Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code. Benefits include:
- Run dbt projects against Airflow connections instead of dbt profiles
- Native support for installing and running dbt in a virtual environment to avoid dependency conflicts with Airflow
- Run tests immediately after a model is done to catch issues early
- Utilize Airflow's data-aware scheduling to run models immediately after upstream ingestion
- Turn each dbt model into a task/task group complete with retries, alerting, etc.
Check out the Quickstart guide on our docs.
You can render an Airflow Task Group using the DbtTaskGroup class. Here's an example with the jaffle_shop project:
from pendulum import datetime
from airflow import DAG
from airflow.operators.empty import EmptyOperator
from cosmos import DbtTaskGroup, ProfileConfig, ProjectConfig
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"},
),
)
with DAG(
dag_id="extract_dag",
start_date=datetime(2022, 11, 27),
schedule_interval="@daily",
):
e1 = EmptyOperator(task_id="pre_dbt")
dbt_tg = DbtTaskGroup(
project_config=ProjectConfig("jaffle_shop"),
profile_config=profile_config,
)
e2 = EmptyOperator(task_id="post_dbt")
e1 >> dbt_tg >> e2This will generate an Airflow Task Group that looks like this:
- Join us on the Airflow Slack at #airflow-dbt
We follow Semantic Versioning for releases. Check CHANGELOG.rst for the latest changes.
All contributions, bug reports, bug fixes, documentation improvements, enhancements are welcome.
A detailed overview an how to contribute can be found in the Contributing Guide.
As contributors and maintainers to this project, you are expected to abide by the Contributor Code of Conduct.