README.md

This is the dbt project for an Analytics Engineer challenge

dbt set up instructions

This repository integrates with dbt, which creates directed acyclic graphs (DAGs) of data models based on their dependencies, and can be used to build or update these tables from source.

Installation

Follow install instructions per the dbt docs:

• Run all the necessary installs (note that you will need to use pip to install the Snowflake adaptor, which this project is built for). Note the section on best practices for installing dbt Core with pip for instructions on how to manage the project with virtual environments

You should now be able to configure your profile.

Configuring ~/.dbt/profile.yml

In order to configure your connection to the Snowflake environment, create a new dbt profile set to authenticate to the project via username and password.

Either add the below template to your existing dbt profile file, or if the file doesn't exist yet, create a blank template as follows:

mkdir ~/.dbt
touch ~/.dbt/profiles.yml

Template:

  synthesia_challenge:
  outputs:
    dev:
      account: <snowflake_account>
      database: analytics
      password: <password>
      role: <your_account_role>
      schema: dev
      threads: 1
      type: snowflake
      user: Synthesia
      warehouse: COMPUTE_WH
  target: dev

Test your connectivity

Now that your profile is configured, you should be able to test your connectivity to Snowflake from the dbt CLI by running a simple dbt model, such as the example model provided:

pipenv run dbt build -s users

You should receive a success message if this has completed successfully, and be able to verify that the example view has been created in your Snowflake dataset.

Now you're ready to go - happy modelling!