Data Pipeline Management
Data Pipeline Management (Orchestration)
What is it?
Data Pipeline Management, often referred to as Orchestration, is the control plane responsible for automating, scheduling, and monitoring the flow of data.
It does not process the data itself; rather, it manages the dependencies between tasks. For example, it ensures that “Task B” (Train Model) only starts after “Task A” (Ingest Data) has successfully completed. It handles retries on failure, backfilling historical data, and alerting when pipelines break.
Why is it Important?
- Reliability: Replaces fragile “cron jobs” with robust workflows that handle failures gracefully.
- Dependency Management: Complex data platforms have thousands of interdependent tasks; manual management is impossible.
- Observability: Provides a central dashboard to see the health of all data flows, making debugging significantly easier.
Well-known Solutions
- Code-based (Python): Apache Airflow (Industry Standard), Dagster, Prefect, Mage.
- Simple/Lightweight: Luigi.
- Cloud Managed: AWS Step Functions, Google Cloud Composer (Airflow), Azure Data Factory.