add new eat section

---
 website/snippets/_cloud-environments-info.md  |  45 ++++++++++++++++++
 .../using-dbt-cloud/extended-attributes.jpg   | Bin 0 -> 16315 bytes
 2 files changed, 45 insertions(+)
 create mode 100644 website/static/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index d8ea7e3d799..a243a508011 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -42,3 +42,48 @@ By default, all environments will use the default branch in your repository (usu
 - **Deployment:** determines the branch is cloned during job executions for each environment.
 
 For more info, check out this [FAQ page on this topic](/faqs/Environments/custom-branch-settings)!
+
+
+### Extended Attributes (Beta)
+
+:::important This feature is currently in beta
+
+Extended Attributes allows users to enter YAML attributes in their Environment settings. It's currently in beta and if you're interested in getting access to the beta, please contact us -- HOW CAN USES TRY THIS BETA? IF IT'S FEATURE FLAG, HOW DO THEY JOIN THE BETA? DO WE NEED TO ADD A FORM LINK? OR EMAIL?
+:::
+
+Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings.
+
+It enhances user control over environments (deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.  
+
+Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials. It accepts any attributes accepted by a dbt adapter in its profiles.yml. However, it doesn't mask secret values, and we recommend avoiding setting secret values to prevent visibility in the text box and logs.
+
+<Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg" width="95%" title="Extended Attributes helps users add profiles.yml attributes to dbt Cloud Environment settings using a free form text box." /> <br />
+
+The **Extended Attributes** text box must contain valid characters only. Any invalid characters will result in an error message. Invalid characters include:
+
+- `/` (forward slash)
+- `\` (backslash)
+- `$` (dollar sign)
+- `!` (exclamation point)
+- `@` (at sign)
+- `#` (hash) (NEED CONFIRMATION)
+- `+` (plus sign)
+- `%` (percentage sign)
+
+If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the keys. For each individual key:
+
+- If the key exists in another source (such as your project settings), it will replace its value in the profile.
+- If the key doesn't exist, it will add the key or value pair to the profile. 
+
+The following code is an example of the types of attributes you can add to the **Extended Attributes** text box:
+
+```yaml
+type: postgres      
+host: localhost      
+user: alice      
+password: <your_password>      
+port: 5432      
+dbname: jaffle_shop      
+schema: dbt_alice      
+threads: 4
+```
diff --git a/website/static/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg b/website/static/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg
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From 4b70dcbf5cc29205a60e7d4aeca90f30a421539e Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 10 Aug 2023 13:13:57 +0100
Subject: [PATCH 623/703] simplify

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index a243a508011..46c1288f1e8 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -53,7 +53,7 @@ Extended Attributes allows users to enter YAML attributes in their Environment s
 
 Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings.
 
-It enhances user control over environments (deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.  
+It provides users with more control over environments (deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.
 
 Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials. It accepts any attributes accepted by a dbt adapter in its profiles.yml. However, it doesn't mask secret values, and we recommend avoiding setting secret values to prevent visibility in the text box and logs.
 

From adaba7d5e99de1904792c0886a425b4fdf6ca8f1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 10 Aug 2023 13:17:08 +0100
Subject: [PATCH 624/703] simplify lang

---
 website/snippets/_cloud-environments-info.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 46c1288f1e8..36c8839f551 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -51,11 +51,11 @@ For more info, check out this [FAQ page on this topic](/faqs/Environments/custom
 Extended Attributes allows users to enter YAML attributes in their Environment settings. It's currently in beta and if you're interested in getting access to the beta, please contact us -- HOW CAN USES TRY THIS BETA? IF IT'S FEATURE FLAG, HOW DO THEY JOIN THE BETA? DO WE NEED TO ADD A FORM LINK? OR EMAIL?
 :::
 
-Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings.
+Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings. It provides users with more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.
 
-It provides users with more control over environments (deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.
+Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials. It accepts any attributes accepted by a dbt adapter in its `profiles.yml`. 
 
-Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials. It accepts any attributes accepted by a dbt adapter in its profiles.yml. However, it doesn't mask secret values, and we recommend avoiding setting secret values to prevent visibility in the text box and logs.
+However, something to note is that it doesn't mask secret values. We recommend avoiding setting secret values to prevent visibility in the text box and logs.
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg" width="95%" title="Extended Attributes helps users add profiles.yml attributes to dbt Cloud Environment settings using a free form text box." /> <br />
 

From 76b9b03870320b998ee283e0cec2d3639e86ebf7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 13:21:34 +0100
Subject: [PATCH 625/703] Update website/snippets/_cloud-environments-info.md

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 36c8839f551..20427475bea 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -48,7 +48,7 @@ For more info, check out this [FAQ page on this topic](/faqs/Environments/custom
 
 :::important This feature is currently in beta
 
-Extended Attributes allows users to enter YAML attributes in their Environment settings. It's currently in beta and if you're interested in getting access to the beta, please contact us -- HOW CAN USES TRY THIS BETA? IF IT'S FEATURE FLAG, HOW DO THEY JOIN THE BETA? DO WE NEED TO ADD A FORM LINK? OR EMAIL?
+Extended Attributes allow users to enter YAML attributes in their Environment settings. It's currently in beta and if you're interested in joining the beta, please contact us -- HOW CAN USES TRY THIS BETA? IF IT'S FEATURE FLAG, HOW DO THEY JOIN THE BETA? DO WE NEED TO ADD A FORM LINK? OR EMAIL?
 :::
 
 Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings. It provides users with more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.

From f4a742d33b8aea70970f6ba29681fdc6603fdcd8 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 10 Aug 2023 13:25:07 +0100
Subject: [PATCH 626/703] change to sentence

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 20427475bea..b41dc056b91 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -44,7 +44,7 @@ By default, all environments will use the default branch in your repository (usu
 For more info, check out this [FAQ page on this topic](/faqs/Environments/custom-branch-settings)!
 
 
-### Extended Attributes (Beta)
+### Extended attributes (Beta)
 
 :::important This feature is currently in beta
 

From 9bc111f6372ee1aa9ac5b88b36bb7658077a3858 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 10 Aug 2023 09:14:06 -0400
Subject: [PATCH 627/703] Update redshift-configs.md

---
 .../docs/reference/resource-configs/redshift-configs.md    | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index b0c37dc2bac..2b54635ac05 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -109,9 +109,11 @@ models:
 The Redshift adapter supports [materialized views](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-overview.html) and refreshes them for every subsequent `dbt run` that you execute. For more information, see [Refresh Materialized Views](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html) in the Redshift docs.
 
 Materialized views support the optional configuration `on_configuration_change` with the following values: 
-- `apply` (default) &mdash; attempts to update the existing database object if possible, avoiding a complete rebuild. The `auto_refresh` action can applied without the need to rebuild the materialized view.
+- `apply` (default) &mdash; attempts to update the existing database object if possible, avoiding a complete rebuild. 
 - `skip` &mdash; allows runs to continue while also providing a warning that the model was skipped
-- `fail` &mdash; forces runs to fail if a change is detected in a materialized view 
+- `fail` &mdash; forces runs to fail if a change is detected in a materialized view
+
+Additionally, you can apply the `auto_refresh` configuration to have Redshift [automatically refresh](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html) the materialized view for you. This action can applied without the need to rebuild the materialized view. 
 
 You can create a materialized view by editing _one_ of these files:
 - the SQL file for your model
@@ -125,6 +127,7 @@ The following examples create a materialized view:
 {{
   config(
     materialized = 'materialized_view',
+    auto_refresh = false,
     on_configuration_change = 'apply',
   )
 }}

From e5ee954e1bb175434709ad338e589c70eedb10cb Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 15:15:53 +0100
Subject: [PATCH 628/703] add info on the 'change row display' settings in the
 ide

adding this new feature per @StefanDBTLabs 's update
---
 .../cloud/dbt-cloud-ide/ide-user-interface.md | 33 ++++++++++---------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index 63a4f9a0312..852f7202465 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -28,7 +28,7 @@ The IDE streamlines your workflow, and features a popular user interface layout
 4. **File Explorer &mdash;** The File Explorer shows the filetree of your repository. You can:
     - Click on any file in the filetree to open the file in the File Editor. 
     - Click and drag files between directories to move files. 
-    - Right click a file to access the sub-menu options like duplicate file, copy file name, copy as `ref`, rename, delete.
+    - Right-click a file to access the sub-menu options like duplicate file, copy file name, copy as `ref`, rename, delete.
     - **Note**: To perform these actions, the user must not be in `read-only` mode, which generally happens when the user is viewing the default branch.
     - Use file indicators, located to the right of your files or folder name, to see when changes or actions were made:
       * Unsaved (•) — The IDE detects unsaved changes to your file/folder
@@ -59,7 +59,7 @@ The IDE features some delightful tools and layouts to make it easier for you to
 
    - **Version Control Options menu &mdash;** Below the Git Actions button, the **Changes** section, which lists all file changes since the last commit. You can click on a change to open the Git Diff View to see the inline changes. You can also right-click any file and use the file-specific options in the Version Control Options menu.
 
-<Lightbox src="/img/docs/dbt-cloud/cloud-ide/version-control-options-menu.png" width="30%" title="Right-click edited files to access Version Control Options menu"/>
+<Lightbox src="/img/docs/dbt-cloud/cloud-ide/version-control-options-menu.png" width="30%" title=" Right-click edited files to access Version Control Options menu"/>
 
 ## Additional editing features
 
@@ -72,23 +72,25 @@ The IDE features some delightful tools and layouts to make it easier for you to
 - **Git Diff View &mdash;** Clicking on a file in the **Changes** section of the **Version Control Menu** will open the changed file with Git Diff view. The editor will show the previous version on the left and the in-line changes made on the right.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-git-diff-view-with-save.jpg" width="100%" title="The Git Diff View displays the previous version on the left and the changes made on the right of the Editor"/>
 
-- **Markdown Preview console tab &mdash;** The Markdown Preview console tab shows a preview of your .md file's markdown code in your repository, and updates it automatically as you edit your code.
+- **Markdown Preview console tab &mdash;** The Markdown Preview console tab shows a preview of your .md file's markdown code in your repository and updates it automatically as you edit your code.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-markdown-with-save.jpg" width="100%" title="The Markdown Preview console tab renders markdown code below the Editor tab."/>
 
 - **CSV Preview console tab &mdash;** The CSV Preview console tab displays the data from your CSV file in a table, which updates automatically as you edit the file in your seed directory.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-csv.jpg" width="100%" title="View csv code in the CSV Preview console tab below the Editor tab."/>
 
 ## Console section
+
 The console section, located below the File editor, includes various console tabs and buttons to help you with tasks such as previewing, compiling, building, and viewing the <Term id="dag" />. Refer to the following sub-bullets for more details on the console tabs and buttons.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-console-overview.jpg" width="100%" title="The Console section is located below the File editor and has various tabs and buttons to help execute tasks"/>
 
-1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not, and sends the results to the Results console tab. 
-   * To prevent the IDE from returning too much data and causing browser problems, a limit of 500 is automatically added to queries executed via the Preview Button. However, you can change this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro. 
+1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not and sends the results to the Results console tab. 
+   * To prevent the IDE from returning too much data and causing browser problems, there is a 500-row limit automatically set to queries executed using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
+   * In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
    * The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
 
 2. **Compile button &mdash;** The Compile button compiles the SQL code from the active File Editor, irrespective of its save status, and outputs it to the Compiled Code tab.
 
-3. **Build button &mdash;** The build button allows users to quickly access dbt commands related to the active model in the File Editor. The available commands include dbt build, dbt test, and dbt run, with options to include only the current resource, the resource and its upstream dependencies, the resource and its downstream dependencies, or the resource with all dependencies. This menu is available for all executable nodes.
+3. **Build button &mdash;** The build button allows users to quickly access dbt commands related to the active model in the File Editor. The available commands include dbt build, dbt test, and dbt run, with options to include only the current resource, the resource and its upstream dependencies, the resource, and its downstream dependencies, or the resource with all dependencies. This menu is available for all executable nodes.
 
 3. **Format button &mdash;** The editor has a **Format** button that can reformat the contents of your files. For SQL files, it uses either `sqlfmt` or `sqlfluff`, and for Python files, it uses `black`.
 
@@ -106,9 +108,10 @@ The console section, located below the File editor, includes various console tab
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/lineage-console-tab.jpg" width="100%" title="View resource lineage in the Lineage tab"/>
 
 ## Invocation history
-The Invocation History Drawer stores information on dbt invocations in the IDE. When you invoke a command (like execute a dbt command such as `dbt run`), the associated logs are displayed in the Invocation History Drawer. 
 
-You can open the drawer multiple ways: 
+The Invocation History Drawer stores information on dbt invocations in the IDE. When you invoke a command (like executing a dbt command such as `dbt run`), the associated logs are displayed in the Invocation History Drawer. 
+
+You can open the drawer in multiple ways: 
 - Clicking the `^` icon next to the Command bar on the lower left of the page
 - Typing a dbt command and pressing enter
 - Or pressing Control-backtick (or Ctrl + `)
@@ -117,15 +120,15 @@ You can open the drawer multiple ways:
 
 1. **Invocation History list &mdash;** The left-hand panel of the Invocation History Drawer displays a list of previous invocations in the IDE, including the command, branch name, command status, and elapsed time.
 
-2. **Invocation Summary &mdash;** The Invocation Summary, located above **System Logs**, displays information about a selected command from the Invocation History list , such as the command, its status (`Running` if it's still running), the git branch that was active during the command, and the time the command was invoked.
+2. **Invocation Summary &mdash;** The Invocation Summary, located above **System Logs**, displays information about a selected command from the Invocation History list, such as the command, its status (`Running` if it's still running), the git branch that was active during the command, and the time the command was invoked.
 
-3. **System Logs toggle &mdash;** The System Logs toggle, located under the Invocation Summary, allows the user to see the full stdout and debug logs for entirety of the invoked command. 
+3. **System Logs toggle &mdash;** The System Logs toggle, located under the Invocation Summary, allows the user to see the full stdout and debug logs for the entirety of the invoked command. 
 
-4. **Command Control button &mdash;** Use the Command Control button, located on the right-side, to control your invocation and cancel or rerun a selected run.
+4. **Command Control button &mdash;** Use the Command Control button, located on the right side, to control your invocation and cancel or rerun a selected run.
 
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-results.jpg" width="100%" title="The Invocation History list displays a list of previous invocations in the IDE"/>
 
-5. **Node Summary tab &mdash;** Clicking on the Results Status Tabs will filter the Node Status List based on their corresponding status. The available statuses are Pass (successful invocation of a node), Warn (test executed with warning), Error (database error or test failure), Skip (nodes not run due to upstream error), and Queued (nodes that have not executed yet).
+5. **Node Summary tab &mdash;** Clicking on the Results Status Tabs will filter the Node Status List based on their corresponding status. The available statuses are Pass (successful invocation of a node), Warn (test executed with a warning), Error (database error or test failure), Skip (nodes not run due to upstream error), and Queued (nodes that have not executed yet).
 
 6. **Node result toggle &mdash;** After running a dbt command, information about each executed node can be found in a Node Result toggle, which includes a summary and debug logs. The Node Results List lists every node that was invoked during the command.
 
@@ -135,12 +138,12 @@ You can open the drawer multiple ways:
 ## Modals and Menus
 Use menus and modals to interact with IDE and access useful options to help your development workflow. 
 
-- **Editor tab menu &mdash;** To interact with open editor tabs, right-click any tab to access the helpful options in the file tab menu.<Lightbox src="/img/docs/dbt-cloud/cloud-ide/editor-tab-menu-with-save.jpg" width="100%" title="Right click a tab to view the Editor tab menu options"/>
+- **Editor tab menu &mdash;** To interact with open editor tabs, right-click any tab to access the helpful options in the file tab menu.<Lightbox src="/img/docs/dbt-cloud/cloud-ide/editor-tab-menu-with-save.jpg" width="100%" title=" Right-click a tab to view the Editor tab menu options"/>
 
 - **File Search &mdash;** You can easily search for and navigate between files using the File Navigation menu, which can be accessed by pressing Command-O or Control-O or clicking on the 🔍 icon in the File Explorer.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-file-search-with-save.jpg" width="100%" title="The Command History returns a log and detail of all your dbt Cloud invocations."/>
 
-- **Global Command Palette&mdash;** The Global Command Palette provides helpful shortcuts to interact with the IDE, such as git actions, specialized dbt commands, compile, and preview actions, among others. To open the menu, use Command-P or Control-P.
+- **Global Command Palette&mdash;** The Global Command Palette provides helpful shortcuts to interact with the IDE, such as git actions, specialized dbt commands, and compile, and preview actions, among others. To open the menu, use Command-P or Control-P.
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-global-command-palette-with-save.jpg" width="100%" title="The Command History returns a log and detail of all your dbt Cloud invocations."/>
 
 - **IDE Status modal &mdash;**  The IDE Status modal shows the current error message and debug logs for the server. This also contains an option to restart the IDE. Open this by clicking on the IDE Status button.
@@ -159,7 +162,7 @@ Use menus and modals to interact with IDE and access useful options to help your
 
   * Toggling between dark or light mode for a better viewing experience
   * Restarting the IDE
-  * Fully recloning your repository to refresh your git state and viewing status details
+  * Fully recloning your repository to refresh your git state and view status details
   * Viewing status details, including the IDE Status modal.
 
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-options-menu-with-save.jpg" width="85%" title="Access the IDE Options menu to switch to dark or light mode, restart the IDE, reclone your repo, or view the IDE status"/>

From 60b7966feca776e2bc91d9bc1171e7238811c9ba Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 10 Aug 2023 10:38:38 -0400
Subject: [PATCH 629/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 2b54635ac05..06f20d62e39 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -110,7 +110,7 @@ The Redshift adapter supports [materialized views](https://docs.aws.amazon.com/r
 
 Materialized views support the optional configuration `on_configuration_change` with the following values: 
 - `apply` (default) &mdash; attempts to update the existing database object if possible, avoiding a complete rebuild. 
-- `skip` &mdash; allows runs to continue while also providing a warning that the model was skipped
+- `continue` &mdash; allows runs to continue while also providing a warning that the model was skipped
 - `fail` &mdash; forces runs to fail if a change is detected in a materialized view
 
 Additionally, you can apply the `auto_refresh` configuration to have Redshift [automatically refresh](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html) the materialized view for you. This action can applied without the need to rebuild the materialized view. 

From a7d6b8a89a777a973634df81f540ab57065b23da Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 10 Aug 2023 10:40:14 -0400
Subject: [PATCH 630/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index 06f20d62e39..ae67d67ad6a 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -110,7 +110,7 @@ The Redshift adapter supports [materialized views](https://docs.aws.amazon.com/r
 
 Materialized views support the optional configuration `on_configuration_change` with the following values: 
 - `apply` (default) &mdash; attempts to update the existing database object if possible, avoiding a complete rebuild. 
-- `continue` &mdash; allows runs to continue while also providing a warning that the model was skipped
+- `continue` &mdash; allows runs to continue while also providing a warning that the model was not executed
 - `fail` &mdash; forces runs to fail if a change is detected in a materialized view
 
 Additionally, you can apply the `auto_refresh` configuration to have Redshift [automatically refresh](https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html) the materialized view for you. This action can applied without the need to rebuild the materialized view. 

From 139c98ff37fc4402bc61e99ef71f5d1c7279e721 Mon Sep 17 00:00:00 2001
From: Amy Chen <46451573+amychen1776@users.noreply.github.com>
Date: Thu, 10 Aug 2023 11:08:12 -0400
Subject: [PATCH 631/703] Update
 website/docs/reference/resource-configs/redshift-configs.md

---
 website/docs/reference/resource-configs/redshift-configs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/resource-configs/redshift-configs.md b/website/docs/reference/resource-configs/redshift-configs.md
index ae67d67ad6a..1cf4f193cd2 100644
--- a/website/docs/reference/resource-configs/redshift-configs.md
+++ b/website/docs/reference/resource-configs/redshift-configs.md
@@ -127,7 +127,7 @@ The following examples create a materialized view:
 {{
   config(
     materialized = 'materialized_view',
-    auto_refresh = false,
+    auto_refresh = False,
     on_configuration_change = 'apply',
   )
 }}

From a95906a1a07abc58db9db09f7533c68866bc5519 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:08:55 +0100
Subject: [PATCH 632/703] Update website/snippets/_version-callout.md

---
 website/snippets/_version-callout.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 40afbcfa8bf..71142cea738 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,7 +1,9 @@
 
 :::info Model versioning is different to a version tag
 
-Take note that a [version tag](/reference/project-configs/version) (or `version`) is different to [`versions`](/reference/resource-properties/versions) or [Model versions](/docs/collaborate/govern/model-versions). A version tag refers to the compatibility of the dbt project with a specific version of dbt. A version tag isn't required for dbt v1.5 and higher. 
+Take note that [model versions](/docs/collaborate/govern/model-versions) are different from [dbt_project.yml versions](/reference/project-configs/version#dbt_projectyml-versions) and [.yml property file versions](/reference/project-configs/version#yml-property-file-versions).
+
+Model versions is a _feature_ that enables better governance and data model management by allowing you to track changes and updates to models over time. dbt_project.yml versions refer to the compatibility of the dbt project with a specific version of dbt. Version numbers within .yml property files inform how dbt parses those YAML files. The latter two are completely optional starting from version 1.5.
 
 Model versions and `versions` are _features_ that enable better governance and data model management by allowing you to track changes and updates to models over time.  Model versions and `versions` are available in dbt v1.5 or higher.
 :::

From 89b141d0eb8feba50547aca89af650e72b2af09f Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:09:15 +0100
Subject: [PATCH 633/703] Update website/snippets/_version-callout.md

---
 website/snippets/_version-callout.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 71142cea738..6194a023a56 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -5,5 +5,4 @@ Take note that [model versions](/docs/collaborate/govern/model-versions) are dif
 
 Model versions is a _feature_ that enables better governance and data model management by allowing you to track changes and updates to models over time. dbt_project.yml versions refer to the compatibility of the dbt project with a specific version of dbt. Version numbers within .yml property files inform how dbt parses those YAML files. The latter two are completely optional starting from version 1.5.
 
-Model versions and `versions` are _features_ that enable better governance and data model management by allowing you to track changes and updates to models over time.  Model versions and `versions` are available in dbt v1.5 or higher.
 :::

From 8a938e0f7780462bf8964840b8de68c14c8f1a66 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:10:07 +0100
Subject: [PATCH 634/703] Update website/snippets/_version-callout.md

---
 website/snippets/_version-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index 6194a023a56..ae6f0bc4383 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -3,6 +3,6 @@
 
 Take note that [model versions](/docs/collaborate/govern/model-versions) are different from [dbt_project.yml versions](/reference/project-configs/version#dbt_projectyml-versions) and [.yml property file versions](/reference/project-configs/version#yml-property-file-versions).
 
-Model versions is a _feature_ that enables better governance and data model management by allowing you to track changes and updates to models over time. dbt_project.yml versions refer to the compatibility of the dbt project with a specific version of dbt. Version numbers within .yml property files inform how dbt parses those YAML files. The latter two are completely optional starting from version 1.5.
+Model versions is a _feature_ that enables better governance and data model management by allowing you to track changes and updates to models over time. dbt_project.yml versions refer to the compatibility of the dbt project with a specific version of dbt. Version numbers within .yml property files inform how dbt parses those YAML files. The latter two are completely optional starting from dbt v1.5.
 
 :::

From adf36b9d7e64da35b16e8805ba8000029a7805f5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:12:03 +0100
Subject: [PATCH 635/703] Update website/snippets/_version-callout.md

---
 website/snippets/_version-callout.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_version-callout.md b/website/snippets/_version-callout.md
index ae6f0bc4383..45c183c2c0d 100644
--- a/website/snippets/_version-callout.md
+++ b/website/snippets/_version-callout.md
@@ -1,5 +1,5 @@
 
-:::info Model versioning is different to a version tag
+:::info Model versions, dbt_project.yml versions, and .yml versions
 
 Take note that [model versions](/docs/collaborate/govern/model-versions) are different from [dbt_project.yml versions](/reference/project-configs/version#dbt_projectyml-versions) and [.yml property file versions](/reference/project-configs/version#yml-property-file-versions).
 

From 8b05bd3a09d7663c4855c1500410bda3c0136043 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:13:43 +0100
Subject: [PATCH 636/703] Update model-versions.md

---
 website/docs/docs/collaborate/govern/model-versions.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index e21f963057f..37d8628c2eb 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -6,10 +6,13 @@ description: "Version models to help with lifecycle management"
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
+<VersionBlock firstVersion="1.5" lastVersion="1.8">
+  
 :::info New functionality
 This functionality is new in v1.5 — if you have thoughts, participate in [the discussion on GitHub](https://github.com/dbt-labs/dbt-core/discussions/6736)!
 :::
 
+</VersionBlock>
 
 import VersionsCallout from '/snippets/_version-callout.md';
 
@@ -21,7 +24,7 @@ Versioning APIs is a hard problem in software engineering. The root of the chall
 
 When sharing a final dbt model with other teams or systems, that model is operating like an API. When the producer of that model needs to make significant changes, how can they avoid breaking the queries of its users downstream?
 
-Model versioning is a tool to tackle this problem, thoughtfully and head-on. The goal of is not to make the problem go away entirely, nor to pretend it's easier or simpler than it is.
+Model versioning is a tool to tackle this problem, thoughtfully and head-on. The goal is not to make the problem go away entirely, nor to pretend it's easier or simpler than it is.
 
 ## Related documentation
 - [`versions`](/reference/resource-properties/versions)

From d79ed6f7ae06fd442ba3bcc83c4ca2c2b4b792a5 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:33:30 +0100
Subject: [PATCH 637/703] Update model-versions.md

---
 website/docs/docs/collaborate/govern/model-versions.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index 37d8628c2eb..a4a30ee0db0 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -9,7 +9,9 @@ keyword: governance, model version, model versioning, dbt model versioning
 <VersionBlock firstVersion="1.5" lastVersion="1.8">
   
 :::info New functionality
+
 This functionality is new in v1.5 — if you have thoughts, participate in [the discussion on GitHub](https://github.com/dbt-labs/dbt-core/discussions/6736)!
+
 :::
 
 </VersionBlock>

From 97858223c2bb9fd0deda2d33552df90eba512881 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 16:43:12 +0100
Subject: [PATCH 638/703] Update model-versions.md

---
 website/docs/docs/collaborate/govern/model-versions.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index a4a30ee0db0..e47c5764af7 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -6,7 +6,7 @@ description: "Version models to help with lifecycle management"
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
-<VersionBlock firstVersion="1.5" lastVersion="1.8">
+<VersionBlock firstVersion="1.5">
   
 :::info New functionality
 

From 74a370d1078d23349e9ce4352178a1c604499098 Mon Sep 17 00:00:00 2001
From: Drew McLean <drew.mclean@dbtlabs.com>
Date: Thu, 10 Aug 2023 12:30:06 -0400
Subject: [PATCH 639/703] Update set-up-sso-saml-2.0.md

Remove the link to `Configuring SAML endpoints in AD` which points to a hash mark that doesn't exist and is confusing customers.
---
 website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
index d5a16e91792..22a8094553d 100644
--- a/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
+++ b/website/docs/docs/cloud/manage-access/set-up-sso-saml-2.0.md
@@ -303,7 +303,7 @@ Follow these steps to set up single sign-on (SSO) with dbt Cloud:
 5. Select **Integrate any other application you don't find in the gallery (Non-gallery)** as the application type.
 6. Click **Create**.
 7. You can find the new application by clicking **Enterprise applications** and selecting **All applications**.
-8. Click the application you just created and follow the instructions for configuring it in [Configuring SAML endpoints in AD](#configuring-saml-endpoints-in-ad).
+8. Click the application you just created.
 9. Select **Single sign-on** under Manage in the left navigation.
 10. Click **Set up single sign on** under Getting Started.
 11. Click **SAML** in "Select a single sign-on method" section.

From 638eb116b7fcc25b8e20a1ee5f0e673364a5b43a Mon Sep 17 00:00:00 2001
From: Will Stamatis <114952643+wlsdbt@users.noreply.github.com>
Date: Thu, 10 Aug 2023 12:42:30 -0400
Subject: [PATCH 640/703] Update cumulative-metrics.md

corrected "ration" to "ratio"
---
 website/docs/docs/build/cumulative-metrics.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/cumulative-metrics.md b/website/docs/docs/build/cumulative-metrics.md
index 3b4d2327e8a..5312d133d4d 100644
--- a/website/docs/docs/build/cumulative-metrics.md
+++ b/website/docs/docs/build/cumulative-metrics.md
@@ -14,7 +14,7 @@ This metric is common for calculating things like weekly active users, or month-
 | --------- | ----------- | ---- |
 | `name` | The name of the metric. | Required |
 | `description` | The description of the metric. | Optional |
-| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required |
 | `label` | The value that will be displayed in downstream tools. | Required |
 | `type_params` | The type parameters of the metric. | Required |
 | `measure` | The measure you are referencing. | Required |

From 14e3829e46f183fd12490e66475357b0e095a8a7 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 17:49:47 +0100
Subject: [PATCH 641/703] Update simple.md

fix typos
---
 website/docs/docs/build/simple.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/build/simple.md b/website/docs/docs/build/simple.md
index bebd7c4589a..7022ca9d007 100644
--- a/website/docs/docs/build/simple.md
+++ b/website/docs/docs/build/simple.md
@@ -6,7 +6,7 @@ sidebar_label: Simple
 tags: [Metrics, Semantic Layer]
 ---
 
-Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your data platform, and can be filtered by one or multiple dimensions.
+Simple metrics are metrics that directly reference a single measure, without any additional measures involved. They are aggregations over a column in your data platform and can be filtered by one or multiple dimensions.
 
  The parameters, description, and type for simple metrics are: 
 
@@ -14,7 +14,7 @@ Simple metrics are metrics that directly reference a single measure, without any
 | --------- | ----------- | ---- |
 | `name` | The name of the metric. | Required |
 | `description` | The description of the metric. | Optional |
-| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required |
 | `label` | The value that will be displayed in downstream tools. | Required |
 | `type_params` | The type parameters of the metric. | Required |
 | `measure` | The measure you're referencing. | Required |

From 7fa71abe7b1695be7d773b6efcbab227e0805a65 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 17:50:36 +0100
Subject: [PATCH 642/703] Update derived-metrics.md

---
 website/docs/docs/build/derived-metrics.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/derived-metrics.md b/website/docs/docs/build/derived-metrics.md
index 53d567f0867..375794cc5c8 100644
--- a/website/docs/docs/build/derived-metrics.md
+++ b/website/docs/docs/build/derived-metrics.md
@@ -14,7 +14,7 @@ In MetricFlow, derived metrics are metrics created by defining an expression usi
 | --------- | ----------- | ---- |
 | `name` | The name of the metric. | Required |
 | `description` | The description of the metric. | Optional |
-| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required |
 | `label` | The value that will be displayed in downstream tools. | Required |
 | `type_params` | The type parameters of the metric. | Required |
 | `expr` | The derived expression. | Required |

From 408d41d6dbe72840df48f1eb8e88568ce81a6b84 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Aug 2023 17:51:02 +0100
Subject: [PATCH 643/703] Update ratio-metrics.md

---
 website/docs/docs/build/ratio-metrics.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/ratio-metrics.md b/website/docs/docs/build/ratio-metrics.md
index d2640d09016..97efe0f55bf 100644
--- a/website/docs/docs/build/ratio-metrics.md
+++ b/website/docs/docs/build/ratio-metrics.md
@@ -14,7 +14,7 @@ Ratio allows you to create a ratio between two metrics. You simply specify a num
 | --------- | ----------- | ---- |
 | `name` | The name of the metric. | Required |
 | `description` | The description of the metric. | Optional |
-| `type` | The type of the metric (cumulative, derived, ration, or simple). | Required |
+| `type` | The type of the metric (cumulative, derived, ratio, or simple). | Required |
 | `label` | The value that will be displayed in downstream tools. | Required |
 | `type_params` | The type parameters of the metric. | Required |
 | `numerator` | The name of the metric used for the numerator, or structure of properties. | Required |

From a2887246fb1ffe9546de3a02d4b736a8bde3c00a Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Thu, 10 Aug 2023 17:58:17 +0100
Subject: [PATCH 644/703] fix callout

---
 website/docs/docs/collaborate/govern/model-versions.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index e47c5764af7..49ed65f9a36 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -6,12 +6,10 @@ description: "Version models to help with lifecycle management"
 keyword: governance, model version, model versioning, dbt model versioning
 ---
 
-<VersionBlock firstVersion="1.5">
-  
-:::info New functionality
+<VersionBlock firstVersion="1.5" lastVersion="1.8">
 
+:::info New functionality
 This functionality is new in v1.5 — if you have thoughts, participate in [the discussion on GitHub](https://github.com/dbt-labs/dbt-core/discussions/6736)!
-
 :::
 
 </VersionBlock>

From 6c0e849ceae6a8647436495ba186e3a99b726e3c Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 10 Aug 2023 10:03:13 -0700
Subject: [PATCH 645/703] Fold in PM feedback

---
 website/docs/docs/deploy/jobs.md |  8 ++++++++
 website/sidebars.js              | 10 +++++-----
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/website/docs/docs/deploy/jobs.md b/website/docs/docs/deploy/jobs.md
index b4eec18de12..1c86cc7047e 100644
--- a/website/docs/docs/deploy/jobs.md
+++ b/website/docs/docs/deploy/jobs.md
@@ -9,6 +9,14 @@ In dbt Cloud, you can create and set up triggers for these jobs:
 - [Deploy jobs](/docs/deploy/deploy-jobs)
 - [Continuous integration (CI) jobs](/docs/deploy/continuous-integration)  
 
+:::tip Join our beta 
+
+dbt Labs is currently running a beta that provides improved UI updates for setting up deploy jobs and CI jobs. For docs on deploy jobs, refer to [Create and schedule jobs (Beta version)](/docs/deploy/deploy-jobs?version=beta#create-and-schedule-jobs). For docs on CI jobs, refer to [Set up CI jobs (Beta version)](/docs/deploy/ci-jobs?version=beta#set-up-ci-jobs).
+
+If you're interested in joining our beta, please fill out our Google Form to [sign up](https://forms.gle/VxwBD1xjzouE84EQ6).
+
+:::
+
 Below is a comparison table that describes how deploy jobs and CI jobs behave differently:
 
 |  | Deploy Jobs | CI Jobs |
diff --git a/website/sidebars.js b/website/sidebars.js
index c51903d31d8..a789f27ab2e 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -331,17 +331,17 @@ const sidebarSettings = {
       items: [
         "docs/deploy/job-scheduler",
         "docs/deploy/deploy-environments",
-        "docs/deploy/jobs",
-        "docs/deploy/deploy-jobs",
+        "docs/deploy/continuous-integration",
         {
           type: "category",
-          label: "Continuous integration",
-          link: { type: "doc", id: "docs/deploy/continuous-integration" },
+          label: "Jobs",
+          link: { type: "doc", id: "docs/deploy/jobs" },
           items: [
+            "docs/deploy/deploy-jobs",
             "docs/deploy/ci-jobs",
+            "docs/deploy/job-commands",
           ],
         },
-        "docs/deploy/job-commands",
         {
           type: "category",
           label: "Monitor jobs and alerts",

From 606aea5ec2007f454be4422f83e3f52c94913400 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 10 Aug 2023 10:07:30 -0700
Subject: [PATCH 646/703] Update website/docs/docs/deploy/deploy-jobs.md

---
 website/docs/docs/deploy/deploy-jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deploy-jobs.md b/website/docs/docs/deploy/deploy-jobs.md
index 9f903dfe850..3d754beb609 100644
--- a/website/docs/docs/deploy/deploy-jobs.md
+++ b/website/docs/docs/deploy/deploy-jobs.md
@@ -147,5 +147,5 @@ Refer to the following example snippets:
 ## Related docs
 
 - [Artifacts](/docs/deploy/artifacts)
-- [Job commands](/docs/deploy/job-commands)
+- [Continuous integration (CI) jobs](/docs/deploy/ci-jobs)
 - [Webhooks](/docs/deploy/webhooks)
\ No newline at end of file

From cdc82e0eb3a42cbb748b958588f7b71b561d1828 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 10 Aug 2023 10:12:49 -0700
Subject: [PATCH 647/703] Fix warning about old redirect error

---
 website/static/_redirects | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index a3833dd9888..ae726f816c4 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -151,7 +151,7 @@ docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab /docs/deploy/dbt-cloud-job
 /docs/deploy/architecture /docs/cloud/about-cloud/architecture 301
 /docs/deploy/single-tenant /docs/cloud/about-cloud/tenancy 301
 /docs/deploy/multi-tenant /docs/cloud/about-cloud/tenancy 301
-/docs/cloud/manage-access/about-access 301 /docs/cloud/manage-access/about-user-access 301
+/docs/cloud/manage-access/about-access /docs/cloud/manage-access/about-user-access 301
 /docs/collaborate/git/connect-github /docs/cloud/git/connect-github 301
 /docs/collaborate/git/connect-gitlab /docs/cloud/git/connect-gitlab 301
 /docs/collaborate/git/connect-azure-devops /docs/cloud/git/connect-azure-devops 301

From 92c62317fc32b1a8c9be3d2a76a32a916cda4afe Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 10 Aug 2023 11:25:46 -0700
Subject: [PATCH 648/703] Update website/static/_redirects

reproduce warning. afterward fix again
---
 website/static/_redirects | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index ae726f816c4..a3833dd9888 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -151,7 +151,7 @@ docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab /docs/deploy/dbt-cloud-job
 /docs/deploy/architecture /docs/cloud/about-cloud/architecture 301
 /docs/deploy/single-tenant /docs/cloud/about-cloud/tenancy 301
 /docs/deploy/multi-tenant /docs/cloud/about-cloud/tenancy 301
-/docs/cloud/manage-access/about-access /docs/cloud/manage-access/about-user-access 301
+/docs/cloud/manage-access/about-access 301 /docs/cloud/manage-access/about-user-access 301
 /docs/collaborate/git/connect-github /docs/cloud/git/connect-github 301
 /docs/collaborate/git/connect-gitlab /docs/cloud/git/connect-gitlab 301
 /docs/collaborate/git/connect-azure-devops /docs/cloud/git/connect-azure-devops 301

From a47cbf583fe75c4d8007552dae66c2346790a456 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 10 Aug 2023 11:47:44 -0700
Subject: [PATCH 649/703] Update website/docs/docs/deploy/jobs.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/deploy/jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/jobs.md b/website/docs/docs/deploy/jobs.md
index 1c86cc7047e..28928008dc0 100644
--- a/website/docs/docs/deploy/jobs.md
+++ b/website/docs/docs/deploy/jobs.md
@@ -21,7 +21,7 @@ Below is a comparison table that describes how deploy jobs and CI jobs behave di
 
 |  | Deploy Jobs | CI Jobs |
 | --- | --- | --- |
-| Purpose | Building production data assets | Building and testing new code before merging changes into production |
+| Purpose | Builds production data assets | Builds and tests new code before merging changes into production |
 | Trigger types | Triggered by a schedule or by API | Triggered by a webhook from a commit to a PR or by API |
 | Destination | Builds into a production database and schema | Builds into a staging database and ephemeral schema, lived for the lifetime of the PR |
 | Execution Mode | Runs execute sequentially, so as to not have collisions on the underlying DAG. | Runs execute in parallel to promote team velocity. |

From f0c32980e589665f717b414d9ad9ec900d5a1586 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Thu, 10 Aug 2023 11:48:31 -0700
Subject: [PATCH 650/703] Update website/docs/docs/deploy/jobs.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/deploy/jobs.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/jobs.md b/website/docs/docs/deploy/jobs.md
index 28928008dc0..d66cdd87619 100644
--- a/website/docs/docs/deploy/jobs.md
+++ b/website/docs/docs/deploy/jobs.md
@@ -25,5 +25,5 @@ Below is a comparison table that describes how deploy jobs and CI jobs behave di
 | Trigger types | Triggered by a schedule or by API | Triggered by a webhook from a commit to a PR or by API |
 | Destination | Builds into a production database and schema | Builds into a staging database and ephemeral schema, lived for the lifetime of the PR |
 | Execution Mode | Runs execute sequentially, so as to not have collisions on the underlying DAG. | Runs execute in parallel to promote team velocity. |
-| Efficiency run savings | Over scheduled jobs are detected and unnecessary runs are canceled to avoid queue clog. | Run cancellation happens when an in-flight run becomes stale due to a new commit of the PR being pushed. |
+| Efficiency run savings | Detects over scheduled jobs and cancels unnecessary runs to avoid queue clog. | Cancels runs when an in-flight run becomes stale when a new commit is pushed to the pull request. |
 | State comparison | Only sometimes needs to detect state | Almost always needs to compare state against the production environment to build on modified code and its dependents. |

From 51487c9db520564d6e63bdbfc48340f7844195aa Mon Sep 17 00:00:00 2001
From: Ly Nguyen <ly.nguyen@dbtlabs.com>
Date: Thu, 10 Aug 2023 11:54:38 -0700
Subject: [PATCH 651/703] Fix malformed redirect

---
 website/static/_redirects | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/static/_redirects b/website/static/_redirects
index a3833dd9888..ae726f816c4 100644
--- a/website/static/_redirects
+++ b/website/static/_redirects
@@ -151,7 +151,7 @@ docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab /docs/deploy/dbt-cloud-job
 /docs/deploy/architecture /docs/cloud/about-cloud/architecture 301
 /docs/deploy/single-tenant /docs/cloud/about-cloud/tenancy 301
 /docs/deploy/multi-tenant /docs/cloud/about-cloud/tenancy 301
-/docs/cloud/manage-access/about-access 301 /docs/cloud/manage-access/about-user-access 301
+/docs/cloud/manage-access/about-access /docs/cloud/manage-access/about-user-access 301
 /docs/collaborate/git/connect-github /docs/cloud/git/connect-github 301
 /docs/collaborate/git/connect-gitlab /docs/cloud/git/connect-gitlab 301
 /docs/collaborate/git/connect-azure-devops /docs/cloud/git/connect-azure-devops 301

From 6c958e8941c746b9aed6e0d3949e5062eeb12b7d Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Fri, 11 Aug 2023 12:59:15 -0400
Subject: [PATCH 652/703] Apply suggestions from code review

Co-authored-by: Grace Goheen <53586774+graciegoheen@users.noreply.github.com>
---
 website/docs/reference/resource-properties/constraints.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/docs/reference/resource-properties/constraints.md b/website/docs/reference/resource-properties/constraints.md
index de5b3ef7284..c84af0ab64d 100644
--- a/website/docs/reference/resource-properties/constraints.md
+++ b/website/docs/reference/resource-properties/constraints.md
@@ -378,10 +378,10 @@ select
 version: 2
 
 models:
-  - name: double_nested_fields
+  - name: nested_column_constraints_example
     config:
-      contract: {enforced: true}
-      materialized: table
+      contract: 
+        enforced: true
     columns:
       - name: a
         data_type: string

From 84853128592217e5985b8223929da3727a1cdb13 Mon Sep 17 00:00:00 2001
From: "Leona B. Campbell" <3880403+runleonarun@users.noreply.github.com>
Date: Fri, 11 Aug 2023 17:08:33 -0700
Subject: [PATCH 653/703] Update 3-dbt-cloud-job-on-merge.md

---
 .../custom-cicd-pipelines/3-dbt-cloud-job-on-merge.md         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/orchestration/custom-cicd-pipelines/3-dbt-cloud-job-on-merge.md b/website/docs/guides/orchestration/custom-cicd-pipelines/3-dbt-cloud-job-on-merge.md
index d618f9eec64..d22d1d14284 100644
--- a/website/docs/guides/orchestration/custom-cicd-pipelines/3-dbt-cloud-job-on-merge.md
+++ b/website/docs/guides/orchestration/custom-cicd-pipelines/3-dbt-cloud-job-on-merge.md
@@ -1,11 +1,11 @@
 ---
-title: Run a dbt Cloud job on merge
+title: Run a dbt Cloud job on merge 
 id: 3-dbt-cloud-job-on-merge
 ---
 
 This job will take a bit more to setup, but is a good example of how to call the dbt Cloud API from a CI/CD pipeline. The concepts presented here can be generalized and used in whatever way best suits your use case.
 
-The setup below shows how to call the dbt Cloud API to run a job every time there is a push to your main branch (The branch where pull requests are typically merged. Commonly referred to as the main, primary, or master branch, but can be named differently).
+The setup below shows how to call the dbt Cloud API to run a job every time there's a push to your main branch (The branch where pull requests are typically merged. Commonly referred to as the main, primary, or master branch, but can be named differently).
 
 
 ### 1. Get your dbt Cloud API key

From 19c94df98b48251cba7aeccb13f7b3c382a0ccfc Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Fri, 11 Aug 2023 21:23:17 -0400
Subject: [PATCH 654/703] Updating billing page

---
 website/docs/docs/cloud/billing.md | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md
index 76b37560be3..b25ac4518b6 100644
--- a/website/docs/docs/cloud/billing.md
+++ b/website/docs/docs/cloud/billing.md
@@ -50,20 +50,25 @@ dbt Cloud offers several [plans](https://www.getdbt.com/pricing) with different
 
 ### Developer plan billing
 
-Developer plans are free and include one Developer license and 5,000 models each month. Models are refreshed at the beginning of each calendar month. If you exceed 5,000 models, any subsequent runs will be canceled until models are refreshed or until you upgrade to a paid plan. The rest of the dbt Cloud platform is still accessible, and no work will be lost. 
+Developer plans are free and include one Developer license and 3,000 models each month. Models are refreshed at the beginning of each calendar month. If you exceed 3,000 models, any subsequent runs will be canceled until models are refreshed or until you upgrade to a paid plan. The rest of the dbt Cloud platform is still accessible, and no work will be lost.
+
+All included successful models built numbers above reflect our most current pricing and packaging. Based on your usage terms when you signed up for the Developer Plan, the included model entitlements may be different from what’s reflected above.
+
 
 ### Team plan billing 
 
-Team customers pay monthly via credit card for seats and usage, and accounts include 20,000 models monthly. Seats are charged upfront at the beginning of the month. If you add seats during the month, seats will be prorated and charged on the same day. Seats removed during the month will be reflected on the next invoice and are not eligible for refunds. You can change the credit card information and the number of seats from the billings section anytime. Accounts will receive one monthly invoice that includes the upfront charge for the seats and the usage charged in arrears from the previous month. 
+Team customers pay monthly via credit card for seats and usage, and accounts include 15,000 models monthly. Seats are charged upfront at the beginning of the month. If you add seats during the month, seats will be prorated and charged on the same day. Seats removed during the month will be reflected on the next invoice and are not eligible for refunds. You can change the credit card information and the number of seats from the billings section anytime. Accounts will receive one monthly invoice that includes the upfront charge for the seats and the usage charged in arrears from the previous month.
+
+Usage is calculated and charged in arrears for the previous month. If you exceed 15,000 models in any month, you will be billed for additional usage on your next invoice. Additional use is billed at the rates on our [pricing page](https://www.getdbt.com/pricing). 
 
-Usage is calculated and charged in arrears for the previous month. If you exceed 20,000 models in any month, you will be billed for additional usage on your next invoice. Additional use is billed at the rates on our [pricing page](https://www.getdbt.com/pricing). 
 
+Included models that are not consumed do not roll over to future months. You can estimate your bill with a simple formula:
 
-Included models that are not consumed do not roll over to future months. You can estimate your bill with a simple formula: 
+`($100 x number of developer seats) + ((models built - 15,000) x $0.01)`
 
-`($100 x number of developer seats) + ((models built - 20,000) x $0.01)`
+If you cancel your plan, the plan will remain active through the end of the current billing period and may continue to accrue usage. Please review the Managing usage section [#managing-usage] below to avoid further usage accruing. If your account has exceeded the 15,000 included successful models built during the month of cancellation, a usage invoice will be sent the following month.
 
-If you cancel your plan, the plan will remain active through the end of the current billing period and may continue to accrue usage. Please review the Managing usage section [#managing-usage] below to avoid further usage accruing. If your account has exceeded the 20,000 included successful models built during the month of cancellation, a usage invoice will be sent the following month.
+All included successful models built numbers above reflect our most current pricing and packaging. Based on your usage terms when you signed up for the Team Plan, the included model entitlements may be different from what’s reflected above.
 
 :::note Legacy pricing plans
  

From 9c7b163843ffb2038c496e37bbc7a05408969042 Mon Sep 17 00:00:00 2001
From: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
Date: Fri, 11 Aug 2023 21:27:41 -0400
Subject: [PATCH 655/703] Removing paragraph

---
 website/docs/docs/cloud/billing.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/website/docs/docs/cloud/billing.md b/website/docs/docs/cloud/billing.md
index b25ac4518b6..e271240e899 100644
--- a/website/docs/docs/cloud/billing.md
+++ b/website/docs/docs/cloud/billing.md
@@ -66,8 +66,6 @@ Included models that are not consumed do not roll over to future months. You can
 
 `($100 x number of developer seats) + ((models built - 15,000) x $0.01)`
 
-If you cancel your plan, the plan will remain active through the end of the current billing period and may continue to accrue usage. Please review the Managing usage section [#managing-usage] below to avoid further usage accruing. If your account has exceeded the 15,000 included successful models built during the month of cancellation, a usage invoice will be sent the following month.
-
 All included successful models built numbers above reflect our most current pricing and packaging. Based on your usage terms when you signed up for the Team Plan, the included model entitlements may be different from what’s reflected above.
 
 :::note Legacy pricing plans

From d6dfd3c049cd25c206b66e2dc0cb29ba294b1315 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 09:15:30 -0400
Subject: [PATCH 656/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index 852f7202465..af3750a8f74 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -84,7 +84,7 @@ The console section, located below the File editor, includes various console tab
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-console-overview.jpg" width="100%" title="The Console section is located below the File editor and has various tabs and buttons to help execute tasks"/>
 
 1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not and sends the results to the Results console tab. 
-   * To prevent the IDE from returning too much data and causing browser problems, there is a 500-row limit automatically set to queries executed using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
+   * **500-row limit:** To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
    * In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
    * The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
 

From e226bac99271d3dbede248c7cd558f260163b5e4 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 09:16:16 -0400
Subject: [PATCH 657/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index af3750a8f74..d98cf55d824 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -85,7 +85,7 @@ The console section, located below the File editor, includes various console tab
 
 1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not and sends the results to the Results console tab. 
    * **500-row limit:** To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
-   * In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
+   * **Change row limit default:** In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
    * The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
 
 2. **Compile button &mdash;** The Compile button compiles the SQL code from the active File Editor, irrespective of its save status, and outputs it to the Compiled Code tab.

From bef5892127d798e3824c48b4be3b2d26eeef0792 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 09:16:24 -0400
Subject: [PATCH 658/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index d98cf55d824..9de31eb9f48 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -109,7 +109,7 @@ The console section, located below the File editor, includes various console tab
 
 ## Invocation history
 
-The Invocation History Drawer stores information on dbt invocations in the IDE. When you invoke a command (like executing a dbt command such as `dbt run`), the associated logs are displayed in the Invocation History Drawer. 
+The Invocation History Drawer stores information on dbt invocations in the IDE. When you invoke a command, like executing a dbt command such as `dbt run`, the associated logs are displayed in the Invocation History Drawer. 
 
 You can open the drawer in multiple ways: 
 - Clicking the `^` icon next to the Command bar on the lower left of the page

From f576ae69bab6d1679135995838079d1df1b60fda Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 09:16:29 -0400
Subject: [PATCH 659/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index 9de31eb9f48..f3951ec2c7d 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -86,7 +86,7 @@ The console section, located below the File editor, includes various console tab
 1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not and sends the results to the Results console tab. 
    * **500-row limit:** To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
    * **Change row limit default:** In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
-   * The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
+   * **Specify records returned:** The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
 
 2. **Compile button &mdash;** The Compile button compiles the SQL code from the active File Editor, irrespective of its save status, and outputs it to the Compiled Code tab.
 

From 8209f3df0640babd471db5a208d7e8d6042371fb Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 09:17:01 -0400
Subject: [PATCH 660/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md

---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index f3951ec2c7d..5ca99cf1c5e 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -59,7 +59,7 @@ The IDE features some delightful tools and layouts to make it easier for you to
 
    - **Version Control Options menu &mdash;** Below the Git Actions button, the **Changes** section, which lists all file changes since the last commit. You can click on a change to open the Git Diff View to see the inline changes. You can also right-click any file and use the file-specific options in the Version Control Options menu.
 
-<Lightbox src="/img/docs/dbt-cloud/cloud-ide/version-control-options-menu.png" width="30%" title=" Right-click edited files to access Version Control Options menu"/>
+<Lightbox src="/img/docs/dbt-cloud/cloud-ide/version-control-options-menu.png" width="30%" title="Right-click edited files to access Version Control Options menu"/>
 
 ## Additional editing features
 

From c349de602d2f2d3554b95f338c72df31a2e6483f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 14 Aug 2023 09:35:28 -0400
Subject: [PATCH 661/703] address leona's feedback

---
 .../docs/cloud/dbt-cloud-ide/ide-user-interface.md  | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index 5ca99cf1c5e..4e053b1419c 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -84,9 +84,16 @@ The console section, located below the File editor, includes various console tab
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-console-overview.jpg" width="100%" title="The Console section is located below the File editor and has various tabs and buttons to help execute tasks"/>
 
 1. **Preview button &mdash;** When you click on the Preview button, it runs the SQL in the active file editor regardless of whether you have saved it or not and sends the results to the Results console tab. 
-   * **500-row limit:** To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the **Preview Button**. You can modify this by adding `limit your_number` at the end of your SQL statement. For example, `SELECT * FROM` table `limit 100` will return up to 100 rows. Remember that you must write the `limit your_number` explicitly and cannot derive it from a macro.
-   * **Change row limit default:** In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on **Change row display** next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.
-   * **Specify records returned:** The IDE also supports `SELECT TOP #`, which specifies the number of records to return.
+
+<details>
+<summary>Row limits in IDE</summary>
+The dbt Cloud IDE returns default row limits, however, you can also specify the number of records returned. Refer to the following sub-bullets for more info: <br /><br />
+<ul>
+<li><b>500-row limit:</b> To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the <b>Preview Button</b>. You can modify this by adding <code>limit your_number</code> at the end of your SQL statement. For example, <code>SELECT * FROM</code> table <code>limit 100</code> will return up to 100 rows. Remember that you must write the <code>limit your_number</code> explicitly and cannot derive it from a macro.</li>
+<li><b>Change row limit default:</b> In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on <b>Change row display</b> next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.</li>
+<li><b>Specify records returned:</b> The IDE also supports <code>SELECT TOP #</code>, which specifies the number of records to return.</li>
+</ul>
+</details>
 
 2. **Compile button &mdash;** The Compile button compiles the SQL code from the active File Editor, irrespective of its save status, and outputs it to the Compiled Code tab.
 

From c7a14c5fe9d7c778df898e20809ad43c43aaa40f Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 14 Aug 2023 09:36:26 -0400
Subject: [PATCH 662/703] add bold

---
 website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
index 4e053b1419c..0723df1b5ad 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/ide-user-interface.md
@@ -90,7 +90,7 @@ The console section, located below the File editor, includes various console tab
 The dbt Cloud IDE returns default row limits, however, you can also specify the number of records returned. Refer to the following sub-bullets for more info: <br /><br />
 <ul>
 <li><b>500-row limit:</b> To prevent the IDE from returning too much data and causing browser problems, dbt automatically sets a 500-row limit when using the <b>Preview Button</b>. You can modify this by adding <code>limit your_number</code> at the end of your SQL statement. For example, <code>SELECT * FROM</code> table <code>limit 100</code> will return up to 100 rows. Remember that you must write the <code>limit your_number</code> explicitly and cannot derive it from a macro.</li>
-<li><b>Change row limit default:</b> In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the **Results** tab when you run a query. To adjust the setting you can click on <b>Change row display</b> next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.</li>
+<li><b>Change row limit default:</b> In dbt version 1.6 or higher, you have the ability to change the default limit of 500 rows shown in the <b>Results</b> tab when you run a query. To adjust the setting you can click on <b>Change row display</b> next to the displayed rows. Keep in mind that you can't set it higher than 10,000 rows. If you refresh the page or close your development session, the default limit will go back to 500 rows.</li>
 <li><b>Specify records returned:</b> The IDE also supports <code>SELECT TOP #</code>, which specifies the number of records to return.</li>
 </ul>
 </details>

From fcb6b563936a7efa4c43ec161aac2cca1d777b28 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Mon, 14 Aug 2023 09:54:49 -0400
Subject: [PATCH 663/703] add links

---
 website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index 6d8631a41e1..b9622acbeaf 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -54,7 +54,7 @@ To stay informed on IDE updates, read [dbt Cloud IDE release notes](/tags/ide),
 | **File state indicators**  |  Ability to see when changes or actions have been made to the file. The indicators **M, D, A,** and **•** appear to the right of your file or folder name and indicate the actions performed: <br /> <br /> - Unsaved **(•)** &mdash; The IDE detects unsaved changes to your file/folder<br /> - Modification **(M)** &mdash; The IDE detects a modification of existing files/folders<br /> - Added **(A)** &mdash; The IDE detects added files<br/> - Deleted **(D)** &mdash; The IDE detects deleted files.
 | **IDE version control** | The IDE version control section and git button allow you to apply the concept of [version control](/docs/collaborate/git/version-control-basics) to your project directly into the IDE. <br /><br /> - Create or change branches<br /> - Commit or revert individual files by right-clicking the edited file<br /> - [Resolve merge conflicts](/docs/collaborate/git/merge-conflicts)<br /> - Execute git commands using the git button <br /> - Link to the repo directly by clicking the branch name |
 | **Project documentation** | Generate and view your [project documentation](/docs/collaborate/build-and-view-your-docs) for your dbt project in real-time. You can inspect and verify what your project's documentation will look like before you deploy your changes to production. |
-| **Preview and Compile button** | You can run your code against your data platform by clicking the **Preview**. Use the **Compile** button in the IDE to generate executable SQL, which occurs locally within dbt. |
+| **Preview and Compile button** | You can run your code against your data platform by clicking the [**Preview**](/docs/cloud/dbt-cloud-ide/ide-user-interface#console-section). Use the [**Compile**](/docs/cloud/dbt-cloud-ide/ide-user-interface#console-section) button in the IDE to generate executable SQL, which occurs locally within dbt. |
 | **Build, test, and run button**  | Build, test, and run your project with a button click or by using the Cloud IDE command bar.  
 | **Command bar** | You can enter and run commands from the command bar at the bottom of the IDE. Use the [rich model selection syntax](/reference/node-selection/syntax) to execute [dbt commands](/reference/dbt-commands) directly within dbt Cloud. You can also view the history, status, and logs of previous runs by clicking History on the left of the bar.
 | **Drag and drop**  | Drag and drop files located in the file explorer, and use the file breadcrumb on the top of the IDE for quick, linear navigation. Access adjacent files in the same file by right-clicking on the breadcrumb file.  

From 986cae241bcdda6634bf2a25cd351ea9e3706d10 Mon Sep 17 00:00:00 2001
From: Ernesto Ongaro <ernesto.ongaro@dbtlabs.com>
Date: Mon, 14 Aug 2023 16:33:56 +0100
Subject: [PATCH 664/703] Clarify flags

---
 website/docs/reference/commands/compile.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index a9821c0ff12..cf04ddff2d1 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -67,8 +67,8 @@ select * from renamed
 </VersionBlock>
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
-- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query.
-- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one.
+- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag. 
+- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag.
 
 
 ### FAQs

From 02d69282be7d9641f3327f8783359f8ec2c13eff Mon Sep 17 00:00:00 2001
From: Ernesto Ongaro <ernesto.ongaro@dbtlabs.com>
Date: Mon, 14 Aug 2023 16:46:50 +0100
Subject: [PATCH 665/703] Update website/docs/reference/commands/compile.md

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index cf04ddff2d1..2616b8b4e9a 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -68,7 +68,7 @@ select * from renamed
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
 - `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag. 
-- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag.
+- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag, which means you need to add `dbt compile` as a prefix: `dbt compile --no-introspect`.
 
 
 ### FAQs

From 4c8d7143ee9a13e5b81e42922425539ac7ea4b31 Mon Sep 17 00:00:00 2001
From: Ernesto Ongaro <ernesto.ongaro@dbtlabs.com>
Date: Mon, 14 Aug 2023 16:46:57 +0100
Subject: [PATCH 666/703] Update website/docs/reference/commands/compile.md

Co-authored-by: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 2616b8b4e9a..60fe05d85e1 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -67,7 +67,7 @@ select * from renamed
 </VersionBlock>
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
-- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag. 
+- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag, which means you need to add `dbt` as a prefix: `dbt --no-populate-cache`.
 - `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag, which means you need to add `dbt compile` as a prefix: `dbt compile --no-introspect`.
 
 

From 49e89454ea2c0c7cfff5f0aeecfd1ecae551651d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 12:01:27 -0400
Subject: [PATCH 667/703] Update website/docs/reference/commands/compile.md

---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 60fe05d85e1..76b49c6a7ee 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -67,7 +67,7 @@ select * from renamed
 </VersionBlock>
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
-- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag, which means you need to add `dbt` as a prefix: `dbt --no-populate-cache`.
+- `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag, which means you need to add `dbt` as a prefix. For example: `dbt --no-populate-cache`.
 - `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag, which means you need to add `dbt compile` as a prefix: `dbt compile --no-introspect`.
 
 

From db5c8d8e5e8900accf51d416654cc32e1f6b9cd1 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 12:01:43 -0400
Subject: [PATCH 668/703] Update website/docs/reference/commands/compile.md

---
 website/docs/reference/commands/compile.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/commands/compile.md b/website/docs/reference/commands/compile.md
index 76b49c6a7ee..ed403d2af32 100644
--- a/website/docs/reference/commands/compile.md
+++ b/website/docs/reference/commands/compile.md
@@ -68,7 +68,7 @@ select * from renamed
 
 The command accesses the data platform to cache-related metadata, and to run introspective queries. Use the flags:
 - `--no-populate-cache` to disable the initial cache population. If metadata is needed, it will be a cache miss, requiring dbt to run the metadata query. This is a `dbt` flag, which means you need to add `dbt` as a prefix. For example: `dbt --no-populate-cache`.
-- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag, which means you need to add `dbt compile` as a prefix: `dbt compile --no-introspect`.
+- `--no-introspect` to disable [introspective queries](/faqs/warehouse/db-connection-dbt-compile#introspective-queries). dbt will raise an error if a model's definition requires running one. This is a `dbt compile` flag, which means you need to add `dbt compile` as a prefix. For example:`dbt compile --no-introspect`.
 
 
 ### FAQs

From a9345d864fabf94a28fd594b9f28f58c06480616 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 14:01:44 -0400
Subject: [PATCH 669/703] Update website/snippets/_cloud-environments-info.md

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index b41dc056b91..32032aa6a40 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -48,7 +48,7 @@ For more info, check out this [FAQ page on this topic](/faqs/Environments/custom
 
 :::important This feature is currently in beta
 
-Extended Attributes allow users to enter YAML attributes in their Environment settings. It's currently in beta and if you're interested in joining the beta, please contact us -- HOW CAN USES TRY THIS BETA? IF IT'S FEATURE FLAG, HOW DO THEY JOIN THE BETA? DO WE NEED TO ADD A FORM LINK? OR EMAIL?
+Extended Attributes is currently in beta for select users and is subject to change.
 :::
 
 Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings. It provides users with more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.

From 3e8f3372390b781faee2c0bcb6f3bba2c2f53fff Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 14:02:50 -0400
Subject: [PATCH 670/703] Update website/snippets/_cloud-environments-info.md

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 32032aa6a40..d0f986705ce 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -70,7 +70,7 @@ The **Extended Attributes** text box must contain valid characters only. Any inv
 - `+` (plus sign)
 - `%` (percentage sign)
 
-If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the keys. For each individual key:
+If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the profiles.yml attributes. For each individual attribute:
 
 - If the key exists in another source (such as your project settings), it will replace its value in the profile.
 - If the key doesn't exist, it will add the key or value pair to the profile. 

From 9d827da7fcb6dec55310baf19f28589bcf1f9a19 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Mon, 14 Aug 2023 14:03:51 -0400
Subject: [PATCH 671/703] Update _cloud-environments-info.md

---
 website/snippets/_cloud-environments-info.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index d0f986705ce..83bcce11836 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -72,8 +72,8 @@ The **Extended Attributes** text box must contain valid characters only. Any inv
 
 If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the profiles.yml attributes. For each individual attribute:
 
-- If the key exists in another source (such as your project settings), it will replace its value in the profile.
-- If the key doesn't exist, it will add the key or value pair to the profile. 
+- If the attribute exists in another source (such as your project settings), it will replace its value in the profile.
+- If the attribute doesn't exist, it will add the attribute or value pair to the profile. 
 
 The following code is an example of the types of attributes you can add to the **Extended Attributes** text box:
 
@@ -81,7 +81,6 @@ The following code is an example of the types of attributes you can add to the *
 type: postgres      
 host: localhost      
 user: alice      
-password: <your_password>      
 port: 5432      
 dbname: jaffle_shop      
 schema: dbt_alice      

From 83b132185eacefa7951131f233171a6eef38af93 Mon Sep 17 00:00:00 2001
From: Jay Allen <44067410+ujmediaservices@users.noreply.github.com>
Date: Mon, 14 Aug 2023 19:33:01 -0700
Subject: [PATCH 672/703] Removing dangling sentence

"Or" had nothing after it. I just removed it rather than add a new use case.
---
 website/docs/docs/collaborate/govern/model-access.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/collaborate/govern/model-access.md b/website/docs/docs/collaborate/govern/model-access.md
index 970f25ef87f..64b70416a2f 100644
--- a/website/docs/docs/collaborate/govern/model-access.md
+++ b/website/docs/docs/collaborate/govern/model-access.md
@@ -29,7 +29,7 @@ The two concepts will be closely related, as we develop multi-project collaborat
 
 ## Groups
 
-Models can be grouped under a common designation with a shared owner. For example, you could group together all models owned by a particular team, related to modeling a specific data source (`github`), or 
+Models can be grouped under a common designation with a shared owner. For example, you could group together all models owned by a particular team, or related to modeling a specific data source (`github`).
 
 Why define model `groups`? There are two reasons:
 - It turns implicit relationships into an explicit grouping, with a defined owner. By thinking about the interface boundaries _between_ groups, you can have a cleaner (less entangled) DAG. In the future, those interface boundaries could be appropriate as the interfaces between separate projects.

From 927820b5be0d7bb7b145fc186d9571f7e0c4bd5d Mon Sep 17 00:00:00 2001
From: Joel Labes <joel.labes@dbtlabs.com>
Date: Tue, 15 Aug 2023 14:34:39 +1200
Subject: [PATCH 673/703] Remove "new in 0.9.0" callout

---
 website/docs/reference/dbt-jinja-functions/ref.md | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/website/docs/reference/dbt-jinja-functions/ref.md b/website/docs/reference/dbt-jinja-functions/ref.md
index c500bb934ab..5d8475550b1 100644
--- a/website/docs/reference/dbt-jinja-functions/ref.md
+++ b/website/docs/reference/dbt-jinja-functions/ref.md
@@ -29,11 +29,7 @@ from {{ref('model_a')}}
 
 `ref()` is, under the hood, actually doing two important things. First, it is interpolating the schema into your model file to allow you to change your deployment schema via configuration. Second, it is using these references between models to automatically build the dependency graph. This will enable dbt to deploy models in the correct order when using `dbt run`.
 
-:::info New in 0.9.0
-
-The `{{ ref }}` function returns a `Relation` object that has the same `table`, `schema`, and `name` attributes at the [{{ this }}](/reference/dbt-jinja-functions/this) variable.
-
-:::
+The `{{ ref }}` function returns a `Relation` object that has the same `table`, `schema`, and `name` attributes as the [{{ this }}](/reference/dbt-jinja-functions/this) variable.
 
 ## Advanced ref usage
 

From 0932277536a1c3bccece84cbccd909f00c8143dd Mon Sep 17 00:00:00 2001
From: Jennifer Berk <jcberk@gmail.com>
Date: Mon, 14 Aug 2023 22:41:06 -0400
Subject: [PATCH 674/703] Fix revenue example indentation and defaults syntax
 on about-metricflow.md

Fixes the "revenue example" semantic model code indentation and defaults syntax on About MetricFlow.

Also adds a comment at the start of the second semantic model and makes it follow the same description-model-defaults order as the first semantic model, to make it easier for a new user to understand.
---
 website/docs/docs/build/about-metricflow.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 154e40d515d..02031bd0e55 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -147,18 +147,18 @@ semantic_models:
         type: time
         type_params:
           time_granularity: day
-      - name: customers
-        defaults: null
-        agg_time_dimension: first_ordered_at
-        description: >
-          Customer dimension table. The grain of the table is one row per
-          customer.
-        model: ref('customers') # The name of the dbt model and schema
+  - name: customers    #The name of the second semantic model
+    description: >
+      Customer dimension table. The grain of the table is one row per
+        customer.
+    model: ref('customers') #The name of the dbt model and schema
+    defaults:
+      agg_time_dimension: first_ordered_at
     entities: #Entities. These usually correspond to keys in the table.
       - name: customer 
         type: primary
         expr: customer_id
-    dimensions:
+    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
       - name: is_new_customer
         type: categorical
         expr: case when first_ordered_at is not null then true else false end

From 72ff189eadf0d352b29b37e7662122eebe97b4f6 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 08:41:59 -0400
Subject: [PATCH 675/703] Update website/docs/docs/build/about-metricflow.md

---
 website/docs/docs/build/about-metricflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index 02031bd0e55..ede185a5e39 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -158,7 +158,7 @@ semantic_models:
       - name: customer 
         type: primary
         expr: customer_id
-    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions: # Dimensions are either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
       - name: is_new_customer
         type: categorical
         expr: case when first_ordered_at is not null then true else false end

From f5fc3e4e472e06e7c31791043dcb38fc1b854369 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 08:47:13 -0400
Subject: [PATCH 676/703] Update about-metricflow.md

fix hashes
---
 website/docs/docs/build/about-metricflow.md | 32 ++++++++++-----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/website/docs/docs/build/about-metricflow.md b/website/docs/docs/build/about-metricflow.md
index ede185a5e39..b1a22b9072c 100644
--- a/website/docs/docs/build/about-metricflow.md
+++ b/website/docs/docs/build/about-metricflow.md
@@ -70,7 +70,7 @@ MetricFlow supports different metric types:
 
 In the upcoming sections, we'll show how data practitioners currently calculate metrics and compare it to how MetricFlow makes defining metrics easier and more flexible. 
 
-The following example data is based off the Jaffle Shop repo. You can view the complete [dbt project](https://github.com/dbt-labs/jaffle-sl-template). The tables we're using in our example model are:
+The following example data is based on the Jaffle Shop repo. You can view the complete [dbt project](https://github.com/dbt-labs/jaffle-sl-template). The tables we're using in our example model are:
 
 - `orders` is a production data platform export that has been cleaned up and organized for analytical consumption
 - `customers` is a partially denormalized table in this case with a column derived from the orders table through some upstream process
@@ -91,7 +91,7 @@ Next, we'll compare how data practitioners currently calculate metrics with mult
 <Tabs>
 <TabItem value="mulqueries" label="Calculate with multiple queries">
 
-The following example displays how data practitioners typically would calculate the order_total metric aggregated. It's also likely that analysts are asked for more details on a metric, like how much revenue came from new customers. 
+The following example displays how data practitioners typically would calculate the `order_total` metric aggregated. It's also likely that analysts are asked for more details on a metric, like how much revenue came from new customers. 
 
 Using the following query creates a situation where multiple analysts working on the same data, each using their own query method &mdash; this can lead to confusion, inconsistencies, and a headache for data management.
 
@@ -121,44 +121,44 @@ In the following three example tabs, use MetricFlow to define a semantic model t
 
 In this example, a measure named `order_total` is defined based on the order_total column in the `orders` table. 
 
-The time dimension `metric_time` provides daily granularity and can be aggregated to weekly or monthly time periods. Additionally, a categorical dimension called `is_new_customer` is specified in the `customers` semantic model.
+The time dimension `metric_time` provides daily granularity and can be aggregated into weekly or monthly time periods. Additionally, a categorical dimension called `is_new_customer` is specified in the `customers` semantic model.
 
 
 ```yaml
 semantic_models:
-  - name: orders    #The name of the semantic model
+  - name: orders    # The name of the semantic model
     description: |
-      Model containing order data. The grain of the table is the order id.
+      A model containing order data. The grain of the table is the order id.
     model: ref('orders') #The name of the dbt model and schema
     defaults:
       agg_time_dimension: metric_time
-    entities: #Entities. These usually correspond to keys in the table.table.
+    entities: # Entities, which usually correspond to keys in the table. 
       - name: order_id
         type: primary
       - name: customer
         type: foreign
         expr: customer_id
-    measures:   #Measures. These are the aggregations on the columns in the table.
+    measures:   # Measures, which are the aggregations on the columns in the table.
       - name: order_total
         agg: sum
-    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions: # Dimensions are either categorical or time. They add additional context to metrics and the typical querying pattern is Metric by Dimension.
       - name: metric_time
         expr: cast(ordered_at as date)
         type: time
         type_params:
           time_granularity: day
-  - name: customers    #The name of the second semantic model
+  - name: customers    # The name of the second semantic model
     description: >
       Customer dimension table. The grain of the table is one row per
         customer.
     model: ref('customers') #The name of the dbt model and schema
     defaults:
       agg_time_dimension: first_ordered_at
-    entities: #Entities. These usually correspond to keys in the table.
+    entities: # Entities, which  usually correspond to keys in the table.
       - name: customer 
         type: primary
         expr: customer_id
-    dimensions: # Dimensions are either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions: # Dimensions are either categorical or time. They add additional context to metrics and the typical querying pattern is Metric by Dimension.
       - name: is_new_customer
         type: categorical
         expr: case when first_ordered_at is not null then true else false end
@@ -178,20 +178,20 @@ Similarly, you could then add additional dimensions like `is_food_order` to your
 semantic_models:
   - name: orders
     description: |
-      Model containing order data. The grain of the table is the order id.
+      A model containing order data. The grain of the table is the order id.
     model: ref('orders')  #The name of the dbt model and schema
     defaults:
       agg_time_dimension: metric_time
-    entities: #Entities. These usually correspond to keys in the table.table.
+    entities: # Entities, which usually correspond to keys in the table
       - name: order_id
         type: primary
       - name: customer
         type: foreign
         expr: customer_id
-    measures: #Measures. These are the aggregations on the columns in the table.
+    measures: # Measures, which are the aggregations on the columns in the table.
       - name: order_total
         agg: sum
-    dimensions: #Dimensions,either categorical or time. These add additional context to metrics. The typical querying pattern is Metric by Dimension.
+    dimensions: # Dimensions are either categorical or time. They add additional context to metrics and the typical querying pattern is Metric by Dimension.
       - name: metric_time
         expr: cast(ordered_at as date)
         type: time
@@ -265,7 +265,7 @@ metrics:
 <details>
   <summary>How does the Semantic Layer handle joins?</summary>
   <div>
-    <div>MetricFlow builds joins based on the types of keys and parameters that are passed to entities. To better understand how joins are constructed see our documentations on join types.<br /><br />Rather than capturing arbitrary join logic, MetricFlow captures the types of each identifier and then helps the user to navigate to appropriate joins. This allows us to avoid the construction of fan out and chasm joins as well as generate legible SQL.</div>
+    <div>MetricFlow builds joins based on the types of keys and parameters that are passed to entities. To better understand how joins are constructed see our documentation on join types.<br /><br />Rather than capturing arbitrary join logic, MetricFlow captures the types of each identifier and then helps the user to navigate to appropriate joins. This allows us to avoid the construction of fan out and chasm joins as well as generate legible SQL.</div>
   </div>
 </details>
 <details>

From 4d2e05add0822fe56a19cafd0bf24563ac6bf511 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 08:49:01 -0400
Subject: [PATCH 677/703] Update
 website/docs/reference/dbt-jinja-functions/ref.md

---
 website/docs/reference/dbt-jinja-functions/ref.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/reference/dbt-jinja-functions/ref.md b/website/docs/reference/dbt-jinja-functions/ref.md
index 5d8475550b1..b9b14bed42a 100644
--- a/website/docs/reference/dbt-jinja-functions/ref.md
+++ b/website/docs/reference/dbt-jinja-functions/ref.md
@@ -29,7 +29,7 @@ from {{ref('model_a')}}
 
 `ref()` is, under the hood, actually doing two important things. First, it is interpolating the schema into your model file to allow you to change your deployment schema via configuration. Second, it is using these references between models to automatically build the dependency graph. This will enable dbt to deploy models in the correct order when using `dbt run`.
 
-The `{{ ref }}` function returns a `Relation` object that has the same `table`, `schema`, and `name` attributes as the [{{ this }}](/reference/dbt-jinja-functions/this) variable.
+The `{{ ref }}` function returns a `Relation` object that has the same `table`, `schema`, and `name` attributes as the [{{ this }} variable](/reference/dbt-jinja-functions/this).
 
 ## Advanced ref usage
 

From 4a190d59597a01b1bdbcacbd4902420566bc6901 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 09:27:12 -0400
Subject: [PATCH 678/703] remove callout

removing callout as eng fixed the bad ux where users had to delete a job first before deleting an environment. now a user can delete an environment, and any associated jobs will also automatically get deleted.
---
 .../faqs/Environments/delete-environment-job.md     | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/website/docs/faqs/Environments/delete-environment-job.md b/website/docs/faqs/Environments/delete-environment-job.md
index b649769f070..ce09490ac00 100644
--- a/website/docs/faqs/Environments/delete-environment-job.md
+++ b/website/docs/faqs/Environments/delete-environment-job.md
@@ -8,15 +8,6 @@ id: delete-environment-job
 
 To delete an environment or job in dbt Cloud, you must have a `developer` [license](/docs/cloud/manage-access/seats-and-users) and have the necessary [access permissions](/docs/cloud/manage-access/about-user-access). 
 
-:::info 📌 Delete a job first before deleting environment
-
-Deleting an environment doesn't automatically delete its associated job(s). If you delete an environment first without deleting the job, you won't be able to delete the job since it's without an environment. 
-
-To completely delete your environment, you _must_:
-1. First delete all jobs associated with that environment,
-2. Then, delete the environment. 
-:::
-
 **Delete a job**
 
 To delete a job or multiple jobs in dbt Cloud:
@@ -33,7 +24,7 @@ To delete a job or multiple jobs in dbt Cloud:
 
 5. Confirm your action in the **Confirm Delete** pop-up by clicking **Confirm Delete** in the bottom right to delete the job immediately. This action cannot be undone. However, you can create a new job with the same information if the deletion was made in error. 
 
-Refresh the page, and the deleted job should now be gone. If you want to delete multiple jobs, you'll need to perform these steps for each individual job. 
+Refresh the page, and the deleted job should now be gone. If you want to delete multiple jobs, you'll need to perform these steps for each job. 
 
 **Delete an environment**
 
@@ -54,4 +45,4 @@ To delete an environment in dbt Cloud:
 
 Refresh your page, and the deleted environment should now be gone. If you want to delete multiple environments, you'll need to perform these steps to delete each one. 
 
-If you're having any issues, feel free to [contact us](mailto:support@getdbt.com) for additional help.
\ No newline at end of file
+If you're having any issues, feel free to [contact us](mailto:support@getdbt.com) for additional help.

From fcbb3bc66e0c7d77721981333f14c3f34b2e6fb0 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 09:35:42 -0400
Subject: [PATCH 679/703] Update delete-environment-job.md

---
 website/docs/faqs/Environments/delete-environment-job.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/faqs/Environments/delete-environment-job.md b/website/docs/faqs/Environments/delete-environment-job.md
index ce09490ac00..1f4b8c343a9 100644
--- a/website/docs/faqs/Environments/delete-environment-job.md
+++ b/website/docs/faqs/Environments/delete-environment-job.md
@@ -8,7 +8,7 @@ id: delete-environment-job
 
 To delete an environment or job in dbt Cloud, you must have a `developer` [license](/docs/cloud/manage-access/seats-and-users) and have the necessary [access permissions](/docs/cloud/manage-access/about-user-access). 
 
-**Delete a job**
+## Delete a job
 
 To delete a job or multiple jobs in dbt Cloud:
 
@@ -26,7 +26,7 @@ To delete a job or multiple jobs in dbt Cloud:
 
 Refresh the page, and the deleted job should now be gone. If you want to delete multiple jobs, you'll need to perform these steps for each job. 
 
-**Delete an environment**
+## Delete an environment
 
 To delete an environment in dbt Cloud:
 

From b70c0dbc46bb4b1d77bee5e60c8ceb60135fc92b Mon Sep 17 00:00:00 2001
From: Charlie Hahn <cyahahn@gmail.com>
Date: Tue, 15 Aug 2023 09:29:42 -0400
Subject: [PATCH 680/703] Update custom-schemas.md

Update grammar from "overriding" to "override"
---
 website/docs/docs/build/custom-schemas.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/custom-schemas.md b/website/docs/docs/build/custom-schemas.md
index b8dbb9a0846..b5f29ce76a2 100644
--- a/website/docs/docs/build/custom-schemas.md
+++ b/website/docs/docs/build/custom-schemas.md
@@ -103,7 +103,7 @@ To modify how dbt generates schema names, you should add a macro named `generate
 
 If you're modifying how dbt generates schema names, don't just replace ```{{ default_schema }}_{{ custom_schema_name | trim }}``` with ```{{ custom_schema_name | trim }}``` in the ```generate_schema_name``` macro. 
 
-Removing ```{{ default_schema }}``` causes developers to overriding each other's models when custom schemas are defined. This can also cause issues during development and continuous integration (CI).
+Removing ```{{ default_schema }}``` causes developers to override each other's models when custom schemas are defined. This can also cause issues during development and continuous integration (CI).
 
 ❌ The following code block is an example of what your code _should not_ look like: 
 ```sql

From 4dded1de6ef92b32df019eacda605ea79b5b0e2c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 09:40:36 -0400
Subject: [PATCH 681/703] Update website/docs/docs/build/custom-schemas.md

---
 website/docs/docs/build/custom-schemas.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/build/custom-schemas.md b/website/docs/docs/build/custom-schemas.md
index b5f29ce76a2..d8a319f40de 100644
--- a/website/docs/docs/build/custom-schemas.md
+++ b/website/docs/docs/build/custom-schemas.md
@@ -103,7 +103,7 @@ To modify how dbt generates schema names, you should add a macro named `generate
 
 If you're modifying how dbt generates schema names, don't just replace ```{{ default_schema }}_{{ custom_schema_name | trim }}``` with ```{{ custom_schema_name | trim }}``` in the ```generate_schema_name``` macro. 
 
-Removing ```{{ default_schema }}``` causes developers to override each other's models when custom schemas are defined. This can also cause issues during development and continuous integration (CI).
+If you remove ```{{ default_schema }}```, it causes developers to override each other's models if they create their own custom schemas. This can also cause issues during development and continuous integration (CI).
 
 ❌ The following code block is an example of what your code _should not_ look like: 
 ```sql

From 34bb5eec8e64ba0e8925858d331458024999d033 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 10:00:36 -0400
Subject: [PATCH 682/703] adding migration guide link

this pr adds the migration guide link to the 1.6 migration guide.

Closes #3921
---
 .../docs/guides/migration/versions/01-upgrading-to-v1.6.md    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
index ea231ce7f9a..d13f3bf2344 100644
--- a/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
+++ b/website/docs/guides/migration/versions/01-upgrading-to-v1.6.md
@@ -22,13 +22,13 @@ dbt Labs is committed to providing backward compatibility for all versions 1.x,
 ### Behavior changes
 
 - dbt Core v1.6 does not support Python 3.7, which reached End Of Life on June 23. Support Python versions are 3.8, 3.9, 3.10, and 3.11.
-- As part of the Semantic layer re-launch (in beta), the spec for `metrics` has changed significantly. Migration guide coming soon: https://github.com/dbt-labs/docs.getdbt.com/pull/3705
+- As part of the [dbt Semantic layer](/docs/use-dbt-semantic-layer/dbt-sl) re-launch (in beta), the spec for `metrics` has changed significantly. Refer to the [migration guide](/guides/migration/sl-migration) for more info on how to migrate to the re-launched dbt Semantic Layer.
 - The manifest schema version is now v10.
 - dbt Labs is ending support for Homebrew installation of dbt-core and adapters. See [the discussion](https://github.com/dbt-labs/dbt-core/discussions/8277) for more details.
 
 ### For consumers of dbt artifacts (metadata)
 
-The [manifest](/reference/artifacts/manifest-json) schema version has updated to `v10`. Specific changes:
+The [manifest](/reference/artifacts/manifest-json) schema version has been updated to `v10`. Specific changes:
 - Addition of `semantic_models` and changes to `metrics` attributes
 - Addition of `deprecation_date` as a model property
 - Addition of `on_configuration_change` as default node configuration (to support materialized views)

From d7f8042618e97799fcc3868d222f81f593f08dee Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 10:23:01 -0400
Subject: [PATCH 683/703] Update redshift-setup.md

---
 website/docs/docs/core/connect-data-platform/redshift-setup.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/core/connect-data-platform/redshift-setup.md b/website/docs/docs/core/connect-data-platform/redshift-setup.md
index 344e1f51639..175d5f6a715 100644
--- a/website/docs/docs/core/connect-data-platform/redshift-setup.md
+++ b/website/docs/docs/core/connect-data-platform/redshift-setup.md
@@ -85,7 +85,7 @@ Click on one of these authentication methods for further details on how to confi
 
 <TabItem value="database">
 
-The following table contains the parameters for database (password-based) connection method.
+The following table contains the parameters for the database (password-based) connection method.
 
 
 | Profile field | Example | Description |

From 3d087498a3be2255a7280af2445a42d108183f67 Mon Sep 17 00:00:00 2001
From: Isabela Sobral <35778239+belasobral93@users.noreply.github.com>
Date: Tue, 15 Aug 2023 11:43:15 -0500
Subject: [PATCH 684/703] Update deployment-tools.md

adjusting docs link to take you to dbt cloud docs for astronome
---
 website/docs/docs/deploy/deployment-tools.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deployment-tools.md b/website/docs/docs/deploy/deployment-tools.md
index e642e4b95e2..b707d131e6b 100644
--- a/website/docs/docs/deploy/deployment-tools.md
+++ b/website/docs/docs/deploy/deployment-tools.md
@@ -30,7 +30,7 @@ Invoking dbt Core jobs through the [BashOperator](https://registry.astronomer.io
 </TabItem>
 </Tabs>
 
-For more details on both of these methods, including example implementations, check out [this guide](https://www.astronomer.io/guides/airflow-dbt).
+For more details on both of these methods, including example implementations, check out [this guide](https://www.astronomer.io/guides/airflow-dbt](https://docs.astronomer.io/learn/airflow-dbt-cloud).
 
 ## Azure Data Factory
 

From 768ac19f34e96e4bf4f91c4cfe1df32e2bb51f28 Mon Sep 17 00:00:00 2001
From: Isabela Sobral <35778239+belasobral93@users.noreply.github.com>
Date: Tue, 15 Aug 2023 11:45:42 -0500
Subject: [PATCH 685/703] Update deployment-tools.md

updating broken link for airflow's dbt cloud provider
---
 website/docs/docs/deploy/deployment-tools.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deployment-tools.md b/website/docs/docs/deploy/deployment-tools.md
index e642e4b95e2..990474d9c97 100644
--- a/website/docs/docs/deploy/deployment-tools.md
+++ b/website/docs/docs/deploy/deployment-tools.md
@@ -16,7 +16,7 @@ If your organization is using [Airflow](https://airflow.apache.org/), there are
 
 <TabItem value="airflowcloud" label="dbt Cloud">
 
-Installing the [dbt Cloud Provider](https://registry.astronomer.io/providers/dbt-cloud) to orchestrate dbt Cloud jobs. This package contains multiple Hooks, Operators, and Sensors to complete various actions within dbt Cloud.
+Installing the [dbt Cloud Provider](https://registry.astronomer.io/providers/dbt-cloud](https://airflow.apache.org/docs/apache-airflow-providers-dbt-cloud/stable/index.html) to orchestrate dbt Cloud jobs. This package contains multiple Hooks, Operators, and Sensors to complete various actions within dbt Cloud.
 
 <Lightbox src="/img/docs/running-a-dbt-project/airflow_dbt_connector.png" title="Airflow DAG using DbtCloudRunJobOperator"/>
 <Lightbox src="/img/docs/running-a-dbt-project/dbt_cloud_airflow_trigger.png" title="dbt Cloud job triggered by Airflow"/>

From d3c5cab8a3e84459baec2d424bc4fec4b28d5d1f Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 14:19:02 -0400
Subject: [PATCH 686/703] Update website/docs/docs/deploy/deployment-tools.md

---
 website/docs/docs/deploy/deployment-tools.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deployment-tools.md b/website/docs/docs/deploy/deployment-tools.md
index b707d131e6b..d171a86a3a2 100644
--- a/website/docs/docs/deploy/deployment-tools.md
+++ b/website/docs/docs/deploy/deployment-tools.md
@@ -30,7 +30,7 @@ Invoking dbt Core jobs through the [BashOperator](https://registry.astronomer.io
 </TabItem>
 </Tabs>
 
-For more details on both of these methods, including example implementations, check out [this guide](https://www.astronomer.io/guides/airflow-dbt](https://docs.astronomer.io/learn/airflow-dbt-cloud).
+For more details on both of these methods, including example implementations, check out [this guide](https://docs.astronomer.io/learn/airflow-dbt-cloud).
 
 ## Azure Data Factory
 

From fb4ce8cafd287b41e7f2759552c3ab30d2a2126c Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Tue, 15 Aug 2023 15:14:40 -0400
Subject: [PATCH 687/703] Update website/docs/docs/deploy/deployment-tools.md

---
 website/docs/docs/deploy/deployment-tools.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/deploy/deployment-tools.md b/website/docs/docs/deploy/deployment-tools.md
index c41894ce6e9..b9ab14e1c0c 100644
--- a/website/docs/docs/deploy/deployment-tools.md
+++ b/website/docs/docs/deploy/deployment-tools.md
@@ -16,7 +16,7 @@ If your organization is using [Airflow](https://airflow.apache.org/), there are
 
 <TabItem value="airflowcloud" label="dbt Cloud">
 
-Installing the [dbt Cloud Provider](https://registry.astronomer.io/providers/dbt-cloud](https://airflow.apache.org/docs/apache-airflow-providers-dbt-cloud/stable/index.html) to orchestrate dbt Cloud jobs. This package contains multiple Hooks, Operators, and Sensors to complete various actions within dbt Cloud.
+Installing the [dbt Cloud Provider](https://airflow.apache.org/docs/apache-airflow-providers-dbt-cloud/stable/index.html) to orchestrate dbt Cloud jobs. This package contains multiple Hooks, Operators, and Sensors to complete various actions within dbt Cloud.
 
 <Lightbox src="/img/docs/running-a-dbt-project/airflow_dbt_connector.png" title="Airflow DAG using DbtCloudRunJobOperator"/>
 <Lightbox src="/img/docs/running-a-dbt-project/dbt_cloud_airflow_trigger.png" title="dbt Cloud job triggered by Airflow"/>

From 795d743f5741cc8f84e49e283998d644a9c97e76 Mon Sep 17 00:00:00 2001
From: "Eddo W. Hintoso" <eddowh@users.noreply.github.com>
Date: Tue, 15 Aug 2023 12:37:10 -0700
Subject: [PATCH 688/703] Discovery API: update docs (#3894)

## What are you changing in this pull request and why?

I'm updating the docs of Discovery API because we have breaking changes
to the API and we need to update the docs to better reflect the current
state of our API, since the docs point to legacy endpoints we plan to
deprecate soon.

- [x] update use case queries to use bigints and new apis
- [x] update other queries to use bigints and new apis
- [x] docs parity with legacy vs new endpoints
- [x] fix bug where schemas and types weren't retrieved properly
- [x] update or remove screenshots (we're keeping it)
- [ ] verify that Python code works (no bandwidth -- if it breaks it
breaks, not the most urgent for getting docs out)

Some common mistakes I found while going through each query and
executing it to make sure it's valid.

- Fields were `snake_case` when they should be `camelCase`
- Typos in filters and fields
- Nested nodes were not updated so `... on` syntaxes were invalid
- Querying non-nested-allowed fields of a nested node, like `children`,
`tests`, etc.

<!---
Describe your changes and why you're making them. If linked to an open
issue or a pull request on dbt Core, then link to them here!

To learn more about the writing conventions used in the dbt Labs docs,
see the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md).
-->

## Checklist
<!--
Uncomment if you're publishing docs for a prerelease version of dbt
(delete if not applicable):
- [ ] Add versioning components, as described in [Versioning
Docs](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-entire-pages)
- [ ] Add a note to the prerelease version [Migration
Guide](https://github.com/dbt-labs/docs.getdbt.com/tree/current/website/docs/guides/migration/versions)
-->
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

(Below might be a separate PR, but leaving it for now)

Adding new pages (delete if not applicable):
- [x] Add page to `website/sidebars.js`
- [x] Provide a unique filename for the new page

Removing or renaming existing pages (delete if not applicable):
- [ ] Remove page from `website/sidebars.js`
- [ ] Add an entry `website/static/_redirects`
- [ ] [Ran link
testing](https://github.com/dbt-labs/docs.getdbt.com#running-the-cypress-tests-locally)
to update the links that point to the deleted page

---------

Co-authored-by: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Co-authored-by: Ly Nguyen <ly.nguyen@dbtlabs.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 contributing/single-sourcing-content.md       |   44 +-
 .../docs/dbt-cloud-apis/discovery-querying.md |  175 +--
 .../discovery-use-cases-and-examples.md       | 1206 +++++++++--------
 ...nvironment-applied-modelHistoricalRuns.mdx |   50 +
 .../schema-discovery-environment.mdx          |   64 +-
 .../schema-discovery-exposure.mdx             |   19 +-
 .../schema-discovery-exposures.mdx            |   19 +-
 .../schema-discovery-job-exposure.mdx         |   64 +
 .../schema-discovery-job-exposures.mdx        |   65 +
 .../schema-discovery-job-metric.mdx           |   58 +
 .../schema-discovery-job-metrics.mdx          |   60 +
 .../schema-discovery-job-model.mdx            |   91 ++
 .../schema-discovery-job-models.mdx           |   59 +
 .../schema-discovery-job-seed.mdx             |   42 +
 .../schema-discovery-job-seeds.mdx            |   40 +
 .../schema-discovery-job-snapshots.mdx        |   49 +
 .../schema-discovery-job-source.mdx           |   52 +
 .../schema-discovery-job-sources.mdx          |   65 +
 .../schema-discovery-job-test.mdx             |   43 +
 .../schema-discovery-job-tests.mdx            |   43 +
 .../dbt-cloud-apis/schema-discovery-job.mdx   |   62 +
 .../schema-discovery-metric.mdx               |   17 +-
 .../schema-discovery-metrics.mdx              |   19 +-
 .../dbt-cloud-apis/schema-discovery-model.mdx |   26 +-
 .../schema-discovery-modelByEnv.mdx           |   22 +-
 .../schema-discovery-models.mdx               |   24 +-
 .../dbt-cloud-apis/schema-discovery-seed.mdx  |   18 +-
 .../dbt-cloud-apis/schema-discovery-seeds.mdx |   17 +-
 .../schema-discovery-snapshots.mdx            |   20 +-
 .../schema-discovery-source.mdx               |   18 +-
 .../schema-discovery-sources.mdx              |   24 +-
 .../dbt-cloud-apis/schema-discovery-test.mdx  |   17 +-
 .../dbt-cloud-apis/schema-discovery-tests.mdx |   17 +-
 website/docs/docs/dbt-cloud-apis/schema.jsx   |  238 +++-
 website/sidebars.js                           |   48 +-
 .../_discovery_api_job_deprecation_notice.md  |    7 +
 36 files changed, 2043 insertions(+), 859 deletions(-)
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-environment-applied-modelHistoricalRuns.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposure.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposures.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-metric.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-metrics.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-model.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-models.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-seed.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-seeds.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-snapshots.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-source.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-sources.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-test.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job-tests.mdx
 create mode 100644 website/docs/docs/dbt-cloud-apis/schema-discovery-job.mdx
 create mode 100644 website/snippets/_discovery_api_job_deprecation_notice.md

diff --git a/contributing/single-sourcing-content.md b/contributing/single-sourcing-content.md
index ca27372e5bc..5b87d494c94 100644
--- a/contributing/single-sourcing-content.md
+++ b/contributing/single-sourcing-content.md
@@ -15,9 +15,9 @@ Versions are managed in the `versions` array located in the `website/dbt-version
 
 ### Adding a new version
 
-To add a new version to the site, a new object must be added to the `versions` array in the same format as existing versions. This object holds two properties: **version** and **EOLDate (See End of Life Dates below)**. 
+To add a new version to the site, a new object must be added to the `versions` array in the same format as existing versions. This object holds two properties: **version** and **EOLDate (See End of Life Dates below)**.
 
-Example Version: 
+Example Version:
 
 ```jsx
 exports.versions = [
@@ -36,7 +36,7 @@ The **EOLDate** property determines when a version is no longer supported. A ver
 
 When a documentation page is viewed, the **EOLDate** property for the active version is compared to today’s date. If the current version has reached or is nearing the end of support, a banner will show atop the page, notifying the visitor of the end-of-life status.
 
-Two different versions of the banner will show depending on the end-of-life date: 
+Two different versions of the banner will show depending on the end-of-life date:
 
 - When the version is within 3 months of the **EOLDate.**
 - When the version has passed the **EOLDate.**
@@ -76,7 +76,7 @@ exports.versionedPages = [
 
 ## Versioning blocks of content
 
-The **VersionBlock** component provides the ability to version a specific piece of content on a docs page. 
+The **VersionBlock** component provides the ability to version a specific piece of content on a docs page.
 
 This component can be added directly to a markdown file in a similar way as other components (FAQ, File, Lightbox).
 
@@ -99,7 +99,7 @@ Both properties can be used together to set a range where the content should sho
 
 ### Example for versioning entire pages
 
-On the [Docs Defer page](https://docs.getdbt.com/reference/node-selection/defer), tabs are used to show different versions of a piece of code. **v0.21.0 and later** shows `--select`, while **v-.20.x and earlier** changes this to `--models`. 
+On the [Docs Defer page](https://docs.getdbt.com/reference/node-selection/defer), tabs are used to show different versions of a piece of code. **v0.21.0 and later** shows `--select`, while **v-.20.x and earlier** changes this to `--models`.
 
 ![oldway](https://user-images.githubusercontent.com/3880403/163254165-dea23266-2eea-4e65-b3f0-c7b6d3e51fc3.png)
 
@@ -149,7 +149,7 @@ Using a global variable requires two steps:
 exports.dbtVariables = {
   dbtCore: {
     name: "dbt Core"
-  } 
+  }
 }
 ```
 
@@ -198,13 +198,13 @@ In the above example, the **dbtCloud** property has a default name of “dbt Clo
 
 ### Global variables example
 
-The global `<Var />` component can be used inline, for example: 
+The global `<Var />` component can be used inline, for example:
 
 ```markdown
 This piece of markdown content explains why <Var name="dbt" /> is awesome.
 ```
 
-However, a Var component cannot start a new line of content. Fortunately, a workaround exists to use the Var component at the beginning of a line of content. 
+However, a Var component cannot start a new line of content. Fortunately, a workaround exists to use the Var component at the beginning of a line of content.
 
 To use the component at the beginning of a sentence, add a non-breaking space character before the component:
 
@@ -231,7 +231,7 @@ A partial file allows you to reuse content throughout the docs. Here are the ste
 2. Go back to the docs file that will pull content from the partial file.
 3. Add the following import file: `import ComponentName from '/snippets/_this-is-your-partial-file-name.md';`
    * You must always add an import file in that format. Note you can name `ComponentName` (a partial component) can be whatever makes sense for your purpose.
-   * `.md` needs to be added to the end of the filename. 
+   * `.md` needs to be added to the end of the filename.
 4. To use the partial component, go to the next line and add `<ComponentName />`. This fetches the reusable content in the partial file
    * Note `anyname` can be whatever makes sense for your purpose.
 
@@ -258,15 +258,15 @@ Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam fermentum portti
 ```markdown
 Docs content here.
 
-`import SetUpPages from '/snippets/_partial-name.md';`
-
-<SetUpPages />        					
+import SetUpPages from '/snippets/_partial-name.md';
+<!-- It's important to leave a blank line or a comment between import and usage, otherwise it won't work -->
+<SetUpPages />
 
 Docs content here.
 ```
 
 - `import SetUpPages from '/snippets/_partial-name.md';`  &mdash; A partial file that will be imported by other files
-- `<SetUpPages />` &mdash;  A component that imports content from the partial file. You can also use it to pass in data into the partial using props (See 'How to use props to pass different content on multiple pages?' below). 
+- `<SetUpPages />` &mdash;  A component that imports content from the partial file. You can also use it to pass in data into the partial using props (See 'How to use props to pass different content on multiple pages?' below).
 
 4. This will then render the content of the docs in the partial file.
 
@@ -276,32 +276,32 @@ Docs content here.
 
 <details>
 <summary><b>How to use props to pass different content on multiple pages?</b></summary><br />
-	
+
 You can add props on the component only if you want to pass in data from the component into the partial file. This is useful for using the same partial component on
 multiple docs pages and displaying different values for each. For example, if we wanted to use a partial on multiple pages and pass in a different 'feature' for each
 docs page, you can write it as:
 
-```
+```markdown
 import SetUpPages from '/snippets/_available-enterprise-only.md';
-
-`<SetUpPages feature='A really cool feature' /> 
+<!-- It is important to leave a blank line or a comment between import and usage, otherwise it won't work -->
+<SetUpPages feature='A really cool feature' />
 ```
-  
+
 Then in the `/snippets/_available-enterprise-only.md file`, you can display that feature prop with:
-  
+
 >This feature: `{props.feature}` other content etc...
 
 This will then translate to:
-  
+
 >This feature: A really cool feature other content etc...
 
 In this example, the component `<SetUpPages feature='` is passing 'feature' into the partial. This is useful when using dynamic data (for example if you wanted to use the same partial on multiple docs pages, but change the values within the partial for each page)
-  
+
 </details>
 
 ### Snippets
 
-The Snippet component allows for content to be reusable throughout the Docs. This is very similar to the existing FAQ component. Using partial files, which is a built-in Docusaurus feature, is recommended over snippets. 
+The Snippet component allows for content to be reusable throughout the Docs. This is very similar to the existing FAQ component. Using partial files, which is a built-in Docusaurus feature, is recommended over snippets.
 
 Creating and using a snippet requires two steps:
 
diff --git a/website/docs/docs/dbt-cloud-apis/discovery-querying.md b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
index 40836203faa..eaa30c36dfb 100644
--- a/website/docs/docs/dbt-cloud-apis/discovery-querying.md
+++ b/website/docs/docs/dbt-cloud-apis/discovery-querying.md
@@ -1,14 +1,14 @@
 ---
 title: "Query the Discovery API"
 id: "discovery-querying"
-sidebar_label: "Query the Discovery API" 
+sidebar_label: "Query the Discovery API"
 ---
 
-The Discovery API supports ad-hoc queries and integrations.. If you are new to the API, read the [Discovery API overview](/docs/dbt-cloud-apis/discovery-api) for an introduction.
+The Discovery API supports ad-hoc queries and integrations. If you are new to the API, refer to [About the Discovery API](/docs/dbt-cloud-apis/discovery-api) for an introduction.
 
-Use the Discovery API to evaluate data pipeline health and project state across runs or at a moment in time. dbt Labs provide a [GraphQL explorer](https://metadata.cloud.getdbt.com/graphql) for this API, enabling you to run queries and browse the schema. 
+Use the Discovery API to evaluate data pipeline health and project state across runs or at a moment in time. dbt Labs provide a [GraphQL explorer](https://metadata.cloud.getdbt.com/graphql) for this API, enabling you to run queries and browse the schema.
 
-Since GraphQL describes the data in the API, the schema displayed in the GraphQL explorer accurately represents the graph and fields available to query.  
+Since GraphQL describes the data in the API, the schema displayed in the GraphQL explorer accurately represents the graph and fields available to query.
 
 <Snippet path="metadata-api-prerequisites" />
 
@@ -16,17 +16,17 @@ Since GraphQL describes the data in the API, the schema displayed in the GraphQL
 
 Currently, authorization of requests takes place [using a service token](/docs/dbt-cloud-apis/service-tokens). dbt Cloud admin users can generate a Metadata Only service token that is authorized to execute a specific query against the Discovery API.
 
-Once you've created a token, you can use it in the Authorization header of requests to the dbt Cloud Discovery API. Be sure to include the Token prefix in the Authorization header, or the request will fail with a `401 Unauthorized` error. Note that `Bearer` can be used instead of `Token` in the Authorization header. Both syntaxes are equivalent. 
+Once you've created a token, you can use it in the Authorization header of requests to the dbt Cloud Discovery API. Be sure to include the Token prefix in the Authorization header, or the request will fail with a `401 Unauthorized` error. Note that `Bearer` can be used instead of `Token` in the Authorization header. Both syntaxes are equivalent.
 
-## Access the Discovery API 
+## Access the Discovery API
 
 1. Create a [service account token](/docs/dbt-cloud-apis/service-tokens) to authorize requests. dbt Cloud Admin users can generate a _Metadata Only_ service token, which can be used to execute a specific query against the Discovery API to authorize requests.
 
-2. Find your API URL using the endpoint `https://metadata.{YOUR_ACCESS_URL}/graphql`. 
+2. Find your API URL using the endpoint `https://metadata.{YOUR_ACCESS_URL}/graphql`.
 
     * Replace `{YOUR_ACCESS_URL}` with the appropriate [Access URL](/docs/cloud/about-cloud/regions-ip-addresses) for your region and plan. For example, if your multi-tenant region is North America, your endpoint is `https://metadata.cloud.getdbt.com/graphql`. If your multi-tenant region is EMEA, your endpoint is `https://metadata.emea.dbt.com/graphql`.
 
-3. For specific query points, refer to the [schema documentation](/docs/dbt-cloud-apis/discovery-schema-model). 
+3. For specific query points, refer to the [schema documentation](/docs/dbt-cloud-apis/discovery-schema-model).
 
 
 ## Run queries using HTTP requests
@@ -36,7 +36,7 @@ You can run queries by sending a `POST` request to the `https://metadata.YOUR_AC
 * `YOUR_TOKEN` in the Authorization header with your actual API token. Be sure to include the Token prefix.
 * `QUERY_BODY` with a GraphQL query, for example `{ "query": "<query text>" }`
 * `VARIABLES` with a dictionary of your GraphQL query variables, such as a job ID or a filter.
-* `ENDPOINT` with the endpoint you're querying, such as environment. 
+* `ENDPOINT` with the endpoint you're querying, such as environment.
 
   ```shell
   curl 'https://metadata.YOUR_ACCESS_URL/graphql' \
@@ -48,10 +48,13 @@ You can run queries by sending a `POST` request to the `https://metadata.YOUR_AC
 
 Python example:
 
-```py
-response = requests.post('YOUR_ACCESS_URL',
-headers={"authorization": "Bearer "+YOUR_TOKEN, "content-type": "application/json"},
-json={"query": QUERY_BODY, "variables": VARIABLES})
+```python
+response = requests.post(
+    'YOUR_ACCESS_URL',
+    headers={"authorization": "Bearer "+YOUR_TOKEN, "content-type": "application/json"},
+    json={"query": QUERY_BODY, "variables": VARIABLES}
+)
+
 metadata = response.json()['data'][ENDPOINT]
 ```
 
@@ -71,66 +74,74 @@ You can use the Discovery API to query data from the previous three months. For
 
 ## Run queries with the GraphQL explorer
 
-You can run ad-hoc queries directly in the [GraphQL API explorer](https://metadata.cloud.getdbt.com/graphql) and use the document explorer on the left-hand side, where you can see all possible nodes and fields. 
+You can run ad-hoc queries directly in the [GraphQL API explorer](https://metadata.cloud.getdbt.com/graphql) and use the document explorer on the left-hand side to see all possible nodes and fields.
+
+Refer to the [Apollo explorer documentation](https://www.apollographql.com/docs/graphos/explorer/explorer) for setup and authorization info.
+
+1. Access the [GraphQL API explorer](https://metadata.cloud.getdbt.com/graphql) and select fields you want to query.
 
-Refer to the [Apollo explorer documentation](https://www.apollographql.com/docs/graphos/explorer/explorer) for setup and authorization info. 
+2. Select **Variables** at the bottom of the explorer and replace any `null` fields with your unique values.
 
-1. Access the [GraphQL API explorer](https://metadata.cloud.getdbt.com/graphql) and select fields you'd like query. 
+3. [Authenticate](https://www.apollographql.com/docs/graphos/explorer/connecting-authenticating#authentication) using Bearer auth with `YOUR_TOKEN`. Select **Headers** at the bottom of the explorer and select **+New header**.
 
-2. Go to **Variables** at the bottom of the explorer and replace any `null` fields with your unique values.
+4. Select **Authorization** in the **header key** dropdown list and enter your Bearer auth token in the **value** field. Remember to include the Token prefix. Your header key should be in this format: `{"Authorization": "Bearer <YOUR_TOKEN>}`.
 
-3. [Authenticate](https://www.apollographql.com/docs/graphos/explorer/connecting-authenticating#authentication) via Bearer auth with `YOUR_TOKEN`. Go to **Headers** at the bottom of the explorer and select **+New header**.
+<!-- TODO: Screenshot needs to be replaced with new one. If we want to show model historical runs, show `environment { applied { modelHistoricalRuns } }` -->
+<!-- However we can choose to leave this be, since the important info from the screenshot is to show how the GraphQL API canbe used -- the content (request and response) doesn't matter too much` -->
 
-4. Select **Authorization** in the **header key** drop-down list and enter your Bearer auth token in the **value** field. Remember to include the Token prefix. Your header key should look like this `{"Authorization": "Bearer <YOUR_TOKEN>}`.
 <br />
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/graphql_header.jpg" width="85%" title="Enter the header key and Bearer auth token values"/>
 
-5. Run your query by pressing the blue query button in the top-right of the Operation editor (to the right of the query). You should see a successful query response on the right side of the explorer.
+1. Run your query by clicking the blue query button in the top right of the **Operation** editor (to the right of the query). You should see a successful query response on the right side of the explorer.
+
+<!-- TODO: Screenshot needs to be replaced with new one. If we want to show model historical runs, show `environment { applied { modelHistoricalRuns } }` -->
+<!-- However we can choose to leave this be, since the important info from the screenshot is to show how the GraphQL API canbe used -- the content (request and response) doesn't matter too much` -->
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/graphql.jpg" width="85%" title="Run queries using the Apollo Server GraphQL explorer"/>
 
 ### Fragments
 
-Use the [`..on`](https://www.apollographql.com/docs/react/data/fragments/) notation to query across lineage and retrieve results from specific node types.
+Use the [`... on`](https://www.apollographql.com/docs/react/data/fragments/) notation to query across lineage and retrieve results from specific node types.
 
 ```graphql
-
-environment(id: $environmentId) {
-  applied {
-    models(first: $first,filter:{uniqueIds:"MODEL.PROJECT.MODEL_NAME"}) {
-      edges {
-        node {
-          name
-          ancestors(types:[Model, Source, Seed, Snapshot]) {
-            ... on ModelAppliedStateNode {
-              name
-              resourceType
-              materializedType
-              executionInfo {
-                executeCompletedAt
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first, filter: { uniqueIds: "MODEL.PROJECT.MODEL_NAME" }) {
+        edges {
+          node {
+            name
+            ancestors(types: [Model, Source, Seed, Snapshot]) {
+              ... on ModelAppliedStateNestedNode {
+                name
+                resourceType
+                materializedType
+                executionInfo {
+                  executeCompletedAt
+                }
               }
-            }
-            ... on SourceAppliedStateNode {
-              sourceName
-							name
-              resourceType
-              freshness {
-                maxLoadedAt
+              ... on SourceAppliedStateNestedNode {
+                sourceName
+                name
+                resourceType
+                freshness {
+                  maxLoadedAt
+                }
               }
-            }
-            ... on SnapshotAppliedStateNode {
-              name
-              resourceType
-              executionInfo {                  
-                executeCompletedAt
+              ... on SnapshotAppliedStateNestedNode {
+                name
+                resourceType
+                executionInfo {
+                  executeCompletedAt
+                }
               }
-            }
-            ... on SeedAppliedStateNode {
-              name
-              resourceType                
-              executionInfo {
-                executeCompletedAt
+              ... on SeedAppliedStateNestedNode {
+                name
+                resourceType
+                executionInfo {
+                  executeCompletedAt
+                }
               }
             }
           }
@@ -139,56 +150,59 @@ environment(id: $environmentId) {
     }
   }
 }
-
 ```
 
 ### Pagination
 
-Querying large datasets can impact performance on multiple functions in the API pipeline. Pagination eases the burden by returning smaller data sets one page at a time. This is useful for returning a particular portion of the dataset or the entire dataset piece-by-piece to enhance performance. dbt Cloud utilizes cursor-based pagination, which makes it easy to return pages of constantly changing data. 
+Querying large datasets can impact performance on multiple functions in the API pipeline. Pagination eases the burden by returning smaller data sets one page at a time. This is useful for returning a particular portion of the dataset or the entire dataset piece-by-piece to enhance performance. dbt Cloud utilizes cursor-based pagination, which makes it easy to return pages of constantly changing data.
 
-Use the `PageInfo` object to return information about the page. The following fields are available:
+Use the `PageInfo` object to return information about the page. The available fields are:
 
-- `startCursor` string type - corresponds to the first `node` in the `edge`.
-- `endCursor` string type - corresponds to the last `node` in the `edge`.
-- `hasNextPage` boolean type - whether there are more `nodes` after the returned results.
-- `hasPreviousPage` boolean type - whether `nodes` exist before the returned results. 
+- `startCursor` string type &mdash; Corresponds to the first `node` in the `edge`.
+- `endCursor` string type &mdash; Corresponds to the last `node` in the `edge`.
+- `hasNextPage` boolean type &mdash; Whether or not there are more `nodes` after the returned results.
 
 There are connection variables available when making the query:
 
-- `first` integer type - will return the first 'n' `nodes` for each page, up to 500.
-- `after` string type sets the cursor to retrieve `nodes` after. It's best practice to set the `after` variable with the object ID defined in the `endcursor` of the previous page. 
+- `first` integer type &mdash; Returns the first n `nodes` for each page, up to 500.
+- `after` string type &mdash; Sets the cursor to retrieve `nodes` after. It's best practice to set the `after` variable with the object ID defined in the `endCursor` of the previous page.
+
+Below is an example that returns the `first` 500 models `after` the specified Object ID in the variables. The `PageInfo` object returns where the object ID where the cursor starts, where it ends, and whether there is a next page.
 
-The following example shows that we're returning the `first` 500 models `after` the specified Object ID in the variables. The `PageInfo` object will return where the object ID where the cursor starts, where it ends, and whether there is a next page. 
+<!-- TODO: Update screenshot to use `$environmentId: BigInt!, or remove it` -->
+<!-- However we can choose to leave this be, since the important info from the screenshot is to show how the GraphQL API canbe used -- the content (request and response) doesn't matter too much` -->
 
 <Lightbox src="/img/paginate.png" width="75%" title="Example of pagination"/>
 
-Here is a code example of the `PageInfo` object:
+Below is a code example of the `PageInfo` object:
 
 ```graphql
 pageInfo {
-          startCursor
-          endCursor
-          hasNextPage
-        }
-        totalCount # Total number of pages
-
+  startCursor
+  endCursor
+  hasNextPage
+}
+totalCount # Total number of records across all pages
 ```
 
 ### Filters
 
-Filtering helps to narrow down the results of an API query. Want to query and return only models and tests that are failing? Or find models that are taking too long to run? You can fetch execution details such as [`executionTime`](/docs/dbt-cloud-apis/discovery-schema-models#fields), [`runElapsedTime`](/docs/dbt-cloud-apis/discovery-schema-models#fields), or [`status`](/docs/dbt-cloud-apis/discovery-schema-models#fields). This helps data teams monitor the performance of their models, identify bottlenecks, and optimize the overall data pipeline.
+Filtering helps to narrow down the results of an API query. If you want to query and return only models and tests that are failing or find models that are taking too long to run, you can fetch execution details such as [`executionTime`](/docs/dbt-cloud-apis/discovery-schema-models#fields), [`runElapsedTime`](/docs/dbt-cloud-apis/discovery-schema-models#fields), or [`status`](/docs/dbt-cloud-apis/discovery-schema-models#fields). This helps data teams monitor the performance of their models, identify bottlenecks, and optimize the overall data pipeline.
 
-In the following example, we can see that we're filtering results to models that have succeeded on their `lastRunStatus`:
+Below is an example that filters for results of models that have succeeded on their `lastRunStatus`:
 
 <Lightbox src="/img/filtering.png" width="75%" title="Example of filtering"/>
 
-Here is a code example that filters for models that have an error on their last run and tests that have failed:
+Below is an example that filters for models that have an error on their last run and tests that have failed:
 
-```graphql
+<!-- TODO: Update screenshot to use `$environmentId: BigInt!, or remove it` -->
+<!-- However we can choose to leave this be, since the important info from the screenshot is to show how the GraphQL API canbe used -- the content (request and response) doesn't matter too much` -->
 
-environment(id: $environmentId) {
+```graphql
+query ModelsAndTests($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
     applied {
-      models(first: $first, filter: {lastRunStatus:error}) {
+      models(first: $first, filter: { lastRunStatus: error }) {
         edges {
           node {
             name
@@ -198,7 +212,7 @@ environment(id: $environmentId) {
           }
         }
       }
-      tests(first: $first, filter: {status:"fail"}) {
+      tests(first: $first, filter: { status: "fail" }) {
         edges {
           node {
             name
@@ -207,9 +221,10 @@ environment(id: $environmentId) {
             }
           }
         }
-		}
+      }
+    }
+  }
 }
-
 ```
 
 ## Related content
diff --git a/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md b/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md
index 030688d9aeb..8efb1ec0d37 100644
--- a/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md
+++ b/website/docs/docs/dbt-cloud-apis/discovery-use-cases-and-examples.md
@@ -3,9 +3,9 @@ title: "Use cases and examples for the Discovery API"
 sidebar_label: "Uses and examples"
 ---
 
-With the Discovery API, you can query the metadata in dbt Cloud to learn more about your dbt deployments and the data it generates to analyze them and make improvements. 
+With the Discovery API, you can query the metadata in dbt Cloud to learn more about your dbt deployments and the data it generates to analyze them and make improvements.
 
-You can use the API in a variety of ways to get answers to your business questions. Below describes some of the uses of the API and is meant to give you an idea of the questions this API can help you answer.  
+You can use the API in a variety of ways to get answers to your business questions. Below describes some of the uses of the API and is meant to give you an idea of the questions this API can help you answer.
 
 | Use Case | Outcome | Example Questions |
 | --- | --- | --- |
@@ -17,13 +17,13 @@ You can use the API in a variety of ways to get answers to your business questio
 
 ## Performance
 
-You can use the Discovery API to identify inefficiencies in pipeline execution to reduce infrastructure costs and improve timeliness. Below are example questions and queries you can run. 
+You can use the Discovery API to identify inefficiencies in pipeline execution to reduce infrastructure costs and improve timeliness. Below are example questions and queries you can run.
 
 For performance use cases, people typically query the historical or latest applied state across any part of the DAG (for example, models) using the `environment`, `modelByEnvironment`, or job-level endpoints.
 
 ### How long did each model take to run?
 
-It’s helpful to understand how long it takes to build models (tables) and tests to execute during a dbt run. Longer model build times result in higher infrastructure costs and fresh data arriving later to stakeholders. Analyses like these can be in observability tools or ad-hoc queries, like in a notebook. 
+It’s helpful to understand how long it takes to build models (tables) and tests to execute during a dbt run. Longer model build times result in higher infrastructure costs and fresh data arriving later to stakeholders. Analyses like these can be in observability tools or ad-hoc queries, like in a notebook.
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/model-timing.jpg" width="200%" title="Model timing visualization in dbt Cloud"/>
 
@@ -35,33 +35,42 @@ Data teams can monitor the performance of their models, identify bottlenecks, an
 1. Use latest state environment-level API to get a list of all executed models and their execution time. Then, sort the models by `executionTime` in descending order.
 
 ```graphql
-query Query($environmentId: Int!, $first: Int!){
-    environment(id: $environmentId) {
-        applied {
-            models(first: $first) {
-                edges {
-                    node {
-                        name
-                        uniqueId
-                        materializedType
-                        executionInfo {
-                            lastSuccessRunId
-                            executionTime
-                            executeStartedAt
-                        }
-                    }
-                }
+query AppliedModels($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first) {
+        edges {
+          node {
+            name
+            uniqueId
+            materializedType
+            executionInfo {
+              lastSuccessRunId
+              executionTime
+              executeStartedAt
             }
+          }
         }
+      }
     }
+  }
 }
 ```
 
-2. Get the most recent 20 run results for the longest running model. Review the results of the model across runs, or you can go to the job/run or commit itself to investigate further. 
+2. Get the most recent 20 run results for the longest running model. Review the results of the model across runs or you can go to the job/run or commit itself to investigate further.
 
 ```graphql
-query($environmentId: Int!, $uniqueId: String!, $lastRunCount: Int!) {
-    modelByEnvironment(environmentId: $environmentId, uniqueId: $uniqueId, lastRunCount: $lastRunCount) {
+query ModelHistoricalRuns(
+  $environmentId: BigInt!
+  $uniqueId: String
+  $lastRunCount: Int
+) {
+  environment(id: $environmentId) {
+    applied {
+      modelHistoricalRuns(
+        uniqueId: $uniqueId
+        lastRunCount: $lastRunCount
+      ) {
         name
         runId
         runElapsedTime
@@ -70,12 +79,15 @@ query($environmentId: Int!, $uniqueId: String!, $lastRunCount: Int!) {
         executeStartedAt
         executeCompletedAt
         status
+      }
     }
+  }
 }
 ```
 
 3. Use the query results to plot a graph of the longest running model’s historical run time and execution time trends.
 
+<!-- TODO: TEST THIS PYTHON CODE WORKS WITH NEW API AND DOCS! -->
 ```python
 # Import libraries
 import os
@@ -88,11 +100,11 @@ auth_token = *[SERVICE_TOKEN_HERE]*
 
 # Query the API
 def query_discovery_api(auth_token, gql_query, variables):
-    response = requests.post('https://metadata.cloud.getdbt.com/graphql', 
+    response = requests.post('https://metadata.cloud.getdbt.com/graphql',
         headers={"authorization": "Bearer "+auth_token, "content-type": "application/json"},
         json={"query": gql_query, "variables": variables})
     data = response.json()['data']
-    
+
     return data
 
 # Get the latest run metadata for all models
@@ -120,7 +132,7 @@ variables_query_two = {
 }
 
 # Get the historical run metadata for the longest running model
-model_historical_metadata = query_discovery_api(auth_token, query_two, variables_query_two)['modelByEnvironment']
+model_historical_metadata = query_discovery_api(auth_token, query_two, variables_query_two)['environment']['applied']['modelHistoricalRuns']
 
 # Convert to dataframe
 model_df = pd.DataFrame(model_historical_metadata)
@@ -143,7 +155,8 @@ plt.plot(model_df['executeStartedAt'], model_df['executionTime'])
 plt.title(model_df['name'].iloc[0]+" Execution Time")
 plt.show()
 ```
-Plotting examples: 
+
+Plotting examples:
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/plot-of-runelapsedtime.png" width="80%" title="The plot of runElapsedTime over time"/>
 
@@ -152,70 +165,91 @@ Plotting examples:
 
 </details>
 
-### What’s the latest state of each model? 
+### What’s the latest state of each model?
 
 The Discovery API provides information about the applied state of models and how they arrived in that state. You can retrieve the status information from the most recent run and most recent successful run (execution) from the `environment` endpoint and dive into historical runs using job-based and `modelByEnvironment` endpoints.
 
 <details>
 <summary>Example query</summary>
 
-The API returns full identifier information (`database.schema.alias`) and the `executionInfo` for both the most recent run and most recent successful run from the database: 
-
-
-  ```graphql
-	query($environmentId: Int!, $first: Int!){
-	  environment(id: $environmentId) {
-	    applied {
-	     models(first: $first) {
-	       edges {
-	          node {
-	            uniqueId
-	            compiledCode
-	            database
-	            schema
-	            alias
-	            materializedType
-	            executionInfo {
-	              executeCompletedAt
-	              lastJobDefinitionId
-	              lastRunGeneratedAt
-	              lastRunId
-	              lastRunStatus
-	              lastRunError
-	              lastSuccessJobDefinitionId
-	              runGeneratedAt
-	              lastSuccessRunId
-	            }
-	         }
-	       }
-	     }
-	    }
-	  }
-	}
-  ```
+The API returns full identifier information (`database.schema.alias`) and the `executionInfo` for both the most recent run and most recent successful run from the database:
+
+```graphql
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first) {
+        edges {
+          node {
+            uniqueId
+            compiledCode
+            database
+            schema
+            alias
+            materializedType
+            executionInfo {
+              executeCompletedAt
+              lastJobDefinitionId
+              lastRunGeneratedAt
+              lastRunId
+              lastRunStatus
+              lastRunError
+              lastSuccessJobDefinitionId
+              runGeneratedAt
+              lastSuccessRunId
+            }
+          }
+        }
+      }
+    }
+  }
+}
+```
 
 </details>
 
 ### What happened with my job run?
 
-You can query the metadata at the job level to review results for specific runs. This is helpful for historical analysis of deployment performance or optimizing particular jobs. 
+You can query the metadata at the job level to review results for specific runs. This is helpful for historical analysis of deployment performance or optimizing particular jobs.
+
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
 <details>
 <summary>Example query</summary>
 
+Deprecated example:
 ```graphql
-query($jobId: Int!, $runId: Int!){
-	models(jobId: $jobId, runId: $runId) {
-	  name
-	  status
-	  tests {
-	    name
-	    status
-	  }
-	}
+query ($jobId: Int!, $runId: Int!) {
+  models(jobId: $jobId, runId: $runId) {
+    name
+    status
+    tests {
+      name
+      status
+    }
+  }
+}
+```
+
+New example:
+
+```graphql
+query ($jobId: BigInt!, $runId: BigInt!) {
+  job(id: $jobId, runId: $runId) {
+    models {
+      name
+      status
+      tests {
+        name
+        status
+      }
+    }
+  }
 }
 ```
-    
+
 </details>
 
 ### What’s changed since the last run?
@@ -228,41 +262,47 @@ With the API, you can compare the `rawCode` between the definition and applied s
 
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    models(first: $first, filter: {uniqueIds:"MODEL.PROJECT.MODEL_NAME"}) {
-	      edges {
-	        node {
-	          rawCode
-	          ancestors(types: [Source]){
-	            ...on SourceAppliedStateNode {
-	              freshness {
-	                maxLoadedAt
-	              }
-	            }
-	          }
-	          executionInfo {
-	            runGeneratedAt
-	            executeCompletedAt
-	          }
-	          materializedType
-	        }
-	      }
-	    }
-	  }
-	  definition {
-	    models(first: $first, filter: {uniqueIds:"MODEL.PROJECT.MODEL_NAME"}) {
-	      edges {
-	        node {
-	          rawCode
-	          runGeneratedAt
-	          materializedType
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(
+        first: $first
+        filter: { uniqueIds: "MODEL.PROJECT.MODEL_NAME" }
+      ) {
+        edges {
+          node {
+            rawCode
+            ancestors(types: [Source]) {
+              ... on SourceAppliedStateNestedNode {
+                freshness {
+                  maxLoadedAt
+                }
+              }
+            }
+            executionInfo {
+              runGeneratedAt
+              executeCompletedAt
+            }
+            materializedType
+          }
+        }
+      }
+    }
+    definition {
+      models(
+        first: $first
+        filter: { uniqueIds: "MODEL.PROJECT.MODEL_NAME" }
+      ) {
+        edges {
+          node {
+            rawCode
+            runGeneratedAt
+            materializedType
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
@@ -270,45 +310,46 @@ query($environmentId: Int!, $first: Int!){
 
 ## Quality
 
-You can use the Discovery API to monitor data source freshness and test results to diagnose and resolve issues and drive trust in data. When used with [webhooks](/docs/deploy/webhooks), can also help with detecting, investigating, and alerting issues. Below lists example questions the API can help you answer. Below are example questions and queries you can run. 
+You can use the Discovery API to monitor data source freshness and test results to diagnose and resolve issues and drive trust in data. When used with [webhooks](/docs/deploy/webhooks), can also help with detecting, investigating, and alerting issues. Below lists example questions the API can help you answer. Below are example questions and queries you can run.
 
-For quality use cases, people typically query the historical or latest applied state, often in the upstream part of the DAG (for example, sources), using the `environment` or `modelByEnvironment` endpoints.
+For quality use cases, people typically query the historical or latest applied state, often in the upstream part of the DAG (for example, sources), using the `environment` or `environment { applied { modelHistoricalRuns } }` endpoints.
 
 ### Which models and tests failed to run?
+
 By filtering on the latest status, you can get lists of models that failed to build and tests that failed during their most recent execution. This is helpful when diagnosing issues with the deployment that result in delayed or incorrect data.
 
 <details>
 <summary>Example query with code</summary>
 
-1. Get the latest run results across all jobs in the environment and return only the models and tests that errored/failed. 
+1. Get the latest run results across all jobs in the environment and return only the models and tests that errored/failed.
 
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-		applied {
-			models(first: $first, filter: {lastRunStatus:error}) {
-				edges {
-					node {
-						name
-						executionInfo {
-							lastRunId
-						}
-					}
-				}
-			}
-			tests(first: $first, filter: {status:"fail"}) {
-				edges {
-					node {
-						name
-						executionInfo {
-							lastRunId
-						}
-					}
-				}
-			}
-		}
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first, filter: { lastRunStatus: error }) {
+        edges {
+          node {
+            name
+            executionInfo {
+              lastRunId
+            }
+          }
+        }
+      }
+      tests(first: $first, filter: { status: "fail" }) {
+        edges {
+          node {
+            name
+            executionInfo {
+              lastRunId
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
@@ -316,14 +357,18 @@ query($environmentId: Int!, $first: Int!){
 
 
 ```graphql
-query($environmentId: Int!, $uniqueId: String!, $lastRunCount: Int) {
-  modelByEnvironment(environmentId: $environmentId, uniqueId: $uniqueId, lastRunCount: $lastRunCount) {
-    name
-    executeStartedAt
-    status
-    tests {
-      name
-      status
+query ($environmentId: BigInt!, $uniqueId: String!, $lastRunCount: Int) {
+  environment(id: $environmentId) {
+    applied {
+      modelHistoricalRuns(uniqueId: $uniqueId, lastRunCount: $lastRunCount) {
+        name
+        executeStartedAt
+        status
+        tests {
+          name
+          status
+        }
+      }
     }
   }
 }
@@ -337,63 +382,67 @@ query($environmentId: Int!, $uniqueId: String!, $lastRunCount: Int) {
 
 ### When was the data my model uses last refreshed?
 
-You can get the metadata on the latest execution for a particular model or across all models in your project. For instance, investigate when each model or snapshot that's feeding into a given model was last executed or the source or seed was last loaded to gauge the _freshness_ of the data. 
+You can get the metadata on the latest execution for a particular model or across all models in your project. For instance, investigate when each model or snapshot that's feeding into a given model was last executed or the source or seed was last loaded to gauge the _freshness_ of the data.
 
 <details>
 <summary>Example query with code</summary>
 
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    models(first: $first,filter:{uniqueIds:"MODEL.PROJECT.MODEL_NAME"}) {
-	      edges {
-	        node {
-	          name
-	          ancestors(types:[Model, Source, Seed, Snapshot]) {
-	            ... on ModelAppliedStateNode {
-	              name
-	              resourceType
-	              materializedType
-	              executionInfo {
-	                executeCompletedAt
-	              }
-	            }
-	            ... on SourceAppliedStateNode {
-	              sourceName
-								name
-	              resourceType
-	              freshness {
-	                maxLoadedAt
-	              }
-	            }
-	            ... on SnapshotAppliedStateNode {
-	              name
-	              resourceType
-	              executionInfo {                  
-	                executeCompletedAt
-	              }
-	            }
-	            ... on SeedAppliedStateNode {
-	              name
-	              resourceType                
-	              executionInfo {
-	                executeCompletedAt
-	              }
-	            }
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(
+        first: $first
+        filter: { uniqueIds: "MODEL.PROJECT.MODEL_NAME" }
+      ) {
+        edges {
+          node {
+            name
+            ancestors(types: [Model, Source, Seed, Snapshot]) {
+              ... on ModelAppliedStateNestedNode {
+                name
+                resourceType
+                materializedType
+                executionInfo {
+                  executeCompletedAt
+                }
+              }
+              ... on SourceAppliedStateNestedNode {
+                sourceName
+                name
+                resourceType
+                freshness {
+                  maxLoadedAt
+                }
+              }
+              ... on SnapshotAppliedStateNestedNode {
+                name
+                resourceType
+                executionInfo {
+                  executeCompletedAt
+                }
+              }
+              ... on SeedAppliedStateNestedNode {
+                name
+                resourceType
+                executionInfo {
+                  executeCompletedAt
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
+<!-- TODO: TEST THIS PYTHON CODE WORKS WITH NEW API AND DOCS! -->
 ```python
 # Extract graph nodes from response
-def extract_nodes(data): 
+def extract_nodes(data):
     models = []
     sources = []
     groups = []
@@ -422,9 +471,9 @@ def create_freshness_graph(models_df, sources_df):
         if model["executionInfo"]["executeCompletedAt"] is not None:
           model_freshness = current_time - pd.Timestamp(model["executionInfo"]["executeCompletedAt"])
           for ancestor in model["ancestors"]:
-              if ancestor["resourceType"] == "SourceAppliedStateNode":
+              if ancestor["resourceType"] == "SourceAppliedStateNestedNode":
                   ancestor_freshness = current_time - pd.Timestamp(ancestor["freshness"]['maxLoadedAt'])
-              elif ancestor["resourceType"] == "ModelAppliedStateNode":
+              elif ancestor["resourceType"] == "ModelAppliedStateNestedNode":
                   ancestor_freshness = current_time - pd.Timestamp(ancestor["executionInfo"]["executeCompletedAt"])
 
               if ancestor_freshness > max_freshness:
@@ -437,11 +486,11 @@ def create_freshness_graph(models_df, sources_df):
     for _, model in models_df.iterrows():
         for parent in model["parents"]:
             G.add_edge(parent["uniqueId"], model["uniqueId"])
-    
+
     return G
 ```
 
-Graph example: 
+Graph example:
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/lineage-graph-with-freshness-info.png" width="75%" title="A lineage graph with source freshness information"/>
 
@@ -450,7 +499,7 @@ Graph example:
 
 ### Are my data sources fresh?
 
-Checking [source freshness](/docs/build/sources#snapshotting-source-data-freshness) allows you to ensure that sources loaded and used in your dbt project are compliant with expectations. The API provides the latest metadata about source loading and information about the freshness check criteria. 
+Checking [source freshness](/docs/build/sources#snapshotting-source-data-freshness) allows you to ensure that sources loaded and used in your dbt project are compliant with expectations. The API provides the latest metadata about source loading and information about the freshness check criteria.
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/source-freshness-page.png" width="75%" title="Source freshness page in dbt Cloud"/>
 
@@ -458,47 +507,49 @@ Checking [source freshness](/docs/build/sources#snapshotting-source-data-freshne
 <summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    sources(first: $first, filters:{freshnessChecked:true, database:"production"}) {
-	      edges {
-	        node {
-	          sourceName
-	          name
-	          identifier
-	          loader
-	          freshness {
-	            freshnessJobDefinitionId
-	            freshnessRunId
-	            freshnessRunGeneratedAt
-	            freshnessStatus
-	            freshnessChecked
-	            maxLoadedAt
-	            maxLoadedAtTimeAgoInS
-	            snapshottedAt
-	            criteria {
-	              errorAfter {
-	                count
-	                period
-	              }
-	              warnAfter {
-	                count
-	                period
-	              }
-	            }
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      sources(
+        first: $first
+        filter: { freshnessChecked: true, database: "production" }
+      ) {
+        edges {
+          node {
+            sourceName
+            name
+            identifier
+            loader
+            freshness {
+              freshnessJobDefinitionId
+              freshnessRunId
+              freshnessRunGeneratedAt
+              freshnessStatus
+              freshnessChecked
+              maxLoadedAt
+              maxLoadedAtTimeAgoInS
+              snapshottedAt
+              criteria {
+                errorAfter {
+                  count
+                  period
+                }
+                warnAfter {
+                  count
+                  period
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
 </details>
 
-
 ### What’s the test coverage and status?
 
 [Tests](https://docs.getdbt.com/docs/build/tests) are an important way to ensure that your stakeholders are reviewing high-quality data. You can execute tests during a dbt Cloud run. The Discovery API provides complete test results for a given environment or job, which it represents as the `children` of a given node that’s been tested (for example, a `model`).
@@ -506,32 +557,32 @@ query($environmentId: Int!, $first: Int!){
 <details>
 <summary>Example query</summary>
 
-For the following example, the `parents` are the nodes (code) that's being tested and `executionInfo` describes the latest test results: 
+For the following example, the `parents` are the nodes (code) that's being tested and `executionInfo` describes the latest test results:
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    tests(first: $first) {
-	      edges {
-	        node {
-	          name
-	          columnName
-	          parents {
-	            name
-	            resourceType
-	          }
-	          executionInfo {
-	            lastRunStatus
-	            lastRunError
-	            executeCompletedAt
-	            executionTime
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      tests(first: $first) {
+        edges {
+          node {
+            name
+            columnName
+            parents {
+              name
+              resourceType
+            }
+            executionInfo {
+              lastRunStatus
+              lastRunError
+              executeCompletedAt
+              executionTime
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
@@ -541,44 +592,41 @@ query($environmentId: Int!, $first: Int!){
 
 ### How is this model contracted and versioned?
 
-To enforce the shape of a model's definition, you can define contracts on models and their columns. You can also specify model versions to keep track of discrete stages in its evolution and use the appropriate one. 
+To enforce the shape of a model's definition, you can define contracts on models and their columns. You can also specify model versions to keep track of discrete stages in its evolution and use the appropriate one.
+
+<!-- TODO: The description above is not accurate for the desired query below because only applied models can query catalogs, so the query is changed to `environment.applied`. We need to change the description to refer to the applied state, or do not query `catalog` from the definition state node. -->
 
 <details>
 <summary>Example query</summary>
 
 
 ```graphql
-query{
-	environment(id:123) {
-		definition {
-			models(first:100, filter:{access:public}) {
-				edges {
-					nodes {
-						name
-						latest_version
-						contract_enforced
-						constraints{
-							name
-							type
-							expression
-							columns
-						}
-						catalog {
-							columns {
-								name
-								type
-								constraints {
-									name
-									type
-									expression
-								}
-							}
-						}
-					}
-				}
-			}
-		}
-	}
+query {
+  environment(id: 123) {
+    applied {
+      models(first: 100, filter: { access: public }) {
+        edges {
+          node {
+            name
+            latestVersion
+            contractEnforced
+            constraints {
+              name
+              type
+              expression
+              columns
+            }
+            catalog {
+              columns {
+                name
+                type
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
@@ -594,42 +642,50 @@ For discovery use cases, people typically query the latest applied or definition
 
 ### What does this dataset and its columns mean?
 
-Query the Discovery API to map a table/view in the data platform to the model in the dbt project; then, retrieve metadata about its meaning, including descriptive metadata from its YAML file and catalog information from its YAML file and the schema. 
-
+Query the Discovery API to map a table/view in the data platform to the model in the dbt project; then, retrieve metadata about its meaning, including descriptive metadata from its YAML file and catalog information from its YAML file and the schema.
 
 <details>
 <summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    models(first: $first, filter: {database:"analytics", schema:"prod", identifier:"customers"}) {
-	      edges {
-	        node {
-	          name
-	          description
-	          tags
-	          meta
-	          catalog {
-	            columns {
-	              name
-	              description
-	              type
-	            }
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(
+        first: $first
+        filter: {
+          database: "analytics"
+          schema: "prod"
+          identifier: "customers"
+        }
+      ) {
+        edges {
+          node {
+            name
+            description
+            tags
+            meta
+            catalog {
+              columns {
+                name
+                description
+                type
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 </details>
 
+<!-- TODO: Revise this section to use the `environment.definition.lineage` endpoints instead of querying all nodes
+
 ### What’s the full data lineage?
 
-Lineage, enabled by the `ref` function, is at the core of dbt. Understanding lineage provides many benefits, such as understanding the structure and relationships of datasets (and metrics) and performing impact-and-root-cause analyses to resolve or present issues given changes to definitions or source data. With the Discovery API, you can construct lineage using the `parents` nodes or its `children` and query the entire upstream lineage using `ancestors`. 
+Lineage, enabled by the `ref` function, is at the core of dbt. Understanding lineage provides many benefits, such as understanding the structure and relationships of datasets (and metrics) and performing impact-and-root-cause analyses to resolve or present issues given changes to definitions or source data. With the Discovery API, you can construct lineage using the `parents` nodes or its `children` and query the entire upstream lineage using `ancestors`.
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/example-dag.png" width="80%" title="Example of a DAG"/>
 
@@ -639,120 +695,102 @@ Lineage, enabled by the `ref` function, is at the core of dbt. Understanding lin
 1. Query all project nodes
 
 ```graphql
-query Lineage($environmentId: Int!, $first: Int!) {
-	environment(id: $environmentId) {
-	  definition {
-	    sources(first: $first) {
-	      edges {
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          children{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	    seeds(first: $first) {
-	      edges {
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          children {
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	    snapshots(first: $first) {
-	      edges {
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          parents {
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	          children {
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	    models(first:$first){
-	      edges {
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          parents{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	          children{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	    metrics(first: $first){
-	      edges{
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          parents{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	          children{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	    exposures(first: $first){
-	      edges{
-	        node {
-	          uniqueId
-	          name
-	          resourceType
-	          parents{
-	            uniqueId
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query Lineage($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    definition {
+      sources(first: $first) {
+        edges {
+          node {
+            uniqueId
+            name
+            resourceType
+            children {
+              uniqueId
+              name
+              resourceType
+            }
+          }
+        }
+      }
+      seeds(first: $first) {
+        edges {
+          node {
+            uniqueId
+            name
+            resourceType
+            children {
+              uniqueId
+              name
+              resourceType
+            }
+          }
+        }
+      }
+      snapshots(first: $first) {
+        edges {
+          node {
+            uniqueId
+            name
+            resourceType
+            parents {
+              uniqueId
+              name
+              resourceType
+            }
+            children {
+              uniqueId
+              name
+              resourceType
+            }
+          }
+        }
+      }
+      models(first: $first) {
+        edges {
+          node {
+            uniqueId
+            name
+            resourceType
+            parents {
+              uniqueId
+              name
+              resourceType
+            }
+            children {
+              uniqueId
+              name
+              resourceType
+            }
+          }
+        }
+      }
+      exposures(first: $first) {
+        edges {
+          node {
+            uniqueId
+            name
+            resourceType
+            parents {
+              uniqueId
+              name
+              resourceType
+            }
+          }
+        }
+      }
+      # metrics and semanticModels coming soon...
+    }
+  }
 }
 ```
 
-Then, extract the node definitions and create a lineage graph. You can traverse downstream from sources and seeds (adding an edge from each node with children to its children) or iterate through each node’s parents (if it has them). Keep in mind that models, snapshots, and metrics can have parents and children, whereas sources and seeds have only children and exposures only have parents. 
+Then, extract the node definitions and create a lineage graph. You can traverse downstream from sources and seeds (adding an edge from each node with children to its children) or iterate through each node’s parents (if it has them). Remember that models, snapshots, and metrics can have parents and children, whereas sources and seeds have only children and exposures only have parents.
 
 
 2. Extract the node definitions, construct a lineage graph, and plot the graph.
 
-
 ```python
+# TODO: TEST THIS PYTHON CODE WORKS WITH NEW API AND DOCS!
 import networkx as nx
 import os
 import matplotlib.pyplot as plt
@@ -762,7 +800,7 @@ from collections import defaultdict
 
 # Write Discovery API query
 gql_query = """
-query Definition($environmentId: Int!, $first: Int!) {
+query Definition($environmentId: BigInt!, $first: Int!) {
 *[ADD QUERY HERE]*
 }
 
@@ -777,18 +815,18 @@ variables = {
 
 # Query the API
 def query_discovery_api(auth_token, gql_query, variables):
-    response = requests.post('https://metadata.cloud.getdbt.com/beta/graphql', 
+    response = requests.post('https://metadata.cloud.getdbt.com/beta/graphql',
         headers={"authorization": "Bearer "+auth_token, "content-type": "application/json"},
         json={"query": gql_query, "variables": variables})
     data = response.json()['data']['environment']
-    
+
     return data
 
 
 # Extract nodes for graph
 def extract_node_definitions(api_response):
     nodes = []
-    node_types = ["models", "sources", "seeds", "snapshots", "metrics", "exposures"]
+    node_types = ["models", "sources", "seeds", "snapshots", "exposures"]  # support for metrics and semanticModels coming soon
     for node_type in node_types:
         if node_type in api_response["definition"]:
             for node_edge in api_response["definition"][node_type]["edges"]:
@@ -872,37 +910,39 @@ Graph example:
 
 </details>
 
+-->
+
 <VersionBlock firstVersion="1.6">
 
-### Which metrics are available? 
+### Which metrics are available?
 
-Metric definitions are coming soon to the Discovery API with dbt v1.6. You’ll be able to query metrics using the dbt Semantic Layer, use them for documentation purposes (like for a data catalog), and calculate aggregations (like in a BI tool that doesn’t query the SL).
+You can define and query metrics using the [dbt Semantic Layer](/docs/build/about-metricflow), use them for documentation purposes (like for a data catalog), and calculate aggregations (like in a BI tool that doesn’t query the SL). To learn more, refer to [Get started with MetricFlow](/docs/build/sl-getting-started).
 
 <details>
 <summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  definition {
-	    metrics(first: $first) {
-	      edges {
-	        node {
-	          name
-	          description
-	          type
-	          formula
-	          filter
-	          tags
-	          parents {
-	            name
-	            resourceType
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    definition {
+      metrics(first: $first) {
+        edges {
+          node {
+            name
+            description
+            type
+            formula
+            filter
+            tags
+            parents {
+              name
+              resourceType
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 
@@ -912,7 +952,7 @@ query($environmentId: Int!, $first: Int!){
 
 <VersionBlock firstVersion="1.5">
 
-## Governance 
+## Governance
 
 You can use the Discovery API to audit data development and facilitate collaboration within and between teams.
 
@@ -923,95 +963,98 @@ For governance use cases, people tend to query the latest definition state, ofte
 You can define and surface the groups each model is associated with. Groups contain information like owner. This can help you identify which team owns certain models and who to contact about them.
 
 <details>
-<summary>Example query</summary> 
+<summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    model(first: $first, filter:{uniqueIds:["MODEL.PROJECT.NAME"]}) {
-	      edges {
-	        node {
-	          name
-	          description
-	          resourceType
-	          access
-	          group
-	        }
-	      }
-	    }
-	  }
-	  definition {
-	    groups(first: $first) {
-	      edges {
-	        node {
-	          name
-	          resourceType
-	          models {
-	            name
-	          }
-	          owner_name
-	          owner_email
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first, filter: { uniqueIds: ["MODEL.PROJECT.NAME"] }) {
+        edges {
+          node {
+            name
+            description
+            resourceType
+            access
+            group
+          }
+        }
+      }
+    }
+    definition {
+      groups(first: $first) {
+        edges {
+          node {
+            name
+            resourceType
+            models {
+              name
+            }
+            ownerName
+            ownerEmail
+          }
+        }
+      }
+    }
+  }
 }
 ```
 </details>
 
 ### Who can use this model?
 
-You can enable users the ability to specify the level of access for a given model. In the future, public models will function like APIs to unify project lineage and enable reuse of models using cross-project refs. 
+You can enable people the ability to specify the level of access for a given model. In the future, public models will function like APIs to unify project lineage and enable reuse of models using cross-project refs.
 
 
 <details>
-<summary>Example query</summary> 
+<summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  definition {
-	    models(first: $first) {
-	      edges {
-	        node {
-	          name
-	          access
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    definition {
+      models(first: $first) {
+        edges {
+          node {
+            name
+            access
+          }
+        }
+      }
+    }
+  }
 }
+```
 
 ---
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  definition {
-	    models(first: $first, filters:{access:public}) {
-	      edges {
-	        node {
-	          name
-	        }
-	      }
-	    }
-	  }
-	}
+
+```graphql
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    definition {
+      models(first: $first, filter: { access: public }) {
+        edges {
+          node {
+            name
+          }
+        }
+      }
+    }
+  }
 }
 ```
 </details>
 
 </VersionBlock>
 
-## Development 
+## Development
 
 You can use the Discovery API to understand dataset changes and usage and gauge impacts to inform project definition. Below are example questions and queries you can run.
 
 For development use cases, people typically query the historical or latest definition or applied state across any part of the DAG using the `environment` endpoint.
 
 ### How is this model or metric used in downstream tools?
-[Exposures](/docs/build/exposures) provide a method to define how a model or metric is actually used in dashboards and other analytics tools and use cases. You can query an exposure’s definition to see how project nodes are used and query its upstream lineage results to understand the state of the data used in it, which powers use cases like a freshness and quality status tile. 
+[Exposures](/docs/build/exposures) provide a method to define how a model or metric is actually used in dashboards and other analytics tools and use cases. You can query an exposure’s definition to see how project nodes are used and query its upstream lineage results to understand the state of the data used in it, which powers use cases like a freshness and quality status tile.
 
 <Lightbox src="/img/docs/dbt-cloud/discovery-api/data-freshness-metadata.jpg" width="25%" title="Status tiles in dbt Cloud"/>
 
@@ -1019,47 +1062,41 @@ For development use cases, people typically query the historical or latest defin
 <details>
 <summary>Example query</summary>
 
-This example reviews an exposure and the models used in it, including when they were last executed and their test results: 
+Below is an example that reviews an exposure and the models used in it including when they were last executed.
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    exposures(first: $first) {
-	      edges {
-	        node {
-	          name
-	          description
-	          owner_name
-	          url
-	          parents {
-	            name
-	            resourceType
-	            ... on ModelAppliedStateNode {
-	              executionInfo {
-	                executeCompletedAt
-	                lastRunStatus
-	              }
-	              tests {
-	                executionInfo {
-	                  executeCompletedAt
-	                  lastRunStatus
-	                }
-	              }
-	            }
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      exposures(first: $first) {
+        edges {
+          node {
+            name
+            description
+            ownerName
+            url
+            parents {
+              name
+              resourceType
+              ... on ModelAppliedStateNestedNode {
+                executionInfo {
+                  executeCompletedAt
+                  lastRunStatus
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 </details>
 
-### How has this model changed over time? 
-The Discovery API provides historical information about any resource in your project. For instance, you can view how a model has evolved over time (across recent runs) given changes to its shape and contents. 
+### How has this model changed over time?
 
+The Discovery API provides historical information about any resource in your project. For instance, you can view how a model has evolved over time (across recent runs) given changes to its shape and contents.
 
 <details>
 <summary>Example query</summary>
@@ -1067,54 +1104,69 @@ The Discovery API provides historical information about any resource in your pro
 Review the differences in `compiledCode` or `columns` between runs or plot the “Approximate Size” and “Row Count” `stats` over time:
 
 ```graphql
-query(environmentId: Int!, uniqueId: String!, lastRunCount: Int!, withCatalog: Boolean!){
-	modelByEnvironment(environmentId: $environmentId, uniqueId: $uniqueId, lastRunCount: $lastRunCount, withCatalog: $withCatalog) {
-	  name
-	  compiledCode
-		columns {
-			name
-		}
-	  stats {
-	    label
-	    value
-	  }
-	}
+query (
+  $environmentId: BigInt!
+  $uniqueId: String!
+  $lastRunCount: Int!
+  $withCatalog: Boolean!
+) {
+  environment(id: $environmentId) {
+    applied {
+      modelHistoricalRuns(
+        uniqueId: $uniqueId
+        lastRunCount: $lastRunCount
+        withCatalog: $withCatalog
+      ) {
+        name
+        compiledCode
+        columns {
+          name
+        }
+        stats {
+          label
+          value
+        }
+      }
+    }
+  }
 }
 ```
 </details>
 
 ### Which nodes depend on this data source?
+
 dbt lineage begins with data sources. For a given source, you can look at which nodes are its children then iterate downstream to get the full list of dependencies.
 
+Currently, querying beyond 1 generation (defined as a direct parent-to-child) is not supported. To see the grandchildren of a node, you need to make two queries: one to get the node and its children, and another to get the children nodes and their children.
 
 <details>
 <summary>Example query</summary>
 
 ```graphql
-query($environmentId: Int!, $first: Int!){
-	environment(id: $environmentId) {
-	  applied {
-	    sources(first: $first, filter:{uniqueIds:["SOURCE_NAME.TABLE_NAME"]}) {
-	      edges {
-	        node {
-	          loader
-	          children {
-	            uniqueId
-	            resourceType
-	            ... on ModelAppliedStateNode {
-	              database
-	              schema
-	              alias
-	              children {
-	                uniqueId
-	              }
-	            }
-	          }
-	        }
-	      }
-	    }
-	  }
-	}
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      sources(
+        first: $first
+        filter: { uniqueIds: ["SOURCE_NAME.TABLE_NAME"] }
+      ) {
+        edges {
+          node {
+            loader
+            children {
+              uniqueId
+              resourceType
+              ... on ModelAppliedStateNestedNode {
+                database
+                schema
+                alias
+              }
+            }
+          }
+        }
+      }
+    }
+  }
 }
 ```
 </details>
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-environment-applied-modelHistoricalRuns.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment-applied-modelHistoricalRuns.mdx
new file mode 100644
index 00000000000..d1463f9e9b7
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment-applied-modelHistoricalRuns.mdx
@@ -0,0 +1,50 @@
+---
+title: "Model Historical Runs object schema"
+sidebar_label: "Model historical runs"
+id: "discovery-schema-environment-applied-modelHistoricalRuns"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The model historical runs object allows you to query information about a model's run history. 
+
+The [Example query](#example-query) illustrates a few fields you can query with the `modelHistoricalRuns` object. Refer to [Fields](#fields) to view the entire schema, which provides all possible fields you can query.
+
+### Arguments
+
+When querying for `modelHistoricalRuns`, you can use the following arguments:
+
+<NodeArgsTable parent="AppliedState" name="modelHistoricalRuns" />
+
+### Example query
+
+You can use the `environmentId` and the model's `uniqueId` to return the model and its execution time for the last 20 times it was run, regardless of which job ran it.
+
+```graphql
+query {
+  environment(id: 834) {
+    applied {
+      modelHistoricalRuns(
+        uniqueId: "model.marketing.customers"
+        lastRunCount: 20
+      ) {
+        runId # Get historical results for a particular model
+        runGeneratedAt
+        executionTime # View build time across runs
+        status
+        tests {
+          name
+          status
+          executeCompletedAt
+        } # View test results across runs
+      }
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for `modelHistoricalRuns`, you can use the following fields:
+
+<SchemaTable nodeName="ModelNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
index 41fd5555c3f..a82bba6576d 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-environment.mdx
@@ -4,28 +4,34 @@ sidebar_label: "Environment"
 id: "discovery-schema-environment"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 <Snippet path="discovery-public-preview-banner" />
 
-This environment object allows you to query information about a particular model based on `environmentId`.
+The environment object allows you to query information about a particular model based on `environmentId`.
 
-The [example query](#example-query) illustrates a few fields you can query in this `environment` object. Refer to [Fields](#fields) to see the entire schema, which provides all possible fields you can query.
+The [Example queries](#example-queries) illustrate a few fields you can query with this `environment` object. Refer to [Fields](#fields) to view the entire schema, which provides all possible fields you can query.
 
 ### Arguments
 
 When querying for `environment`, you can use the following arguments.
 
-<ArgsTable queryName="environment" useBetaAPI />
+<QueryArgsTable queryName="environment" useBetaAPI />
 
+:::caution
 
-### Example Query
+dbt Labs is making changes to the Discovery API. These changes will take effect on August 15, 2023.
 
-You can use your production environment's `id`: 
+The data type `Int` for `id` is being deprecated and will be replaced with `BigInt`. When the time comes, you will need to update your API call accordingly to avoid errors.
+:::
+
+### Example queries
+
+You can use your production environment's `id`:
 
 ```graphql
 query Example {
-	environment(id: 834){ # Get the latest state of the production environment 
+	environment(id: 834){ # Get the latest state of the production environment
 		applied { # The state of an executed node as it exists as an object in the database
 			models(first: 100){ # Pagination to ensure manageable response for large projects
 				edges { node {
@@ -34,8 +40,8 @@ query Example {
 					executionInfo {executeCompletedAt, executionTime}, # Metadata from when the model was built
 					tests {name, executionInfo{lastRunStatus, lastRunError}}, # Latest test results
 					catalog {columns {name, description, type}, stats {label, value}}, # Catalog info
-					ancestors(types:[Source]) {name, ...on SourceAppliedStateNode {freshness{maxLoadedAt, freshnessStatus}}}, # Source freshness } 
-					children {name, resourceType}}} # Immediate dependencies in lineage	
+					ancestors(types:[Source]) {name, ...on SourceAppliedStateNode {freshness{maxLoadedAt, freshnessStatus}}}, # Source freshness }
+					children {name, resourceType}}} # Immediate dependencies in lineage
 				totalCount } # Number of models in the project
 		}
 		definition { # The logical state of a given project node given its most recent manifest generated
@@ -48,12 +54,50 @@ query Example {
 	}
 ```
 
+With the deprecation of the data type `Int` for `id`, below is an example of replacing it with `BigInt`:
+
+```graphql
+query ($environmentId: BigInt!, $first: Int!) {
+  environment(id: $environmentId) {
+    applied {
+      models(first: $first) {
+        edges {
+          node {
+            uniqueId
+            executionInfo {
+              lastRunId
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+```
+
+With the deprecation of `modelByEnvironment`, below is an example of replacing it with `environment`:
+
+```graphql
+query ($environmentId: BigInt!, $uniqueId: String) {
+  environment(id: $environmentId) {
+    applied {
+      modelHistoricalRuns(uniqueId: $uniqueId) {
+        uniqueId
+        executionTime
+        executeCompletedAt
+      }
+    }
+  }
+}
+```
+
 ### Fields
 When querying an `environment`, you can use the following fields.
 
 <SchemaTable nodeName="Environment" useBetaAPI />
 
-When querying the `applied` field of `environment`, you can use the following fields. 
+When querying the `applied` field of `environment`, you can use the following fields.
 <SchemaTable nodeName="AppliedState" useBetaAPI />
 
 When querying the `definition` field of `environment`, you can use the following fields.
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-exposure.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-exposure.mdx
index d74f12223c5..aa1d27fd83c 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-exposure.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-exposure.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Exposure"
 id: "discovery-schema-exposure"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
-The exposure object allows you to query information about a particular exposure. You can learn more about exposures [here](/docs/build/exposures).
+The exposure object allows you to query information about a particular exposure. To learn more, refer to [Add Exposures to your DAG](/docs/build/exposures).
+
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
 ### Arguments
 
-When querying for an `exposure`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for an `exposure`, the following arguments are available. If you don't include a `runId`, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="exposure" />
+<QueryArgsTable queryName="exposure" />
 
-Below we show some illustrative example queries and outline the schema of this exposure object.
+Below we show some illustrative example queries and outline the schema of the exposure object.
 
-### Example Queries
-#### Exposure information
+### Example query
 
-The example query below queries information about an exposure, including the owner's name and email, the url, and information about parent sources and parent models.
+The example below queries information about an exposure including the owner's name and email, the URL, and information about parent sources and parent models.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-exposures.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-exposures.mdx
index 5e3dcdd45a9..ba539c87dc8 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-exposures.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-exposures.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Exposures"
 id: "discovery-schema-exposures"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
-The exposures object allows you to query information about all exposures in a given job. You can learn more about exposures [here](/docs/build/exposures).
+The exposures object allows you to query information about all exposures in a given job. To learn more, refer to [Add Exposures to your DAG](/docs/build/exposures).
+
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
 ### Arguments
 
-When querying for `exposures`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `exposures`, the following arguments are available. If you don't include a `runId`, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="exposure" />
+<QueryArgsTable queryName="exposure" />
 
-Below we show some illustrative example queries and outline the schema of this exposures object.
+Below we show some illustrative example queries and outline the schema of the exposures object.
 
-### Example Queries
-#### Exposures information
+### Example query
 
-The example query below queries information about all exposures in a given job, including, for each exposure, the owner's name and email, the url, and information about parent sources and parent models.
+The example below queries information about all exposures in a given job including the owner's name and email, the URL, and information about parent sources and parent models for each exposure.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposure.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposure.mdx
new file mode 100644
index 00000000000..58855659d05
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposure.mdx
@@ -0,0 +1,64 @@
+---
+title: "Exposure object schema"
+sidebar_label: "Exposure"
+id: "discovery-schema-job-exposure"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The exposure object allows you to query information about a particular exposure. To learn more, refer to [Add Exposures to your DAG](/docs/build/exposures).
+
+### Arguments
+
+When querying for an `exposure`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="exposure" />
+
+Below we show some illustrative example queries and outline the schema of the exposure object.
+
+### Example query
+
+The example below queries information about an exposure including the owner's name and email, the URL, and information about parent sources and parent models.
+
+```graphql
+{
+  job(id: 123) {
+    exposure(name: "my_awesome_exposure") {
+      runId
+      projectId
+      name
+      uniqueId
+      resourceType
+      ownerName
+      url
+      ownerEmail
+      parentsSources {
+        uniqueId
+        sourceName
+        name
+        state
+        maxLoadedAt
+        criteria {
+          warnAfter {
+            period
+            count
+          }
+          errorAfter {
+            period
+            count
+          }
+        }
+        maxLoadedAtTimeAgoInS
+      }
+      parentsModels {
+        uniqueId
+      }
+    }
+  }
+}
+```
+
+### Fields
+When querying for an `exposure`, the following fields are available:
+
+<SchemaTable nodeName="ExposureNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposures.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposures.mdx
new file mode 100644
index 00000000000..b4fe027e324
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-exposures.mdx
@@ -0,0 +1,65 @@
+---
+title: "Exposures object schema"
+sidebar_label: "Exposures"
+id: "discovery-schema-job-exposures"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The exposures object allows you to query information about all exposures in a given job. To learn more, refer to [Add Exposures to your DAG](/docs/build/exposures).
+
+
+### Arguments
+
+When querying for `exposures`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="exposures" />
+
+Below we show some illustrative example queries and outline the schema of the exposures object.
+
+### Example query
+
+The example below queries information about all exposures in a given job including the owner's name and email, the URL, and information about parent sources and parent models for each exposure.
+
+```graphql
+{
+  job(id: 123) {
+    exposures(jobId: 123) {
+      runId
+      projectId
+      name
+      uniqueId
+      resourceType
+      ownerName
+      url
+      ownerEmail
+      parentsSources {
+        uniqueId
+        sourceName
+        name
+        state
+        maxLoadedAt
+        criteria {
+          warnAfter {
+            period
+            count
+          }
+          errorAfter {
+            period
+            count
+          }
+        }
+        maxLoadedAtTimeAgoInS
+      }
+      parentsModels {
+        uniqueId
+      }
+    }
+  }
+}
+```
+
+### Fields
+When querying for `exposures`, the following fields are available:
+
+<SchemaTable nodeName="ExposureNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metric.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metric.mdx
new file mode 100644
index 00000000000..1f1f490d62f
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metric.mdx
@@ -0,0 +1,58 @@
+---
+title: "Metric object schema"
+sidebar_label: "Metric"
+id: "discovery-schema-job-metric"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The metric object allows you to query information about [metrics](/docs/build/metrics).
+
+### Arguments
+
+When querying for a `metric`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="metric" />
+
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the metric object.
+
+### Example query
+
+The example query below outputs information about a metric. You can also add any field from the model endpoint (the example simply selects name). This includes schema, database, uniqueId, columns, and more. For details, refer to [Model object schema](/docs/dbt-cloud-apis/discovery-schema-model).
+
+
+```graphql
+{
+  job(id: 123) {
+    metric(uniqueId: "metric.jaffle_shop.new_customers") {
+      uniqueId
+      name
+      packageName
+      tags
+      label
+      runId
+      description
+      type
+      sql
+      timestamp
+      timeGrains
+      dimensions
+      meta
+      resourceType
+      filters {
+        field
+        operator
+        value
+      }
+      model {
+        name
+      }
+    }
+  }
+}
+```
+
+### Fields
+When querying for a `metric`, the following fields are available:
+
+<SchemaTable nodeName="MetricNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metrics.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metrics.mdx
new file mode 100644
index 00000000000..174dd5b676a
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-metrics.mdx
@@ -0,0 +1,60 @@
+---
+title: "Metrics object schema"
+sidebar_label: "Metrics"
+id: "discovery-schema-job-metrics"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The metrics object allows you to query information about [metrics](/docs/build/metrics).
+
+
+### Arguments
+
+When querying for `metrics`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="metrics" />
+
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the metrics object.
+
+### Example query
+
+The example query returns information about all metrics for the given job.
+
+```graphql
+{
+  job(id: 123) {
+    metrics {
+      uniqueId
+      name
+      packageName
+      tags
+      label
+      runId
+      description
+      type
+      sql
+      timestamp
+      timeGrains
+      dimensions
+      meta
+      resourceType
+      filters {
+        field
+        operator
+        value
+      }
+      model {
+        name
+      }
+    }
+  }
+}
+```
+
+### Fields
+The metrics object can access the _same fields_ as the [metric node](/docs/dbt-cloud-apis/discovery-schema-job-metric). The difference is that the metrics object can output a list so instead of querying for fields for one specific metric, you can query for those parameters for all metrics in a run.
+
+When querying for `metrics`, the following fields are available:
+
+<SchemaTable nodeName="MetricNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-model.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-model.mdx
new file mode 100644
index 00000000000..abd1ca1b1d6
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-model.mdx
@@ -0,0 +1,91 @@
+---
+title: "Model object schema"
+sidebar_label: "Model"
+id: "discovery-schema-job-model"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The model object allows you to query information about a particular model in a given job.
+
+### Arguments
+
+When querying for a `model`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="model" />
+
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the model object.
+
+### Example query for finding parent models and sources
+
+The example query below uses the `parentsModels` and `parentsSources` fields to fetch information about a model’s parent models and parent sources. The jobID and uniqueID fields are placeholders that you will need to replace with your own values.
+
+```graphql
+{
+  job(id: 123) {
+    model(uniqueId: "model.jaffle_shop.dim_user") {
+      parentsModels {
+        runId
+        uniqueId
+        executionTime
+      }
+      parentsSources {
+        runId
+        uniqueId
+        state
+      }
+    }
+  }
+}
+
+```
+
+### Example query for model timing
+
+The example query below could be useful if you want to understand information around execution timing on a given model (start, end, completion).
+
+```graphql
+{
+  job(id: 123) {
+    model(uniqueId: "model.jaffle_shop.dim_user") {
+      runId
+      projectId
+      name
+      uniqueId
+      resourceType
+      executeStartedAt
+      executeCompletedAt
+      executionTime
+    }
+  }
+}
+```
+
+### Example query for column-level information
+
+You can use the following example query to understand more about the columns of a given model. This query will only work if the job has generated documentation; that is, it will work with the command `dbt docs generate`.
+
+```graphql
+{
+  job(id: 123) {
+    model(uniqueId: "model.jaffle_shop.dim_user") {
+      columns {
+        name
+        index
+        type
+        comment
+        description
+        tags
+        meta
+      }
+    }
+  }
+}
+```
+
+
+### Fields
+
+When querying for a `model`, the following fields are available:
+
+<SchemaTable nodeName="ModelNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-models.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-models.mdx
new file mode 100644
index 00000000000..ee512f3cd97
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-models.mdx
@@ -0,0 +1,59 @@
+---
+title: "Models object schema"
+sidebar_label: "Models"
+id: "discovery-schema-job-models"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+
+The models object allows you to query information about all models in a given job.
+
+### Arguments
+
+When querying for `models`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="models" />
+
+Below we show some illustrative example queries and outline the schema of the models object.
+
+### Example queries
+The database, schema, and identifier arguments are all optional. This means that with this endpoint you can:
+
+- Find a specific model by providing `<database>.<schema>.<identifier>`
+- Find all of the models in a database and/or schema by providing `<database>` and/or `<schema>`
+
+#### Find models by their database, schema, and identifier
+The example query below finds a model by its unique database, schema, and identifier.
+
+```graphql
+{
+  job(id: 123) {
+    models(database:"analytics", schema: "analytics", identifier:"dim_customers") {
+      uniqueId
+    }
+  }
+}
+```
+
+#### Find models by their schema
+The example query below finds all models in this schema and their respective execution times.
+
+```graphql
+{
+  job(id: 123) {
+    models(schema: "analytics") {
+      uniqueId
+      executionTime
+    }
+  }
+}
+```
+
+
+### Fields
+The models object can access the _same fields_ as the [Model node](/docs/dbt-cloud-apis/discovery-schema-job-model). The difference is that the models object can output a list so instead of querying for fields for one specific model, you can query for those parameters for all models within a jobID, database, and so on.
+
+When querying for `models`, the following fields are available:
+
+<SchemaTable nodeName="ModelNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seed.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seed.mdx
new file mode 100644
index 00000000000..924e3e87e91
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seed.mdx
@@ -0,0 +1,42 @@
+---
+title: "Seed object schema"
+sidebar_label: "Seed"
+id: "discovery-schema-job-seed"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The seed object allows you to query information about a particular seed in a given job.
+
+### Arguments
+
+When querying for a `seed`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="seed" />
+
+Below we show some illustrative example queries and outline the schema of the seed object.
+
+### Example query
+
+The example query below pulls relevant information about a given seed. For instance, you can view the load time.
+
+```graphql
+{
+  job(id: 123) {
+    seed(uniqueId: "seed.jaffle_shop.raw_customers") {
+      database
+      schema
+      uniqueId
+      name
+      status
+      error
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for a `seed`, the following fields are available:
+
+<SchemaTable nodeName="SeedNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seeds.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seeds.mdx
new file mode 100644
index 00000000000..6ed45216e5f
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-seeds.mdx
@@ -0,0 +1,40 @@
+---
+title: "Seeds object schema"
+sidebar_label: "Seeds"
+id: "discovery-schema-job-seeds"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The seeds object allows you to query information about all seeds in a given job.
+
+### Arguments
+
+When querying for `seeds`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="seed" />
+
+Below we show some illustrative example queries and outline the schema of the seeds object.
+
+### Example query
+
+The example query below pulls relevant information about all seeds in a given job. For instance, you can view load times.
+
+```graphql
+{
+  job(id: 123) {
+    seeds {
+      uniqueId
+      name
+      executionTime
+      status
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for `seeds`, the following fields are available:
+
+<SchemaTable nodeName="SeedNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-snapshots.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-snapshots.mdx
new file mode 100644
index 00000000000..a57163e0554
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-snapshots.mdx
@@ -0,0 +1,49 @@
+---
+title: "Snapshots object schema"
+sidebar_label: "Snapshots"
+id: "discovery-schema-job-snapshots"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The snapshots object allows you to query information about all snapshots in a given job.
+
+### Arguments
+
+When querying for `snapshots`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="snapshots" />
+
+Below we show some illustrative example queries and outline the schema of the snapshots object.
+
+### Example query
+
+The database, schema, and identifier arguments are optional. This means that with this endpoint you can:
+
+- Find a specific snapshot by providing `<database>.<schema>.<identifier>`
+- Find all of the snapshots in a database and/or schema by providing `<database>` and/or `<schema>`
+
+#### Find snapshots information for a job
+
+The example query returns information about all snapshots in this job.
+
+```graphql
+{
+  job(id: 123) {
+    snapshots {
+      uniqueId
+      name
+      executionTime
+      environmentId
+      executeStartedAt
+      executeCompletedAt
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for `snapshots`, the following fields are available:
+
+<SchemaTable nodeName="SnapshotNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-source.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-source.mdx
new file mode 100644
index 00000000000..972e929f4cd
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-source.mdx
@@ -0,0 +1,52 @@
+---
+title: "Source object schema"
+sidebar_label: "Source"
+id: "discovery-schema-job-source"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The source object allows you to query information about a particular source in a given job.
+
+### Arguments
+
+When querying for a `source`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="source" />
+
+Below we show some illustrative example queries and outline the schema of the source object.
+
+### Example query
+
+The query below pulls relevant information about a given source. For instance, you can view the load time and the state (pass, fail, error) of that source.
+
+```graphql
+{
+  job(id: 123) {
+    source(uniqueId: "source.jaffle_shop.snowplow.event") {
+      uniqueId
+      sourceName
+      name
+      state
+      maxLoadedAt
+      criteria {
+        warnAfter {
+          period
+          count
+        }
+        errorAfter {
+          period
+          count
+        }
+      }
+      maxLoadedAtTimeAgoInS
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for a `source`, the following fields are available:
+
+<SchemaTable nodeName="SourceNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-sources.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-sources.mdx
new file mode 100644
index 00000000000..97f717d269a
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-sources.mdx
@@ -0,0 +1,65 @@
+---
+title: "Sources object schema"
+sidebar_label: "Sources"
+id: "discovery-schema-job-sources"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The sources object allows you to query information about all sources in a given job.
+
+### Arguments
+
+When querying for `sources`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="sources" />
+
+Below we show some illustrative example queries and outline the schema of the sources object.
+
+### Example queries
+
+The database, schema, and identifier arguments are optional. This means that with this endpoint you can:
+
+- Find a specific source by providing `<database>.<schema>.<identifier>`
+- Find all of the sources in a database and/or schema by providing `<database>` and/or `<schema>`
+
+#### Finding sources by their database, schema, and identifier
+
+The example query below finds a source by its unique database, schema, and identifier.
+
+```graphql
+{
+  job(id: 123) {
+    sources(
+      database: "analytics"
+      schema: "analytics"
+      identifier: "dim_customers"
+    ) {
+      uniqueId
+    }
+  }
+}
+```
+
+#### Finding sources by their schema
+
+The example query below finds all sources in this schema and their respective states (pass, error, fail).
+
+```graphql
+{
+  job(id: 123) {
+    sources(schema: "analytics") {
+      uniqueId
+      state
+    }
+  }
+}
+```
+
+### Fields
+
+The sources object can access the _same fields_ as the [source node](/docs/dbt-cloud-apis/discovery-schema-job-source). The difference is that the sources object can output a list so instead of querying for fields for one specific source, you can query for those parameters for all sources within a jobID, database, and so on.
+
+When querying for `sources`, the following fields are available:
+
+<SchemaTable nodeName="SourceNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-test.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-test.mdx
new file mode 100644
index 00000000000..c52aa49ab93
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-test.mdx
@@ -0,0 +1,43 @@
+---
+title: "Test object schema"
+sidebar_label: "Test"
+id: "discovery-schema-job-test"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The test object allows you to query information about a particular test.
+
+### Arguments
+
+When querying for a `test`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="test" />
+
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the test object.
+
+### Example query
+
+The example query below outputs information about a test including the state of the test result. In order of severity, the result can be one of these: "error", "fail", "warn", or "pass".
+
+```graphql
+{
+  job(id: 123) {
+    test(uniqueId: "test.internal_analytics.not_null_metrics_id") {
+      runId
+      accountId
+      projectId
+      uniqueId
+      name
+      columnName
+      state
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for a `test`, the following fields are available:
+
+<SchemaTable nodeName="TestNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job-tests.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-tests.mdx
new file mode 100644
index 00000000000..efcef674c55
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job-tests.mdx
@@ -0,0 +1,43 @@
+---
+title: "Tests object schema"
+sidebar_label: "Tests"
+id: "discovery-schema-job-tests"
+---
+
+import { NodeArgsTable, SchemaTable } from "./schema";
+
+The tests object allows you to query information about all tests in a given job.
+
+### Arguments
+
+When querying for `tests`, the following arguments are available.
+
+<NodeArgsTable parent="JobNode" name="tests" />
+
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the tests object.
+
+### Example query
+
+The example query below finds all tests in this job and includes information about those tests.
+
+```graphql
+{
+  job(id: 123) {
+    tests {
+      runId
+      accountId
+      projectId
+      uniqueId
+      name
+      columnName
+      state
+    }
+  }
+}
+```
+
+### Fields
+
+When querying for `tests`, the following fields are available:
+
+<SchemaTable nodeName="TestNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-job.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-job.mdx
new file mode 100644
index 00000000000..bb30786e19d
--- /dev/null
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-job.mdx
@@ -0,0 +1,62 @@
+---
+title: "Job object schema"
+sidebar_label: "Job"
+id: "discovery-schema-job"
+---
+
+import { QueryArgsTable, SchemaTable } from "./schema";
+
+The job object allows you to query information about a particular model based on `jobId` and, optionally, a `runId`.
+
+If you don't provide a `runId`, the API returns information on the latest runId of a job.
+
+The [example query](#example-query) illustrates a few fields you can query in this `job` object. Refer to [Fields](#fields) to see the entire schema, which provides all possible fields you can query.
+
+### Arguments
+
+When querying for `job`, you can use the following arguments.
+
+<QueryArgsTable queryName="job" />
+
+
+### Example Query
+
+You can use your production job's `id`.
+
+```graphql
+query JobQueryExample {
+  # Provide runId for looking at specific run, otherwise it defaults to latest run
+  job(id: 940) {
+    # Get all models from this job's latest run
+    models(schema: "analytics") {
+      uniqueId
+      executionTime
+    }
+
+    # Or query a single node
+    source(uniqueId: "source.jaffle_shop.snowplow.event") {
+      uniqueId
+      sourceName
+      name
+      state
+      maxLoadedAt
+      criteria {
+        warnAfter {
+          period
+          count
+        }
+        errorAfter {
+          period
+          count
+        }
+      }
+      maxLoadedAtTimeAgoInS
+    }
+  }
+}
+```
+
+### Fields
+When querying an `job`, you can use the following fields.
+
+<SchemaTable nodeName="JobNode" />
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-metric.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-metric.mdx
index 2280c6f7802..aee04ba2cce 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-metric.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-metric.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Metric"
 id: "discovery-schema-metric"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The metric object allows you to query information about [metrics](/docs/build/metrics).
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for a `metric`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for a `metric`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="metric" />
+<QueryArgsTable queryName="metric" />
 
-Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this metric object.
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the metric object.
 
-### Example Queries
-#### Metric information
+### Example query
 
-The example query below outputs information about a metric.  Note that you can also add any field from the Model endpoint -- here we are simply selecting name. This includes schema, database, uniqueId, columns and more -- find documentation [here](/docs/dbt-cloud-apis/discovery-schema-model).
+The example query below outputs information about a metric. You can also add any field from the model endpoint (the example simply selects name). This includes schema, database, uniqueId, columns, and more. For details, refer to [Model object schema](/docs/dbt-cloud-apis/discovery-schema-model).
 
 
 ```graphql
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-metrics.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-metrics.mdx
index 5242eb717dc..30d8d68b365 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-metrics.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-metrics.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Metrics"
 id: "discovery-schema-metrics"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The metrics object allows you to query information about [metrics](/docs/build/metrics).
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `metrics`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `metrics`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="metrics" />
+<QueryArgsTable queryName="metrics" />
 
-Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this metrics object.
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the metrics object.
 
-### Example Queries
-#### Metrics information
+### Example query
 
-The example query returns information about all metrics in this job.
+The example query returns information about all metrics for the given job.
 
 ```graphql
 {
@@ -52,7 +55,7 @@ The example query returns information about all metrics in this job.
 ```
 
 ### Fields
-metrics has access to the *same fields* as the [metric node](/docs/dbt-cloud-apis/discovery-schema-metric).  The difference is that metrics can output a list, so instead of querying for fields for one specific metric, you can query for those parameters for all metrics in a run.
+The metrics object can access the _same fields_ as the [metric node](/docs/dbt-cloud-apis/discovery-schema-metric). The difference is that the metrics object can output a list so instead of querying for fields for one specific metric, you can query for those parameters for all metrics in a run.
 
 When querying for `metrics`, the following fields are available:
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-model.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-model.mdx
index 3fb43edaded..7206fb9a51c 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-model.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-model.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Model"
 id: "discovery-schema-model"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The model object allows you to query information about a particular model in a given job.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for a `model`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for a `model`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="model" />
+<QueryArgsTable queryName="model" />
 
-Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this model object.
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the model object.
 
-### Example Queries
-#### Finding parent models and sources
+### Example query for finding parent models and sources
 
-The example query below uses the `parentsModels` and `parentsSources` fields to fetch information about a model’s parent models and parent sources. Note that we put a placeholder jobID and uniqueID, which you will have to replace.
+The example query below uses the `parentsModels` and `parentsSources` fields to fetch information about a model’s parent models and parent sources. The jobID and uniqueID fields are placeholders that you will need to replace with your own values.
 
 ```graphql
 {
@@ -38,9 +41,9 @@ The example query below uses the `parentsModels` and `parentsSources` fields to
 }
 ```
 
-#### Model Timing
+### Example query for model timing
 
-The example query below could be useful if we wanted to understand information around execution timing on a given model (start, end, completion).
+The example query below could be useful if you wanted to understand information around execution timing on a given model (start, end, completion).
 
 ```graphql
 {
@@ -57,9 +60,10 @@ The example query below could be useful if we wanted to understand information a
 }
 ```
 
-#### Column-level information
+### Example query for column-level information
+
+You can use the following example query to understand more about the columns of a given model. This query will only work if the job has generated documentation; that is, it will work with the command `dbt docs generate`.
 
-You can use the following example query to understand more about the columns of a given model.  Note that this will only work if the job has generated documentation. For example it will work with the command `dbt docs generate`.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
index 078d2512256..dade3d32d8a 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-modelByEnv.mdx
@@ -4,24 +4,34 @@ sidebar_label: "Model by environment"
 id: "discovery-schema-modelByEnv"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
-<Snippet path="discovery-public-preview-banner" />
+:::caution
+
+dbt Labs is making changes to the Discovery API. These changes will take effect on August 15, 2023.
+
+The model by environment object is being deprecated and will be replaced with `environment { applied { modelHistoricalRuns } }`. When the time comes, you will need to update your API calls accordingly to avoid errors. Refer to the
+[Environment object schema](/docs/dbt-cloud-apis/discovery-schema-environment) for details.
+:::
 
 This model by environment object allows you to query information about a particular model based on `environmentId`.
 
-The [example query](#example-query) illustrates a few fields you can query in this `modelByEnvironment` object. Refer to [Fields](#fields) to see the entire schema, which provides all possible fields you can query.
+The [Example query](#example-query) illustrates a few fields you can query in the `modelByEnvironment` object. Refer to [Fields](#fields) to see the entire schema, which provides all possible fields you can query.
+
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
 ### Arguments
 
 When querying for `modelByEnvironment`, you can use the following arguments.
 
-<ArgsTable queryName="modelByEnvironment" />
+<QueryArgsTable queryName="modelByEnvironment" />
 
 
-### Example Query
+### Example query
 
-You can use the `environment_id` and `model_unique_id` to return the model and its execution time for the last 20 times it was run, regardless of which job ran it.
+You can use the `environmentId` and the model's `uniqueId` to return the model and its execution time for the last 20 times it was run, regardless of which job ran it.
 
 
 ```graphql
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-models.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-models.mdx
index a3215eee039..f813d6a9ccf 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-models.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-models.mdx
@@ -4,25 +4,29 @@ sidebar_label: "Models"
 id: "discovery-schema-models"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 
 The models object allows you to query information about all models in a given job.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `models`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `models`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="models" />
+<QueryArgsTable queryName="models" />
 
-Below we show some illustrative example queries and outline the schema of this models object.
+Below we show some illustrative example queries and outline the schema of the models object.
 
-### Example Queries
-As we noted above, database, schema, and identifier are all optional arguments.  This means that with this endpoint, you can:
+### Example queries
+The database, schema, and identifier arguments are optional. This means that with this endpoint you can:
 - Find a specific model by providing `<database>.<schema>.<identifier>`
 - Find all of the models in a database and/or schema by providing `<database>` and/or `<schema>`
 
-#### Finding models by their database, schema, and identifier
+#### Find models by their database, schema, and identifier
 The example query below finds a model by its unique database, schema, and identifier.
 
 ```graphql
@@ -33,8 +37,8 @@ The example query below finds a model by its unique database, schema, and identi
 }
 ```
 
-#### Finding models by their schema
-The example query below finds all models in this schema, and their respective execution times.
+#### Find models by their schema
+The example query below finds all models in this schema and their respective execution times.
 
 ```graphql
 {
@@ -47,7 +51,7 @@ The example query below finds all models in this schema, and their respective ex
 
 
 ### Fields
-Models has access to the *same fields* as the [Model node](/docs/dbt-cloud-apis/discovery-schema-model).  The difference is that Models can output a list, so instead of querying for fields for one specific model, you can query for those parameters for all models within a jobID, database, etc.
+The models object can access the _same fields_ as the [Model node](/docs/dbt-cloud-apis/discovery-schema-model). The difference is that the models object can output a list so instead of querying for fields for one specific model, you can query for those parameters for all models within a jobID, database, and so on.
 
 When querying for `models`, the following fields are available:
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-seed.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-seed.mdx
index 1047545a8be..110f417769b 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-seed.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-seed.mdx
@@ -4,23 +4,25 @@ sidebar_label: "Seed"
 id: "discovery-schema-seed"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The seed object allows you to query information about a particular seed in a given job.
 
-### Arguments
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
-When querying for a `seed`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+### Arguments
 
-<ArgsTable queryName="seed" />
+When querying for a `seed`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-Below we show some illustrative example queries and outline the schema of this seed object.
+<QueryArgsTable queryName="seed" />
 
-### Example Queries
+Below we show some illustrative example queries and outline the schema of the seed object.
 
-#### Seed information
+### Example query
 
-The query below pulls relevant information about a given seed. For example, we could see the load time.
+The query below pulls relevant information about a given seed. For instance, you can view the load time.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-seeds.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-seeds.mdx
index 2cee2b8aa3f..c0a45664e38 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-seeds.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-seeds.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Seeds"
 id: "discovery-schema-seeds"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The seeds object allows you to query information about a all seeds in a given job.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `seeds`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `seeds`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="seed" />
+<QueryArgsTable queryName="seed" />
 
-Below we show some illustrative example queries and outline the schema of this seeds object.
+Below we show some illustrative example queries and outline the schema of the seeds object.
 
-### Example Queries
-#### Seeds information
+### Example query
 
-The query below pulls relevant information about all seeds in a given job.  For example, we could see the load times.
+The query below pulls relevant information about all seeds in a given job.  For instance, you can view load times.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-snapshots.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-snapshots.mdx
index b3f7071319f..4d2316cfc6a 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-snapshots.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-snapshots.mdx
@@ -4,24 +4,28 @@ sidebar_label: "Snapshots"
 id: "discovery-schema-snapshots"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The snapshots object allows you to query information about all snapshots in a given job.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `snapshots`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `snapshots`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="snapshots" />
+<QueryArgsTable queryName="snapshots" />
 
-Below we show some illustrative example queries and outline the schema of this snapshots object.
+Below we show some illustrative example queries and outline the schema of the snapshots object.
 
-### Example Query
-As we noted above, database, schema, and identifier are all optional arguments.  This means that with this endpoint, you can:
+### Example query
+The database, schema, and identifier arguments are optional. This means that with this endpoint you can:
 - Find a specific snapshot by providing `<database>.<schema>.<identifier>`
 - Find all of the snapshots in a database and/or schema by providing `<database>` and/or `<schema>`
 
-#### Finding snapshots information for a job
+#### Find snapshots information for a job
 The example query returns information about all snapshots in this job.
 
 ```graphql
@@ -39,7 +43,7 @@ The example query returns information about all snapshots in this job.
 ```
 
 ### Fields
-Snapshots has access to the *same fields* as the [Snapshot node](/docs/dbt-cloud-apis/discovery-schema-snapshots).  The difference is that Snapshots can output a list, so instead of querying for fields for one specific snapshot, you can query for those parameters for all snapshots within a jobID, database, etc.
+The snapshots object can access the _same fields_ as the [Snapshot node](/docs/dbt-cloud-apis/discovery-schema-snapshots). The difference is that the snapshots object can output a list so instead of querying for fields for one specific snapshot, you can query for those parameters for all snapshots within a jobID, database, and so on.
 
 When querying for `snapshots`, the following fields are available:
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-source.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-source.mdx
index 87d776282fe..3d632a035e3 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-source.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-source.mdx
@@ -4,23 +4,25 @@ sidebar_label: "Source"
 id: "discovery-schema-source"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The source object allows you to query information about a particular source in a given job.
 
-### Arguments
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
 
-When querying for a `source`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+### Arguments
 
-<ArgsTable queryName="source" />
+When querying for a `source`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-Below we show some illustrative example queries and outline the schema of this source object.
+<QueryArgsTable queryName="source" />
 
-### Example Queries
+Below we show some illustrative example queries and outline the schema of the source object.
 
-#### Source information
+### Example query
 
-The query below pulls relevant information about a given source. For example, we could see the load time and the state (“pass”, “fail”, “error”) of that source.
+The example query below pulls relevant information about a given source. For instance, you can view the load time and the state (pass, fail, error) of that source.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-sources.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-sources.mdx
index a719c5caf92..591f8e0307c 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-sources.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-sources.mdx
@@ -4,25 +4,29 @@ sidebar_label: "Sources"
 id: "discovery-schema-sources"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 
 The sources object allows you to query information about all sources in a given job.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `sources`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `sources`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="sources" />
+<QueryArgsTable queryName="sources" />
 
-Below we show some illustrative example queries and outline the schema of this sources object.
+Below we show some illustrative example queries and outline the schema of the sources object.
 
-### Example Queries
-As we noted above, database, schema, and identifier are all optional arguments.  This means that with this endpoint, you can:
+### Example queries
+The database, schema, and identifier arguments are optional. This means that with this endpoint you can:
 - Find a specific source by providing `<database>.<schema>.<identifier>`
 - Find all of the sources in a database and/or schema by providing `<database>` and/or `<schema>`
 
-#### Finding sources by their database, schema, and identifier
+#### Find sources by their database, schema, and identifier
 The example query below finds a source by its unique database, schema, and identifier.
 
 ```graphql
@@ -33,8 +37,8 @@ The example query below finds a source by its unique database, schema, and ident
 }
 ```
 
-#### Finding sources by their schema
-The example query below finds all sources in this schema, and their respective states (pass, error, fail).
+#### Find sources by their schema
+The example query below finds all sources in this schema and their respective states (pass, error, fail).
 
 ```graphql
 {
@@ -46,7 +50,7 @@ The example query below finds all sources in this schema, and their respective s
 ```
 
 ### Fields
-Sources has access to the *same fields* as the [Source node](/docs/dbt-cloud-apis/discovery-schema-source).  The difference is that Sources can output a list, so instead of querying for fields for one specific source, you can query for those parameters for all sources within a jobID, database, etc.
+The sources object can access the _same fields_ as the [Source node](/docs/dbt-cloud-apis/discovery-schema-source). The difference is that the sources object can output a list so instead of querying for fields for one specific source, you can query for those parameters for all sources within a jobID, database, and so on.
 
 When querying for `sources`, the following fields are available:
 
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-test.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-test.mdx
index 2ee915d27c7..ea22a81fc8e 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-test.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-test.mdx
@@ -4,22 +4,25 @@ sidebar_label: "Test"
 id: "discovery-schema-test"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The test object allows you to query information about a particular test.
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for a `test`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for a `test`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="test" />
+<QueryArgsTable queryName="test" />
 
-Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this test object.
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the test object.
 
-### Example Queries
-#### Test result
+### Example query
 
-The example query below outputs information about a test, including the state of the test result. This can be one of, in order of severity, "error", "fail", "warn", "pass."
+The example query below outputs information about a test including the state of the test result. In order of severity, the result can be one of these: "error", "fail", "warn", or "pass".
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema-discovery-tests.mdx b/website/docs/docs/dbt-cloud-apis/schema-discovery-tests.mdx
index 7f087c85fee..250a73cea5d 100644
--- a/website/docs/docs/dbt-cloud-apis/schema-discovery-tests.mdx
+++ b/website/docs/docs/dbt-cloud-apis/schema-discovery-tests.mdx
@@ -4,23 +4,26 @@ sidebar_label: "Tests"
 id: "discovery-schema-tests"
 ---
 
-import { ArgsTable, SchemaTable } from "./schema";
+import { QueryArgsTable, SchemaTable } from "./schema";
 
 The tests object allows you to query information about all tests in a given job.
 
 
+import DiscoveryApiJobDeprecationNotice from '/snippets/_discovery_api_job_deprecation_notice.md';
+
+<DiscoveryApiJobDeprecationNotice />
+
 ### Arguments
 
-When querying for `tests`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job:
+When querying for `tests`, the following arguments are available. If you don't include a runId, the API defaults to the most recent run of the specified job:
 
-<ArgsTable queryName="tests" />
+<QueryArgsTable queryName="tests" />
 
-Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this tests object.
+Below we show some illustrative example queries and outline the schema (all possible fields you can query) of the tests object.
 
-### Example Queries
-#### Tests result
+### Example query
 
-The example query below finds all tests in this job, and includes information about those tests.
+The example query below finds all tests in this job and includes information about those tests.
 
 ```graphql
 {
diff --git a/website/docs/docs/dbt-cloud-apis/schema.jsx b/website/docs/docs/dbt-cloud-apis/schema.jsx
index 8b9bbc358f0..ea6660251f6 100644
--- a/website/docs/docs/dbt-cloud-apis/schema.jsx
+++ b/website/docs/docs/dbt-cloud-apis/schema.jsx
@@ -1,6 +1,52 @@
-import React, { setState } from "react";
+import React from "react";
 import { useState, useEffect } from 'react'
-const queriesQuery = `{
+
+const getTypeString = (typeStructure) => {
+  // Helper function to represent GraphQL type
+  if (!typeStructure) return ''
+
+  if (typeStructure.kind === 'NON_NULL') {
+    return `${getTypeString(typeStructure.ofType)}!`;
+  } else if (typeStructure.kind === 'LIST') {
+    return `[${getTypeString(typeStructure.ofType)}]`;
+  } else if (['OBJECT', 'SCALAR', 'ENUM'].includes(typeStructure.kind)) {
+    return `${typeStructure.name}${getTypeString(typeStructure.ofType)}`;
+  } else {
+    return '';
+  }
+}
+
+export const ArgsTable = ({ data, name }) => {
+  return (
+    <table>
+      <thead>
+        <tr>
+          <td>Field</td>
+          <td>Type</td>
+          <td>Required?</td>
+          <td>Description</td>
+        </tr>
+      </thead>
+      <tbody>
+        {data.fields.find(d => d.name === name).args.map(function ({ name, description, type }) {
+          return (
+            <tr key={name}>
+              <td><code>{name}</code></td>
+              <td><code title={type.description}>{getTypeString(type)}</code></td>
+              <td>{type.kind === 'NON_NULL' ? `Yes` : `No`}</td>
+              <td>{description || `No description provided`}</td>
+            </tr>
+          )
+        })}
+      </tbody>
+    </table>
+  )
+}
+
+const metadataUrl = 'https://metadata.cloud.getdbt.com/graphql'
+const metadataBetaUrl = 'https://metadata.cloud.getdbt.com/beta/graphql'
+
+const queryArgsQuery = `{
   __schema {
     queryType {
       fields {
@@ -18,23 +64,22 @@ const queriesQuery = `{
             name
             description
             kind
-            ofType { name description }
+            ofType { kind name description }
           }
         }
       }
     }
   }
 }`
-const metadataUrl = 'https://metadata.cloud.getdbt.com/graphql'
-const metadataBetaUrl = 'https://metadata.cloud.getdbt.com/beta/graphql'
-export const ArgsTable = ({ queryName, useBetaAPI }) => {
+
+export const QueryArgsTable = ({ queryName, useBetaAPI }) => {
   const [data, setData] = useState(null)
   useEffect(() => {
     const fetchData = () => {
       fetch(useBetaAPI ? metadataBetaUrl : metadataUrl, {
         method: "POST",
         headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({ query: queriesQuery }),
+        body: JSON.stringify({ query: queryArgsQuery }),
       })
         .then((result) => result.json())
         .then((data) => setData(data))
@@ -45,33 +90,89 @@ export const ArgsTable = ({ queryName, useBetaAPI }) => {
     return <h1>Fetching data...</h1>
   }
   return (
-    <table>
-      <thead>
-        <tr>
-          <td>Field</td>
-          <td>Type</td>
-          <td>Required?</td>
-          <td>Description</td>
-        </tr>
-      </thead>
-      <tbody>
-        {data.data.__schema.queryType.fields.find(d => d.name === queryName).args.map(function ({ name, description, type }) {
-          return (
-            <tr key={name}>
-              <td><code>{name}</code></td>
-              {type.ofType ?
-                <td><code title={type.description}>{type.ofType.name}</code></td> :
-                <td><code title={type.description}>{type.name}</code></td>
+    <ArgsTable name={queryName} data={data.data.__schema.queryType} />
+  )
+}
+
+export const NodeArgsTable = ({ parent, name, useBetaAPI }) => {
+  const [data, setData] = useState(null)
+  useEffect(() => {
+    const fetchData = () => {
+      fetch(useBetaAPI ? metadataBetaUrl : metadataUrl, {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          query: `
+          query {
+            __type(name: "${parent}") {
+              ...FullType
+            }
+          }
+
+          fragment FullType on __Type {
+            kind
+            fields(includeDeprecated: true) {
+              name
+              description
+              args {
+                name
+                description
+                defaultValue
+                type {
+                  ...TypeRef
+                }
               }
-              <td>{type.kind === 'NON_NULL' ? `Yes` : `No`}</td>
-              <td>{description || `No description provided`}</td>
-            </tr>
-          )
-        })}
-      </tbody>
-    </table>
+            }
+          }
+
+          # get several levels
+          fragment TypeRef on __Type {
+            kind
+            name
+            ofType {
+              kind
+              name
+              ofType {
+                kind
+                name
+                ofType {
+                  kind
+                  name
+                  ofType {
+                    kind
+                    name
+                    ofType {
+                      kind
+                      name
+                      ofType {
+                        kind
+                        name
+                        ofType {
+                          kind
+                          name
+                        }
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+        `})
+      })
+        .then((result) => result.json())
+        .then((data) => setData(data))
+    }
+    fetchData()
+  }, [])
+  if (!data) {
+    return <h1>Fetching data...</h1>
+  }
+  return (
+    <ArgsTable name={name} data={data.data.__type} />
   )
 }
+
 export const SchemaTable = ({ nodeName, useBetaAPI }) => {
   const [data, setData] = useState(null)
   useEffect(() => {
@@ -80,27 +181,60 @@ export const SchemaTable = ({ nodeName, useBetaAPI }) => {
         method: "POST",
         headers: { "Content-Type": "application/json" },
         body: JSON.stringify({
-          query: `{
-          __type(name: "${nodeName}") {
-            fields {
+          query: `
+          query {
+            __type(name: "${nodeName}") {
+              ...FullType
+            }
+          }
+
+          fragment FullType on __Type {
+            kind
+            name
+            description
+            fields(includeDeprecated: true) {
               name
               description
-              type { 
-                name 
-                description 
-                kind 
-                ofType { 
-                  name 
-                  description
-                  ofType { 
-                    name 
-                    description
-                  }                   
-                } 
+              type {
+                ...TypeRef
               }
             }
           }
-        }`}),
+
+          # get several levels
+          fragment TypeRef on __Type {
+            kind
+            name
+            ofType {
+              kind
+              name
+              ofType {
+                kind
+                name
+                ofType {
+                  kind
+                  name
+                  ofType {
+                    kind
+                    name
+                    ofType {
+                      kind
+                      name
+                      ofType {
+                        kind
+                        name
+                        ofType {
+                          kind
+                          name
+                        }
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+        `}),
       })
         .then((result) => result.json())
         .then((data) => setData(data))
@@ -124,13 +258,7 @@ export const SchemaTable = ({ nodeName, useBetaAPI }) => {
           return (
             <tr key={name}>
               <td><code>{name}</code></td>
-              {type.kind === 'LIST' ?
-                <td><code title={type.description}>[{type.ofType.ofType ? type.ofType.ofType.name : type.ofType.name}]</code></td> :
-                (type.ofType ?
-                  <td><code title={type.description}>{type.ofType.name}</code></td> :
-                  <td><code title={type.description}>{type.name}</code></td>
-                )
-              }
+              <td><code title={type.description}>{getTypeString(type)}</code></td>
               <td>{description}</td>
             </tr>
           )
@@ -138,4 +266,4 @@ export const SchemaTable = ({ nodeName, useBetaAPI }) => {
       </tbody>
     </table>
   )
-}
\ No newline at end of file
+}
diff --git a/website/sidebars.js b/website/sidebars.js
index a789f27ab2e..e319f4d49bf 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -457,7 +457,51 @@ const sidebarSettings = {
               type: "category",
               label: "Schema",
               items: [
-                "docs/dbt-cloud-apis/discovery-schema-environment",
+                {
+                  type: "category",
+                  label: "Job",
+                  link: { type: "doc", id: "docs/dbt-cloud-apis/discovery-schema-job" },
+                  items: [
+                    "docs/dbt-cloud-apis/discovery-schema-job-model",
+                    "docs/dbt-cloud-apis/discovery-schema-job-models",
+                    "docs/dbt-cloud-apis/discovery-schema-job-metric",
+                    "docs/dbt-cloud-apis/discovery-schema-job-metrics",
+                    "docs/dbt-cloud-apis/discovery-schema-job-source",
+                    "docs/dbt-cloud-apis/discovery-schema-job-sources",
+                    "docs/dbt-cloud-apis/discovery-schema-job-seed",
+                    "docs/dbt-cloud-apis/discovery-schema-job-seeds",
+                    // "docs/dbt-cloud-apis/discovery-schema-job-snapshot",
+                    "docs/dbt-cloud-apis/discovery-schema-job-snapshots",
+                    "docs/dbt-cloud-apis/discovery-schema-job-test",
+                    "docs/dbt-cloud-apis/discovery-schema-job-tests",
+                    "docs/dbt-cloud-apis/discovery-schema-job-exposure",
+                    "docs/dbt-cloud-apis/discovery-schema-job-exposures",
+                    // "docs/dbt-cloud-apis/discovery-schema-job-macro",
+                    // "docs/dbt-cloud-apis/discovery-schema-job-macros",
+                  ],
+                },
+                {
+                  type: "category",
+                  label: "Environment",
+                  link: { type: "doc", id: "docs/dbt-cloud-apis/discovery-schema-environment" },
+                  items: [
+                    {
+                      type: "category",
+                      label: "Applied",
+                      items: [
+                        "docs/dbt-cloud-apis/discovery-schema-environment-applied-modelHistoricalRuns",
+                      ],
+                    },
+                    // Uncomment to add Definition subpage, but need to make items non-empty
+                    // {
+                    //   type: "category",
+                    //   label: "Definition",
+                    //   items: [
+                    //     // insert pages here
+                    //   ],
+                    // },
+                  ],
+                },
                 "docs/dbt-cloud-apis/discovery-schema-model",
                 "docs/dbt-cloud-apis/discovery-schema-models",
                 "docs/dbt-cloud-apis/discovery-schema-modelByEnv",
@@ -932,7 +976,7 @@ const sidebarSettings = {
             "guides/orchestration/airflow-and-dbt-cloud/3-running-airflow-and-dbt-cloud",
             "guides/orchestration/airflow-and-dbt-cloud/4-airflow-and-dbt-cloud-faqs",
           ],
-        }, 
+        },
         {
           type: "category",
           label: "Set up Continuous Integration",
diff --git a/website/snippets/_discovery_api_job_deprecation_notice.md b/website/snippets/_discovery_api_job_deprecation_notice.md
new file mode 100644
index 00000000000..71e80a958b4
--- /dev/null
+++ b/website/snippets/_discovery_api_job_deprecation_notice.md
@@ -0,0 +1,7 @@
+:::caution
+dbt Labs is making changes to the Discovery API. These changes will take effect on September 7, 2023.
+
+The data type `Int` for `id` is being deprecated and will be replaced with `BigInt`. Currently, both data types are supported.
+
+To perform job-based queries, you must do it within the `job` schema object, and move the `jobId` and `runId` arguments to `job(...)`. This is now supported so you can update your API calls accordingly. For details, refer to [Job object schema](/docs/dbt-cloud-apis/discovery-schema-job).
+:::

From 263876659510241bae77c893d0797ca06af0e87b Mon Sep 17 00:00:00 2001
From: Will Sargent <109557847+will-sargent-dbtlabs@users.noreply.github.com>
Date: Tue, 15 Aug 2023 17:16:31 -0600
Subject: [PATCH 689/703] Update 2-how-we-style-our-sql.md

Adds information about custom .sqlfluff and .sqlfluffignore files
---
 .../best-practices/how-we-style/2-how-we-style-our-sql.md     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
index 1ea9c064d74..62c5fcda89b 100644
--- a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
+++ b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
@@ -6,7 +6,9 @@ id: 2-how-we-style-our-sql
 ## Basics
 
 - ☁️ Use [SQLFluff](https://sqlfluff.com/) to maintain these style rules automatically.
-  - Reference this [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use.
+  - Custom `.sqlfluff` configuration files are supported.
+  - Reference this [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use in our own projects.
+  - We also support ignoring files and folders using a standard `.sqlfluffignore` file. Reference these docs for the syntax: [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2)
 - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL.
 - ⏭️ Use trailing commas.
 - 4️⃣ Indents should be four spaces.

From b3a4b96ea57fcb0060ce6bbec40f9809732df067 Mon Sep 17 00:00:00 2001
From: "Eddo W. Hintoso" <eddo.hintoso@dbtlabs.com>
Date: Tue, 15 Aug 2023 18:05:12 -0700
Subject: [PATCH 690/703] Run introspection query with
 fields(includeDeprecated: true)

---
 website/docs/docs/dbt-cloud-apis/schema.jsx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-cloud-apis/schema.jsx b/website/docs/docs/dbt-cloud-apis/schema.jsx
index ea6660251f6..31568671573 100644
--- a/website/docs/docs/dbt-cloud-apis/schema.jsx
+++ b/website/docs/docs/dbt-cloud-apis/schema.jsx
@@ -49,7 +49,7 @@ const metadataBetaUrl = 'https://metadata.cloud.getdbt.com/beta/graphql'
 const queryArgsQuery = `{
   __schema {
     queryType {
-      fields {
+      fields(includeDeprecated: true) {
         name
         type {
           name

From 07ac4e054a34d381ab9b31f1a9f714b6cfb26161 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 09:16:30 -0400
Subject: [PATCH 691/703] Update delete-environment-job.md

adding @patkearns10 's awesome feedback to let users know they need to move jobs if they delete envs
---
 website/docs/faqs/Environments/delete-environment-job.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/faqs/Environments/delete-environment-job.md b/website/docs/faqs/Environments/delete-environment-job.md
index 1f4b8c343a9..eb9ac511a7c 100644
--- a/website/docs/faqs/Environments/delete-environment-job.md
+++ b/website/docs/faqs/Environments/delete-environment-job.md
@@ -28,7 +28,7 @@ Refresh the page, and the deleted job should now be gone. If you want to delete
 
 ## Delete an environment
 
-To delete an environment in dbt Cloud:
+Deleting an environment automatically deletes its associated job(s). If you want to keep those jobs, move them to a different environment first. To delete an environment in dbt Cloud:
 
 1. Click **Deploy** on the navigation header and then click **Environments** 
 2. Select the Environment you want to delete. 

From 47ef3617e43cad90504e211e017ee1533660b116 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 10:14:28 -0400
Subject: [PATCH 692/703] Update
 website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md

---
 .../best-practices/how-we-style/2-how-we-style-our-sql.md       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
index 62c5fcda89b..75250745b81 100644
--- a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
+++ b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
@@ -6,7 +6,7 @@ id: 2-how-we-style-our-sql
 ## Basics
 
 - ☁️ Use [SQLFluff](https://sqlfluff.com/) to maintain these style rules automatically.
-  - Custom `.sqlfluff` configuration files are supported.
+  - Customize `.sqlfluff` configuration files to your needs.
   - Reference this [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use in our own projects.
   - We also support ignoring files and folders using a standard `.sqlfluffignore` file. Reference these docs for the syntax: [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2)
 - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL.

From 0663b5e38d341de058ff1f1b4fb7de29e7ace5da Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 10:15:25 -0400
Subject: [PATCH 693/703] Update
 website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md

---
 .../best-practices/how-we-style/2-how-we-style-our-sql.md      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
index 75250745b81..cbc4e69e4d7 100644
--- a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
+++ b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
@@ -7,7 +7,8 @@ id: 2-how-we-style-our-sql
 
 - ☁️ Use [SQLFluff](https://sqlfluff.com/) to maintain these style rules automatically.
   - Customize `.sqlfluff` configuration files to your needs.
-  - Reference this [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use in our own projects.
+  - Refer to our [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we us in our own projects. 
+
   - We also support ignoring files and folders using a standard `.sqlfluffignore` file. Reference these docs for the syntax: [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2)
 - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL.
 - ⏭️ Use trailing commas.

From e3648a7b7332e50318630a62560286a31ee5e2d9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 10:16:39 -0400
Subject: [PATCH 694/703] Update
 website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md

---
 .../best-practices/how-we-style/2-how-we-style-our-sql.md       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
index cbc4e69e4d7..2845c1cbe91 100644
--- a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
+++ b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
@@ -9,7 +9,7 @@ id: 2-how-we-style-our-sql
   - Customize `.sqlfluff` configuration files to your needs.
   - Refer to our [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we us in our own projects. 
 
-  - We also support ignoring files and folders using a standard `.sqlfluffignore` file. Reference these docs for the syntax: [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2)
+  - Exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2).
 - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL.
 - ⏭️ Use trailing commas.
 - 4️⃣ Indents should be four spaces.

From a89a1fe334e8e7ae31ffcbf2baa17ca065232013 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 10:19:43 -0400
Subject: [PATCH 695/703] Update lint-format.md

---
 website/docs/docs/cloud/dbt-cloud-ide/lint-format.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
index 099641cc22f..77c1089ed96 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
@@ -63,7 +63,7 @@ With the dbt Cloud IDE, you can seamlessly use [SQLFluff](https://sqlfluff.com/)
 
 ### Customize linting
 
-SQLFluff is a configurable SQL linter, which means you can configure your own linting rules instead of using the default linting settings in the IDE.  
+SQLFluff is a configurable SQL linter, which means you can configure your own linting rules instead of using the default linting settings in the IDE. You can exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2).
 
 To configure your own linting rules:
 

From 7882282f5fd78895b834c7be02cf213de42686e7 Mon Sep 17 00:00:00 2001
From: Will Sargent <109557847+will-sargent-dbtlabs@users.noreply.github.com>
Date: Wed, 16 Aug 2023 08:25:04 -0600
Subject: [PATCH 696/703] Grammer fix

"use" instead of "us"
---
 .../best-practices/how-we-style/2-how-we-style-our-sql.md       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
index 2845c1cbe91..9684a498bce 100644
--- a/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
+++ b/website/docs/guides/best-practices/how-we-style/2-how-we-style-our-sql.md
@@ -7,7 +7,7 @@ id: 2-how-we-style-our-sql
 
 - ☁️ Use [SQLFluff](https://sqlfluff.com/) to maintain these style rules automatically.
   - Customize `.sqlfluff` configuration files to your needs.
-  - Refer to our [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we us in our own projects. 
+  - Refer to our [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) for the rules we use in our own projects. 
 
   - Exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2).
 - 👻 Use Jinja comments (`{# #}`) for comments that should not be included in the compiled SQL.

From e2466c9d3e9eab4e634708f4628e4f35b03e7ac9 Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 10:31:50 -0400
Subject: [PATCH 697/703] Update lint-format.md

---
 website/docs/docs/cloud/dbt-cloud-ide/lint-format.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
index 77c1089ed96..60af0961db7 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
@@ -63,7 +63,7 @@ With the dbt Cloud IDE, you can seamlessly use [SQLFluff](https://sqlfluff.com/)
 
 ### Customize linting
 
-SQLFluff is a configurable SQL linter, which means you can configure your own linting rules instead of using the default linting settings in the IDE. You can exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2).
+SQLFluff is a configurable SQL linter, which means you can configure your own linting rules instead of using the default linting settings in the IDE. You can exclude files and directories by using a standard `.sqlfluffignore` file. Learn more about the syntax in the [.sqlfluffignore syntax docs](https://docs.sqlfluff.com/en/stable/configuration.html#id2). 
 
 To configure your own linting rules:
 
@@ -76,7 +76,7 @@ To configure your own linting rules:
 
 :::tip Configure dbtonic linting rules
 
-Use the following code example to incorporate well-written dbt code (or dbtonic) to your linting:
+Refer to the [SQLFluff config file](https://github.com/dbt-labs/jaffle-shop-template/blob/main/.sqlfluff) to add the dbt code (or dbtonic) rules we use for our own projects:
 
 <details>
 <summary>dbtonic config code example provided by dbt Labs</summary>
@@ -122,6 +122,8 @@ capitalisation_policy = lower
 group_by_and_order_by_style = implicit
 ```
 </details>
+
+Refer to [How we style our SQL](/guides/best-practices/how-we-style/2-how-we-style-our-sql) for more info. 
 :::
 
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-sqlfluff-config.jpg" width="95%" title="Customize linting by configuring your own linting code rules, including dbtonic linting/styling."/>

From 2e9ebeae9f9bea422bed63935e786e9b50369e1d Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 11:04:40 -0400
Subject: [PATCH 698/703] Update
 website/docs/docs/cloud/dbt-cloud-ide/lint-format.md

---
 website/docs/docs/cloud/dbt-cloud-ide/lint-format.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
index 60af0961db7..8ffd83ef00e 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/lint-format.md
@@ -123,7 +123,7 @@ group_by_and_order_by_style = implicit
 ```
 </details>
 
-Refer to [How we style our SQL](/guides/best-practices/how-we-style/2-how-we-style-our-sql) for more info. 
+For more info on styling best practices, refer to [How we style our SQL](/guides/best-practices/how-we-style/2-how-we-style-our-sql).
 :::
 
 <Lightbox src="/img/docs/dbt-cloud/cloud-ide/ide-sqlfluff-config.jpg" width="95%" title="Customize linting by configuring your own linting code rules, including dbtonic linting/styling."/>

From 305c92f007cfa98e1ca61d6c76d3e30ff315d94d Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 16 Aug 2023 13:46:32 -0400
Subject: [PATCH 699/703] adding eric and florien's feedback

---
 website/snippets/_cloud-environments-info.md | 24 ++++----------------
 1 file changed, 5 insertions(+), 19 deletions(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 83bcce11836..d815fd4365d 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -48,40 +48,26 @@ For more info, check out this [FAQ page on this topic](/faqs/Environments/custom
 
 :::important This feature is currently in beta
 
-Extended Attributes is currently in beta for select users and is subject to change.
+Extended Attributes is currently in [beta](/docs/dbt-versions/product-lifecycles?) for select users and is subject to change.
 :::
 
 Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings. It provides users with more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.
 
-Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials. It accepts any attributes accepted by a dbt adapter in its `profiles.yml`. 
+Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials, including any custom environment variables. It accepts any YAML attributes accepted by a dbt adapter in its `profiles.yml`. 
 
-However, something to note is that it doesn't mask secret values. We recommend avoiding setting secret values to prevent visibility in the text box and logs.
+However, something to note is that it doesn't mask secret values. We recommend avoiding setting secret values to prevent visibility in the text box and logs. 
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg" width="95%" title="Extended Attributes helps users add profiles.yml attributes to dbt Cloud Environment settings using a free form text box." /> <br />
 
-The **Extended Attributes** text box must contain valid characters only. Any invalid characters will result in an error message. Invalid characters include:
+If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the `profiles.yml` attributes. For each individual attribute:
 
-- `/` (forward slash)
-- `\` (backslash)
-- `$` (dollar sign)
-- `!` (exclamation point)
-- `@` (at sign)
-- `#` (hash) (NEED CONFIRMATION)
-- `+` (plus sign)
-- `%` (percentage sign)
+- If the attribute exists in another source (such as your project settings), it will replace its value in the profile (such as environment-level values). It also overrides any custom environment variables.
 
-If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the profiles.yml attributes. For each individual attribute:
-
-- If the attribute exists in another source (such as your project settings), it will replace its value in the profile.
 - If the attribute doesn't exist, it will add the attribute or value pair to the profile. 
 
 The following code is an example of the types of attributes you can add to the **Extended Attributes** text box:
 
 ```yaml
-type: postgres      
-host: localhost      
-user: alice      
-port: 5432      
 dbname: jaffle_shop      
 schema: dbt_alice      
 threads: 4

From 5f1ad3e26ada74473de0c410b02a8cdab801991f Mon Sep 17 00:00:00 2001
From: Greg McKeon <greg-mckeon@users.noreply.github.com>
Date: Wed, 16 Aug 2023 13:59:23 -0400
Subject: [PATCH 700/703] Add beta feature flag instructions (#3931)

## What are you changing in this pull request and why?
add instructions for cloud cli beta

---------

Co-authored-by: Leona B. Campbell <3880403+runleonarun@users.noreply.github.com>
---
 website/docs/docs/cloud/cloud-cli-installation.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/website/docs/docs/cloud/cloud-cli-installation.md b/website/docs/docs/cloud/cloud-cli-installation.md
index 383f1dc364d..6370c7f2c31 100644
--- a/website/docs/docs/cloud/cloud-cli-installation.md
+++ b/website/docs/docs/cloud/cloud-cli-installation.md
@@ -42,7 +42,7 @@ brew upgrade dbt-cloud-cli
 3. Move to a directory with a dbt project, and create a `dbt_cloud.yml` file containing your `project-id` from dbt Cloud.
 4. Invoke `dbt --help` from your terminal to see a list of supported commands.
 
-#### Updating dbt Cloud installation (Windows + Linux)
+#### Updating your dbt Cloud installation (Windows + Linux)
 
 Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-only) and replace the existing `dbt` executable with the new one. You should not have to go through the security steps again.
 
@@ -52,7 +52,9 @@ Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-
 
 2. Ensure that your personal [development credentials](https://cloud.getdbt.com/settings/profile/credentials) are set on the project.
 
-3. Create an environment variable with your [dbt cloud API key](https://cloud.getdbt.com/settings/profile#api-access):
+3. Navigate to [your profile](https://cloud.getdbt.com/settings/profile) and enable the "beta features" flag under "Experimental Features."
+
+4. Create an environment variable with your [dbt cloud API key](https://cloud.getdbt.com/settings/profile#api-access):
 
 ```bash
 
@@ -65,19 +67,19 @@ Follow the same process in [Installing dbt Cloud CLI](#manually-install-windows-
    
 ```
 
-4. Load the new environment variable. Note: you may need to reactivate your python virtual environment after sourcing your shell's dot file. Alternatively, restart your shell instead of sourcing the shell's dot file
+5. Load the new environment variable. Note: you may need to reactivate your python virtual environment after sourcing your shell's dot file. Alternatively, restart your shell instead of sourcing the shell's dot file
 
 ```bash
    > $ source ~/.zshrc
 ```
 
-5. Navigate to a dbt project
+6. Navigate to a dbt project
 
 ```bash
    > $ cd ~/dbt-projects/jaffle_shop
 ```
 
-6. Ensure that a `dbt_cloud.yml` file exists in the project directory. The file is required to have a `project-id` field with a valid [project ID](#glossary):
+7. Ensure that a `dbt_cloud.yml` file exists in the project directory. The file is required to have a `project-id` field with a valid [project ID](#glossary):
 You can find your project ID by selecting your project and clicking on **documentation** in the navigation bar. Your project ID is the number in the URL: https://cloud.getdbt.com/documentation/1/projects/{project id}.
 ```bash
 > $ pwd
@@ -87,7 +89,7 @@ You can find your project ID by selecting your project and clicking on **documen
 project-id: '123456'
 ```
 
-7. The following commands are supported as of now (we will be adding more in the future):
+8. The following commands are supported as of now (we will be adding more in the future):
 
 - `dbt run`
 - `dbt build`

From 4b407ab29d842f68a10110d1571663aadb3fef40 Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 16 Aug 2023 14:19:19 -0400
Subject: [PATCH 701/703] tweak

---
 website/snippets/_cloud-environments-info.md | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index d815fd4365d..7eeeea489f6 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -53,22 +53,28 @@ Extended Attributes is currently in [beta](/docs/dbt-versions/product-lifecycles
 
 Extended Attributes is a feature that allows users to set a flexible [profiles.yml](/docs/core/connect-data-platform/profiles.yml) snippet in their dbt Cloud Environment settings. It provides users with more control over environments (both deployment and development) and extends how dbt Cloud connects to the data platform within a given environment.
 
-Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials, including any custom environment variables. It accepts any YAML attributes accepted by a dbt adapter in its `profiles.yml`. 
+Extended Attributes is a text box extension at the environment level that overrides connection or environment credentials, including any custom environment variables. You can set any YAML attributes that a dbt adapter accepts in its `profiles.yml`.
 
-However, something to note is that it doesn't mask secret values. We recommend avoiding setting secret values to prevent visibility in the text box and logs. 
+Something to note, Extended Attributes doesn't mask secret values. We recommend avoiding setting secret values to prevent visibility in the text box and logs. 
 
 <Lightbox src="/img/docs/dbt-cloud/using-dbt-cloud/extended-attributes.jpg" width="95%" title="Extended Attributes helps users add profiles.yml attributes to dbt Cloud Environment settings using a free form text box." /> <br />
 
 If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the `profiles.yml` attributes. For each individual attribute:
 
-- If the attribute exists in another source (such as your project settings), it will replace its value in the profile (such as environment-level values). It also overrides any custom environment variables.
+- If the attribute exists in another source (such as your project settings), it will replace its value (such as environment-level values) in the profile. It also overrides any custom environment variables.
 
 - If the attribute doesn't exist, it will add the attribute or value pair to the profile. 
 
-The following code is an example of the types of attributes you can add to the **Extended Attributes** text box:
+The following code is an example of the types of attributes you can add in the **Extended Attributes** text box:
 
 ```yaml
-dbname: jaffle_shop      
-schema: dbt_alice      
-threads: 4
+snowflake:
+  target: dev
+  outputs:
+    dev:
+      type: snowflake
+      dbname: jaffle_shop      
+      schema: dbt_alice      
+      threads: 4
 ```
+

From c74769156f2fca07d66fdd05676cc8db062c536b Mon Sep 17 00:00:00 2001
From: mirnawong1 <mirna.wong@fishtownanalytics.com>
Date: Wed, 16 Aug 2023 14:32:49 -0400
Subject: [PATCH 702/703] add like

---
 website/snippets/_cloud-environments-info.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 7eeeea489f6..67fdc9f0a32 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -61,7 +61,7 @@ Something to note, Extended Attributes doesn't mask secret values. We recommend
 
 If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or [orchestrating job runs](/docs/deploy/deployments), Extended Attributes parses through the provided YAML and extracts the `profiles.yml` attributes. For each individual attribute:
 
-- If the attribute exists in another source (such as your project settings), it will replace its value (such as environment-level values) in the profile. It also overrides any custom environment variables.
+- If the attribute exists in another source (such as your project settings), it will replace its value (like environment-level values) in the profile. It also overrides any custom environment variables.
 
 - If the attribute doesn't exist, it will add the attribute or value pair to the profile. 
 

From 874afbbdcdf773d5df3e9d60775b777525d2311e Mon Sep 17 00:00:00 2001
From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com>
Date: Wed, 16 Aug 2023 14:45:16 -0400
Subject: [PATCH 703/703] Update _cloud-environments-info.md

---
 website/snippets/_cloud-environments-info.md | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/website/snippets/_cloud-environments-info.md b/website/snippets/_cloud-environments-info.md
index 67fdc9f0a32..9311dc25139 100644
--- a/website/snippets/_cloud-environments-info.md
+++ b/website/snippets/_cloud-environments-info.md
@@ -68,13 +68,8 @@ If you're developing in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in
 The following code is an example of the types of attributes you can add in the **Extended Attributes** text box:
 
 ```yaml
-snowflake:
-  target: dev
-  outputs:
-    dev:
-      type: snowflake
-      dbname: jaffle_shop      
-      schema: dbt_alice      
-      threads: 4
+dbname: jaffle_shop      
+schema: dbt_alice      
+threads: 4
 ```