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Learn about the fundamentals of running Apache Airflow.

icon Schedule DAGs

One of the fundamental features of Apache Airflow is the ability to schedule jobs. Historically, Airflow users scheduled their DAGs by specifying a schedule with a cron expression, a timedelta object, or a preset Airflow schedule. Timetables, released in Airflow 2.2, allow users to create their own custom schedules using Python, effectively eliminating the limitations of cron. With timetables, you can now schedule DAGs to run at any time. Datasets, introduced in Airflow 2.4, let you schedule your DAGs on updates to a dataset rather than a time-based schedule. For more information about datasets, see Datasets and Data-Aware Scheduling in Airflow.