Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.

Apache Airflow® is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow’s extensible Python framework enables you to build workflows connecting with …

This guide will help you quickly set up Apache Airflow using uv, a fast and modern tool for managing Python environments and dependencies. uv makes the installation process easy and provides a …

The Task SDK provides python-native interfaces for defining DAGs, executing tasks in isolated subprocesses and interacting with Airflow resources (e.g., Connections, Variables, XComs, Metrics, …

Tutorials ¶ Once you have Airflow up and running with the Quick Start, these tutorials are a great way to get a sense for how Airflow works.

Users who are familiar with installing and configuring Python applications, managing Python environments, dependencies and running software with their custom deployment mechanisms.

There is a separate constraints file for each version of Python that Airflow supports. You can create the URL to the file substituting the variables in the template below.

In the first tutorial, you built your first Airflow DAG using traditional Operators like PythonOperator. Now let’s look at a more modern and Pythonic way to write workflows using the TaskFlow API — …

One aspect of “public interface” is extending or using Airflow Python classes and functions. The classes and functions mentioned below can be relied on to maintain backwards-compatible signatures and …

Apache Airflow® allows you to define almost any workflow in Python code, no matter how complex. Because of its versatility, Airflow is used by companies all over the world for a variety of use cases.