Edit on github

Update Repository for Airflow

Now that you have configured your Airflow settings you must ensure that your repository has the correct folder structure to pick up the DAGs we create. You will need to add folders to your project repository in order to match the folder defaults we just configured for Airflow. These folders are orchestrate/dags and, optionally, orchestrate/dags_yml_definitions .

Step 1: Add a folder named orchestrate and a folder inside orchestrate named dags . orchestrate/dags is where you will be placing your DAGs as defined earlier in our Airflow settings with the Python DAGs path field.

Step 2: ONLY If using Git Sync . If you have not already done so, create a branch named airflow_development from main . This branch was defined as the sync branch earlier in our Airflow Settings with the Git branch name field. Best practice will be to keep this branch up-to-date with main .

Step 3: This step is optional if you would like to make use of the dbt-coves dbt-coves generate airflow-dags command. Create the dags_yml_definitions folder inside of your newly created orchestrate folder. This will leave you with two folders inside orchestrate - orchestrate/dags and orchestrate/dags_yml_definitions .

Step 4: This step is optional if you would like to make use of the dbt-coves' extension dbt-coves generate airflow-dags command. You must create a config file for dbt-coves. Please follow the generate DAGs from yml docs.

Create a profiles.yml

Upon creating a service connection, environment variables for your warehouse credentials were created to be used in your profiles.yml file and will allow you to safely commit them with git. The available environment variables will vary based on your data warehouse. We have made it simple to set this up by completing the following steps.

To create your and your profiles.yml :

Step 1: Create the automate folder at the root of your project

Step 2: Create the dbt folder inside the automate folder

Step 3: Create the profiles.yml inside of your automate folder. ie) automate/dbt/profiles.yml

Step 4: Copy the following configuration into your profiles.yml


  target: default_target
      type: snowflake
      threads: 8
      client_session_keep_alive: true

      account: "{{ env_var('DATACOVES__MAIN__ACCOUNT') }}"
      database: "{{ env_var('DATACOVES__MAIN__DATABASE') }}"
      schema: "{{ env_var('DATACOVES__MAIN__SCHEMA') }}"
      user: "{{ env_var('DATACOVES__MAIN__USER') }}"
      password: "{{ env_var('DATACOVES__MAIN__PASSWORD') }}"
      role: "{{ env_var('DATACOVES__MAIN__ROLE') }}"
      warehouse: "{{ env_var('DATACOVES__MAIN__WAREHOUSE') }}"


  target: dev
      type: redshift
      host: "{{ env_var('DATACOVES__MAIN__HOST') }}"
      user: "{{ env_var('DATACOVES__MAIN__USER') }}"
      password: "{{ env_var('DATACOVES__MAIN__PASSWORD') }}"
      dbname: "{{ env_var('DATACOVES__MAIN__DATABASE') }}"
      schema: analytics
      port: 5439


  target: dev
      type: bigquery
      method: service-account
      project: GCP_PROJECT_ID
      dataset:  "{{ env_var('DATACOVES__MAIN__DATASET') }}"
      threads: 4 # Must be a value of 1 or greater
      keyfile:  "{{ env_var('DATACOVES__MAIN__KEYFILE_JSON') }}"


  target: dev
      type: databricks
      catalog: [optional catalog name if you are using Unity Catalog]
      schema: "{{ env_var('DATACOVES__MAIN__SCHEMA') }}" # Required
      host: "{{ env_var('DATACOVES__MAIN__HOST') }}" # Required
      http_path: "{{ env_var('DATACOVES__MAIN__HTTP_PATH') }}" # Required
      token: "{{ env_var('DATACOVES__MAIN__TOKEN') }}" # Required Personal Access Token (PAT) if using token-based authentication
      threads: 4 

Getting Started Next Steps

You will want to set up notifications. Selet the option that works best for your organization.