Apache Airflow is a platform for authoring, scheduling, and monitoring data pipelines as Python code. It provides code-defined workflows (DAGs) executed across a worker array with a web interface and CLI for management.
Project overview
It approaches workflow orchestration through dynamic, code-defined DAGs, enabling versioning and collaborative pipeline maintenance alongside built-in visualization.
Project type
Workflow · Data Processing · Infrastructure
Use cases
Documents & Office
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Data teams, operations teams, and enterprise teams needing to programmatically author, schedule, and monitor workflows as Python code.
Users requiring a web GUI and CLI to visualize and troubleshoot production data pipelines.
Key capabilities
Enables users to author workflows as code (DAGs) that orchestrate tasks. This approach makes workflows maintainable, versionable, testable, and collaborative.
Executes scheduled tasks on an array of workers while following specified dependencies.
A rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues.
Provides command line utilities designed to perform complex surgeries on DAGs.
Leverages the Jinja templating engine, allowing rich customizations and dynamic DAG generation.
Includes a wide range of built-in operators and can be extended to fit user needs via providers.
Limitations and risks
Does not offer native Windows support. Users must use WSL2 or Linux Containers.
The system is not designed to function as a streaming solution.
Getting started
Setup difficulty is rated medium because installation requires managing dependencies and constraints. Users need knowledge of Python pip constraint files, and setting up the scheduler and workers requires configuration.
Pip installation can fail or produce unusable installations without constraint files due to open dependencies.
Evidence and sources
README: Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
README: When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
README: The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
README: We know about around 500 organizations that are using Apache Airflow (but there are likely many more)