Apache Airflow ™

The open source standard for workflow orchestration.

Apache Airflow is a way to programmatically author, schedule and monitor your data pipelines using Python and SQL.

Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. Now, it’s widely recognized as the industry’s leading data orchestration solution.

With over 140 integrations and more added each month, the power of the community ensures that Airflow has the most comprehensive coverage of data sources and other providers, whatever your use case.

modern data orchestration pipelines
modern data orchestration pipelines

Built on a strong and growing community.

Github Stars
Github25k+
Slack Community
Slack21k+
Monthly Downloads
Downloads8m+
Contributors
Contributors1.9k+
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Apache Airflow Core Principles

Airflow is built on a set of core ideals that allow you to leverage the most popular open source workflow orchestrator on the market while maintaining enterprise-ready flexibility and reliability.

Flexible

Fully programmatic workflow authoring allows you to maintain full control of the logic you wish to execute.

Extensible

Leverage a robust ecosystem of open source integrations to connect natively to any third party datastore or API.

Open Source

Get the optionality of an open-source codebase while tapping into a buzzing and action-packed community.

Scalable

Scale your Airflow environment to infinity with a modular and highly-available architecture across a variety of execution frameworks.

Secure

Integrate with your internal authentication systems and secrets managers for an platform ops experience that your security team will love.

Modular

Plug into your internal logging and monitoring systems to keep all of the metrics you care about in one place.

Discover new features of Apache Airflow 2.0

  • A New, Highly Available Scheduler

    Expect faster performance with near-zero task latency. Launch Scheduler replicas to increase task throughput and ensure high-availability. Read more about the Airflow 2 scheduler.

  • Full REST API

    Build programmatic services around your Airflow environment with Airflow's new API, now featuring a robust permissions framework. Airflow Docs 

  • Smart Sensors

    Fit Airflow to event-driven paradigms with resource-saving sensors that operate as single, long-running tasks and don't take up a worker slot. Airflow Docs 

  • Task Flow API

    Pass information between tasks with clean, efficient code that's abstracted from the task dependency layer. Includes support for custom XCom backends. Airflow Docs 

  • Task Groups

    Replace SubDAGs with a new way to group tasks in the Airflow UI. Task Groups don't affect task execution behavior and do not limit parallelism. Airflow Docs 

  • Simplified KubernetesExecutor

    Make the most of the pod_override parameter for easy 1:1 overrides and the new yaml pod_template_file, which replaces configs set in airflow.cfg. Airflow Docs 

  • Independent Providers

    Pull the latest version of any Airflow Provider at anytime, or follow an easy contribution process to build your own and install it as a Python package. Airflow Docs 

  • UI/UX Improvements

    Embrace the a new look for the Airflow UI, now boasting a clean, modern color palette and an auto-refresh toggle in the Graph view. Airflow Docs 

Screenshot of Apache Airflow 2.0 UI

What's in Apache Airflow 2.0?

Check out our Airflow 2.0 video series for a walkthrough of all of the great new features.

Resources