It uses its autoscaling component to add workers to meet demand until it reaches the maximum number of workers you defined. Azure Data Factory Managed Airflow monitors the workers in your environment. Automatic scaling – Automatically scale Apache Airflow workers by setting the minimum and maximum number of workers in your environment.Azure Data Factory Managed Airflow sets up Apache Airflow for you using the same open-source code you can download on the Internet and provides the same familiar User Interface once set up and launched. Automatic Airflow setup – Quickly set up Apache Airflow by choosing an Apache Airflow version when you create a Managed Airflow environment.With the Azure Data Factory Managed Airflow, you can use Airflow and Python skills to create data workflows without managing the underlying infrastructure for scalability, availability, and security. What are the benefits of using Azure Data Factory Managed Airflow? With Managed Airflow, Azure Data Factory now offers multi-orchestration capabilities across visual, code-centric, OSS orchestration requirements. On the contrary, if you would not like to write/ manage python-based DAGs for data process orchestration, you may prefer to use pipelines.If you have the Apache Airflow background or are currently using Apace Airflow, you may prefer to use the Managed Airflow instead of the pipelines.While Managed Airflow offers Apache Airflow-based python DAGs (python code-centric authoring) for defining the data orchestration process. It brings the best of both worlds (Azure and Apache Foundation) by offering Azure Data Factory's reliability, scale, security, and ease of management, with Apache Airflow's extensibility and community-led updates as a managed offering on Azure.Īzure Data Factory offers Pipelines to orchestrate data processes (UI-based authoring) visually. A DAG is defined in a Python script, representing the DAGs structure (tasks and their dependencies) as code.Īzure Data Factory enables data engineers to bring their existing Apache Airflow workflows / DAGs into ADF that runs on a fully managed Airflow Environment (also referred to as Airflow Integration runtime). In Airflow, a DAG – or a Directed Acyclic Graph – is a collection of all the tasks you want to run, organized to reflect their relationships and dependencies. In addition, it natively integrates Apache Airflow with Azure Active Directory for a single sign-on (SSO) and a more secure solution (instead of requiring basic auth for logins).Īpache Airflow is an open-source tool used to programmatically author, schedule, and monitor sequences of processes and tasks referred to as "workflows." Today, we are excited to announce the capability to run Apache Airflow DAGs (Directed Acyclic Graph) within Azure Data Factory, adding a key Open-Source integration that provides extensibility for orchestrating python-based workflows at scale on Azure.Īzure Data Factory Managed Airflow provides a managed orchestration service for Apache Airflow that simplifies the creation and management of Airflow environments.
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