Airflow DAG Templates: Airflow Best Practices Guide

Airflow Best Practices Guide A practical guide to designing production Airflow DAGs that are reliable, testable, and maintainable. By Datanest Digital DAG Design Principles 1. Idempotency Every task should be safe to re-run without side effects: Use MERGE / INSERT OVERWRITE instead of INSERT Partition target tables by execution date Use replaceWhere with Delta Lake # Good: idempotent partition overwrite df . write . format ( " delta " ). mode ( " overwrite " ) \ . option ( " replaceWhere " , f " date = ' { ds } '" ) \ . saveAsTable ( " gold.daily_metrics " ) # Bad: append creates duplicates on re-run df . write . format ( " delta " ). mode ( " append " ). saveAsTable ( " gold.daily_metrics " ) 2. Atomicity Each task should do one thing. If a task fails halfway through, the state should be either "not started" or "fully complete" — never partially complete. 3. Small tasks over large monoliths Break large processing into separate extract / transform / quality / load tasks. This gives you