DBT Test

[AkashSonu14]

Hi,
How to orchestrate a custom test in the dbt project in the airflow give dag using cosmos and dbt_project.yml structure

[dosubot]

To orchestrate a custom test in a dbt project using Airflow with a DAG, Cosmos, and the dbt_project.yml structure, follow these steps:

1. Set Up the Environment: Create a virtual environment in your Dockerfile to install dbt. This helps avoid dependency conflicts between dbt and Airflow.

   FROM my-image:latest
   
   # install dbt into a virtual environment
   RUN python -m venv dbt_venv && source dbt_venv/bin/activate && \
       pip install --no-cache-dir <your-dbt-adapter> && deactivate

2. Install Cosmos: Add astronomer-cosmos to your requirements.txt to use Cosmos for running dbt projects as Airflow DAGs.

3. Organize Your dbt Project: Move your dbt project into the dags directory of your Airflow setup. The structure should look like this:

   ├── dags/
   │   ├── dbt/
   │   │   └── my_dbt_project/
   │   │       ├── dbt_project.yml
   │   │       ├── models/
   │   │       │   ├── my_model.sql
   │   │       │   └── my_other_model.sql
   │   │       └── macros/
   │   │           ├── my_macro.sql
   │   │           └── my_other_macro.sql
   │   └── my_cosmos_dag.py
   ├── Dockerfile
   ├── requirements.txt
   └── ...
   

4. Create the DAG: In the my_cosmos_dag.py file, import the DbtDag class from Cosmos and create a new DAG instance. Use the dbt_executable_path to point to the virtual environment.

   import os
   from datetime import datetime
   from cosmos import DbtDag, ProjectConfig, ProfileConfig, ExecutionConfig
   from cosmos.profiles import PostgresUserPasswordProfileMapping
   
   profile_config = ProfileConfig(
       profile_name="default",
       target_name="dev",
       profile_mapping=PostgresUserPasswordProfileMapping(
           conn_id="airflow_db",
           profile_args={"schema": "public"},
       ),
   )
   
   execution_config = ExecutionConfig(
       dbt_executable_path=f"{os.environ['AIRFLOW_HOME']}/dbt_venv/bin/dbt",
   )
   
   my_cosmos_dag = DbtDag(
       project_config=ProjectConfig(
           dbt_project_path=f"{os.environ['AIRFLOW_HOME']}/dags/dbt/my_dbt_project",
       ),
       profile_config=profile_config,
       execution_config=execution_config,
       schedule_interval="@daily",
       start_date=datetime(2023, 1, 1),
       catchup=False,
       dag_id="my_cosmos_dag",
       default_args={"retries": 2},
   )

5. Start Your Project: Use astro dev start to start your project. You should see your Airflow DAG in the Airflow UI, where you can trigger it.

This setup will allow you to orchestrate your dbt project as an Airflow DAG using Cosmos, with the configuration specified in your dbt_project.yml. If you encounter a DagBag import timeout error, consider increasing the core.dagbag_import_timeout value in your Airflow configuration ^[1][2][3].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[tatiana]

@AkashSonu14 some follow up questions?

1. What type of custom test are you trying to run?
2. How would you run locally?
3. Did you face any particular issues when running it using Cosmos?