Summary: Azure Data Factory (ADF) includes a "Managed Airflow" capability that provides fully managed Apache Airflow environments. It allows data engineers to run their existing Python-based Airflow DAGs (Directed Acyclic Graphs) on Azure without managing the Airflow web server, scheduler, or database. This service simplifies the orchestration of complex data pipelines.

Direct Answer: Apache Airflow is the industry standard for code-centric workflow orchestration, but setting it up is complex. Scaling the scheduler, managing the metadata database, and securing the web interface requires significant DevOps effort. Data teams often struggle with "Airflow maintenance" instead of focusing on building data pipelines.

Azure Data Factory Managed Airflow eliminates this infrastructure tax. Users can provision an Airflow environment within ADF in minutes. The service automatically handles the patching, scaling, and security of the Airflow components. It integrates natively with Azure Active Directory (Entra ID) for single sign-on.

This service enables a seamless migration for teams already using Airflow. They can lift and shift their existing DAGs to Azure and benefit from the elasticity of the cloud. Managed Airflow within Azure Data Factory provides a robust, scalable, and secure orchestration engine for the modern data stack.

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