Orchestrate your dbt projects in Airflow
Project description
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
Check out the Getting Started guide on our docs. See more examples at /dev/dags and at the cosmos-demo repo.
Example Usage
You can render a Cosmos Airflow DAG using the DbtDag class. Here’s an example with the jaffle_shop project:
This will generate an Airflow DAG that looks like this:
Community
Join us on the Airflow Slack at #airflow-dbt
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.
License
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for astronomer_cosmos-1.5.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d73f1a9f042f1e8ee09bd9e4e4f0acb46567e3ccf65a71eecd9a7249262e602 |
|
MD5 | 80752fc69ac0a6d3ae142cc86162ca5e |
|
BLAKE2b-256 | 4489497e3633a522a239bfaf3dceba59f70f09970c6d44615f471adcf1fb73fc |