About Apache Airflow
Apache Airflow is an open source platform for writing, scheduling and monitoring workflows. It is widely used to run data pipelines, but it can coordinate almost any repeatable job.
What it does
Workflows are written as Python code, which means you can use loops, conditions and standard libraries to generate tasks. The web interface shows the status of each run and the logs for each task, so you can see what happened and when. Airflow uses a message queue to send work to multiple workers, which lets it scale as the number of workflows grows.
Key features
- Workflows defined in Python code
- Web interface for run status and task logs
- Operators for major cloud services and third party tools
- Message queue based workers for scaling
- Custom operators for your own systems
Who it's for
Data engineers, backend teams and machine learning engineers who already write Python and need to schedule jobs reliably. It suits organizations that have many interdependent tasks and want to manage them in code.
Pricing and good to know
Airflow is open source under the Apache 2.0 licence and is free to install. The cost is in running it: you need servers or a managed service, plus people who can operate it. Setting it up well takes some experience with Python and deployment, so plan time for configuration and upgrades.
Highlights
- Write workflows as Python code with dynamic tasks
- Monitor run status and task logs in a web interface
- Ready made operators for major cloud services
- Spread work across multiple workers
- Extend it with custom operators for your systems
