It's designed to programmatically define, schedule, and monitor complex data workflows.
Airflow allows developers to create data pipelines using Python code, defining tasks within Directed Acyclic Graphs (DAGs). It includes a built-in scheduler to run these DAGs at specified intervals and a web UI for visually tracking execution, progress, and logs. You'd typically write `.py` files containing DAG definitions and use commands like `airflow scheduler` and `airflow webserver` to get things running.
It's designed to programmatically define, schedule, and monitor complex data workflows.
Teams managing complex, scheduled data processing jobs or long-running operational workflows will find Airflow invaluable.