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.
Example Usage#
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
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="@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 >> e2
This will generate an Airflow Task Group that looks like this:
Getting Started#
To get started now, check out the Getting Started Guide.
Changelog#
We follow Semantic Versioning for releases. Check CHANGELOG.rst for the latest changes.
Contributing Guide#
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.