From a3705c3d2f4da8758df458bc623d209ca4256180 Mon Sep 17 00:00:00 2001 From: Sean McIntyre Date: Wed, 13 Sep 2023 11:21:53 +0200 Subject: [PATCH 1/4] Add update to previous limitation --- website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 a6f3682f9e9..2c89617a90d 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 @@ -115,7 +115,7 @@ In terms of the implementation of the Data Vault itself, we recommend familiariz ### AutomateDV (formerly known as dbtvault) -AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV _expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. +AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. _(Editor's note: As of AutomateDV v0.10.0 this performance issue has been resolved so that users may use the standard incremental configuration.)_ ### datavault4dbt From c079f7bb00008780e076ad21729911a9dd8fafd9 Mon Sep 17 00:00:00 2001 From: Sean McIntyre Date: Wed, 13 Sep 2023 11:27:04 +0200 Subject: [PATCH 2/4] Update 2023-07-03-data-vault-2-0-with-dbt-cloud.md --- website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 2c89617a90d..2c819e7b749 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 @@ -115,7 +115,7 @@ In terms of the implementation of the Data Vault itself, we recommend familiariz ### AutomateDV (formerly known as dbtvault) -AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. _(Editor's note: As of AutomateDV v0.10.0 this performance issue has been resolved so that users may use the standard incremental configuration.)_ +AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. _(Editor's note: As of AutomateDV v0.10.0 this performance issue has been resolved and users may use the standard incremental configuration.)_ ### datavault4dbt From 4e1b1119ac4732b9387e1401a3cd274fa36be818 Mon Sep 17 00:00:00 2001 From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com> Date: Wed, 13 Sep 2023 10:32:16 +0100 Subject: [PATCH 3/4] Update 2023-07-03-data-vault-2-0-with-dbt-cloud.md From 8b8714753a78061bf02b7c9f981c9838bdaba9ff Mon Sep 17 00:00:00 2001 From: mirnawong1 <89008547+mirnawong1@users.noreply.github.com> Date: Wed, 13 Sep 2023 10:38:17 +0100 Subject: [PATCH 4/4] Update website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md --- website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) 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 2c819e7b749..2a4879ac98d 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 @@ -115,7 +115,9 @@ In terms of the implementation of the Data Vault itself, we recommend familiariz ### AutomateDV (formerly known as dbtvault) -AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. _(Editor's note: As of AutomateDV v0.10.0 this performance issue has been resolved and users may use the standard incremental configuration.)_ +AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. + +_(Editor's note: As of AutomateDV v0.10.0, this performance issue has been resolved and users may use the standard incremental configuration.)_ ### datavault4dbt