Apache Airflow is a platform to programmatically author, schedule and monitor workflows. Workflows are defined as Python code, which makes them easier to maintain, version, test and share. The scheduler runs tasks across workers while respecting dependencies, and a web interface shows the status and logs of tasks. Airflow is used for data pipelines, ETL and orchestrating machine-learning workflows, and is geared to batch processing rather than streaming. You install and run it yourself, through PyPI, container images or Kubernetes with Helm.
Airflow is a project of the Apache Software Foundation, a US non-profit, and is licensed under the Apache License 2.0. Development is community-driven, and no single company controls the project. Because you run Airflow yourself, your own infrastructure determines where it runs and where its metadata is stored. Running it on servers or in a cloud located in the EU keeps this orchestration layer in the EU. Where data flows depends on the external services that your workflows connect to.