ETL & data migration
Pipelines that finish, restart cleanly, and tell you when they lied.
A data pipeline is a chemical process with different units: something enters at a rate, something leaves at a rate, and there is a bottleneck. The failures are the same too — the run that half-completed, the source that changed shape without telling anyone, and the reconciliation nobody does because it was never built.
This covers both the recurring pipeline and the one-off migration of a system from A to B, including the boring part that decides whether it worked: proving the two sides match.
What you get
- Throughput and bottleneck measured before the pipeline is redesigned
- Idempotent, restartable jobs — a failed run costs one rerun, not a weekend
- Reconciliation between source and target, as a check that runs on its own
- Alerting on the failure that matters: silently wrong data, not just a crash