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.

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, …

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 …

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.

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.

The Docker Compose file uses the latest Airflow image (apache/airflow). If you need to install a new Python library or system library, you can customize and extend it.

This tutorial introduces the SQLExecuteQueryOperator, a flexible and modern way to execute SQL in Airflow. We’ll use it to interact with a local Postgres database, which we’ll configure in the Airflow UI.

We decided to keep our dependencies as open as possible (in pyproject.toml) so users can install different versions of libraries if needed. This means that from time to time plain pip install apache …