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------
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Welcome to ``airflow_dag_artifact`` Documentation
==============================================================================
A lot of serverless AWS Service supports versioning and alias for deployment. It made the blue / green deployment, canary deployment and rolling back super easy.
- `AWS Lambda Versioning and Alias <https://docs.aws.amazon.com/lambda/latest/dg/configuration-versions.html>`_
- `AWS StepFunction Versioning and Alias <https://docs.aws.amazon.com/step-functions/latest/dg/auth-version-alias.html>`_
- `AWS SageMaker Model Registry Versioning <https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html>`_
However, Airflow DAG does not support this feature yet. This library provides a way to manage Airflow DAG versioning and alias so you can deploy Airflow DAG with confidence.
Please `read this tutorial <https://github.com/MacHu-GWU/airflow_dag_artifact-project/blob/main/examples/deploy_versioned_airflow_dag_artifacts.ipynb>`_ to learn how to use this library.
It also has native `AWS MWAA <https://aws.amazon.com/managed-workflows-for-apache-airflow/>`_ support for DAG deployment automation, with the DAG versioning manage, which is not official supported by Apache Airflow. Please `read this example <https://github.com/MacHu-GWU/airflow_dag_artifact-project/blob/main/examples/aws_mwaa.py>`_ to learn how to use this library with AWS MWAA.
.. _install:
Install
------------------------------------------------------------------------------
``airflow_dag_artifact`` is released on PyPI, so all you need is to:
.. code-block:: console
$ pip install airflow-dag-artifact
To upgrade to latest version:
.. code-block:: console
$ pip install --upgrade airflow-dag-artifact
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