Data pipelines have many steps that depend on each other. Orchestration tools run them in the right order, on schedule, and handle failures.
What Orchestrators Do
- Scheduling: run pipelines at set times or when triggered by events.
- Dependencies: run each task only after the tasks it depends on succeed, typically expressed as a DAG (directed acyclic graph).
- Retries for transient failures.
- Alerting when something fails.
- Backfills: rerun pipelines for past periods.
- Visibility: logs and history of every run.
Popular Tools
Apache Airflow, Dagster, Prefect and cloud-native schedulers, among others. Some focus on tasks; others model the data assets pipelines produce.
Design Principles
- Idempotent tasks: rerunning a task for the same period gives the same result without duplicating data.
- Small, focused tasks that are easy to retry and debug.
- Parameterised by date so backfills are straightforward.
- Keep logic in code that's tested and version-controlled, with the orchestrator only coordinating.
- Data-aware checks: validate outputs before downstream tasks run.
Monitoring
Track run durations, failures and data freshness. Alert the people who can fix problems, with enough context to act.
Start Simple
For a handful of scripts, a scheduler may be enough. Adopt a full orchestrator as pipelines, dependencies and teams grow.