Member-only story
Airflow Data Intervals: A Deep Dive
Building idempotent and re-playable data pipelines
Apache Airflow is a powerful orchestration tool for scheduling and monitoring workflows, but its behaviour can sometimes feel counterintuitive, especially when it comes to data intervals.
Understanding these intervals is crucial for building reliable data pipelines, ensuring idempotency, and enabling replayability. By leveraging data intervals effectively, you can guarantee that your workflows produce consistent and accurate results, even under retries or backfills.
In this article, we’ll explore Airflow’s data intervals in detail, discuss the reasoning behind their design, why they were introduced, and how they can simplify and enhance day-to-day data engineering work.
What Are Data Intervals in Airflow?
Data intervals sit at at the heart of how Apache Airflow schedules and executes workflows. Simply put, a data interval represents the specific time range that a DAG run is responsible for processing.
For instance, in a daily-scheduled DAG, each data interval starts at midnight (00:00) and ends at midnight the following day (24:00). The DAG executes only after the data interval has ended, ensuring that the data for that interval is complete and ready…
