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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.
Quickstart
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Check out the Getting Started guide on our `docs `_. See more examples at `/dev/dags `_ and at the `cosmos-demo repo `_.
Example Usage
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You can render a Cosmos Airflow DAG using the ``DbtDag`` class. Here's an example with the `jaffle_shop project `_:
..
This renders on Github but not Sphinx:
https://github.com/astronomer/astronomer-cosmos/blob/24aa38e528e299ef51ca6baf32f5a6185887d432/dev/dags/basic_cosmos_dag.py#L1-L42
This will generate an Airflow DAG that looks like this:
.. figure:: /docs/_static/jaffle_shop_dag.png
Community
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- Join us on the Airflow `Slack `_ at #airflow-dbt
Changelog
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We follow `Semantic Versioning `_ for releases.
Check `CHANGELOG.rst `_
for the latest changes.
Contributing Guide
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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 `_.
License
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`Apache License 2.0 `_