Apache Airflow is a robust orchestration software for scheduling and monitoring workflows, however its behaviour can typically really feel counterintuitive, particularly in terms of information intervals.
Understanding these intervals is essential for constructing dependable information pipelines, making certain idempotency, and enabling replayability. By leveraging information intervals successfully, you’ll be able to assure that your workflows produce constant and correct outcomes, even underneath retries or backfills.
On this article, we’ll discover Airflow’s information intervals intimately, focus on the reasoning behind their design, why they had been launched, and the way they will simplify and improve day-to-day information engineering work.
Knowledge intervals sit at on the coronary heart of how Apache Airflow schedules and executes workflows. Merely put, an information interval represents the particular time vary {that a} DAG run is answerable for processing.
As an illustration, in a daily-scheduled DAG, every information interval begins at midnight (00:00) and ends at midnight the next day (24:00). The DAG executes solely after the info interval has ended, making certain that the info for that interval is full and prepared…