Orchestration is how you make pipelines reliable. As systems grow, you need retries, backfills, observability, and clear dependencies—without rerunning everything.
The Orchestration Problem
- Data arrives at different times from different sources
- Models retrain on schedules (daily/weekly/monthly)
- Failures cascade unless you isolate stages
- Backfills need to be safe and repeatable
Design Patterns That Scale
- Idempotency: Running a task twice should not create new side effects
- Small tasks: Keep steps composable and independently testable
- Clear contracts: Treat data schemas as APIs between tasks
Tooling Considerations
- Airflow: mature ecosystem and scheduling, higher ops overhead
- Dagster/Prefect: modern developer experience, strong observability
- Kubeflow: Kubernetes-native, good fit for heavy training workloads
What to Monitor
- Pipeline completion time (SLA tracking)
- Stage-specific failure rates
- Data quality and drift checks between stages
- Resource usage and cost
Key insight: The best orchestration tool disappears into the background. If the team fights the tool daily, the system won't scale.