Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.


Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.


Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.


Airflow pipelines are lean and explicit. Parametrizing your scripts is built into its core using the powerful Jinja templating engine.

Pure Python

No more command-line or XML black-magic! Use all Python features to create your workflows including date time formats for scheduling tasks and loops to dynamically generate tasks. This allows you to build your workflows as complicated as you wish.

Useful UI

Monitor, schedule and manage your workflows using web app. No need to learn old, cron-like interfaces. You always have an insight into the status of completed and ongoing tasks along with insight into the logs.

Plenty of integrations

Airflow provides many plug-and-play operators that are ready to handle your task on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other services. This makes Airflow easy to use with your current infrastructure.

Easy to use

Anyone with Python knowledge can deploy a workflow. Apache Airflow does not limit scopes of your pipelines. You can use it for building ML models, transferring data or managing your infrastructure.

Open source

Wherever you want to share your improvement you can do this by opening a PR. It’s simple as that, no barriers, no prolonged procedures. Airflow has many active users who willingly share their experiences. Have any questions? Check our buzzing slack.