apache/airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Table of contents Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban. Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and…
Read the full README ↗ · Preview checked 2026-10-31T06:40:09.846Z
Inside the original README — Document outline
- Apache Airflow
- Project Focus
- Principles
- Requirements
- Getting started
- Installing from PyPI
- Installation
- Official source code
- Convenience packages
- User Interface
- Semantic versioning
- Version Life Cycle
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Repository facts
- Owner
- apache
- Primary language
- Python
- Stars
- 47,330
- Forks
- 18,248
- Open issues + pull requests
- 1,961
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2015-04-13T18:04:58.000Z
- Last push
- 2026-10-31T11:52:48.000Z
Topics and intended use
Owner-supplied topics: airflow, apache, apache-airflow, automation, dag, data-engineering, data-integration, data-orchestrator, data-pipelines, data-science, elt, etl, machine-learning, mlops, orchestration, python, scheduler, workflow, workflow-engine, workflow-orchestration
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-10-31T12:19:22.904Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
Related repositories
EbookFoundation/free-programming-books
donnemartin/system-design-primer