Creating a custom Airflow Python environment (Technical Preview)

To manage job dependencies, Cloudera Data Engineering (CDE) supports creating custom Python environments dedicated to Airflow using the airflow-python-env resource type. With this option, you can install custom libraries for running your Directed Acyclic Graphs (DAGs). The supported version is Python 3.8.

A resource is a named collection of files or other resources referenced by a job. The airflow-python-env resource type allows you to specify a requirements.txt file that defines an environment that you can then activate globally for airflow deployments in a virtual cluster. You can specify any Python package which is compatible with the Airflow python constraints. These constraints can be found at${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt. The Airflow and Python versions depend on your CDE version.