Building a Resilient Data Factory

Data Pipeline Design Patterns That Scale

So you’ve built your first Airflow assembly line. You learned that the DAG is the master schedule, operators are the workstations, and dependencies are the conveyor belts. That foundation is essential.

The only problem is that the tutorial DAGs live in a controlled environment. Production doesn’t. Unreliable APIs, late arriving data, and cascading failures are the reality. Your straightforward task_a >> task_b >> task_c pipeline is broken, and task_c has been failing for hours, consuming bad data.

The fundamental concepts haven’t changed, but the stakes have.