orchestration-and-backfills

Orchestration And Backfills

Overview

Reliable data systems are shaped as much by rerun behavior as by transformation logic. This skill ensures schedules, retries, and backfills are deliberate, safe, and reversible.

When to Use

• adding or changing a scheduled pipeline
• designing retries or failure handling
• introducing a new dependency chain
• reprocessing historical data
• modifying publish windows or cutover behavior

Workflow

1. Define execution semantics. Specify:

   • schedule or trigger type
   • watermark behavior
   • late-arriving data policy
   • idempotency guarantees
   • retry rules
   • failure notification path

2. Separate normal runs from backfills. Historical reprocessing should not silently behave like daily incremental runs unless that has been proven safe. For /backfill or publish-bound replay, load safe-backfill-and-replay-orchestration first and complete templates/backfill-plan.yaml before execution.

3. Design the recovery path before rollout. Include:

   • restart behavior
   • partial failure handling
   • duplicate prevention
   • publish gating
   • rollback or pause steps

4. Estimate cost and blast radius. Backfills can overload warehouses, queues, clusters, or downstream consumers.

5. Prove the run strategy. Use a dry run, limited slice, or non-production environment when possible.

Common Rationalizations

Rationalization	Reality
"The scheduler will handle retries for us."	Default retries may duplicate writes or hide real data issues.
"Backfill is just rerunning the job for older dates."	Historical loads often need different concurrency, checks, and cutover rules.
"We can figure out rollback during the incident."	Recovery plans created during outage pressure are usually incomplete.

Red Flags

• no idempotency strategy is documented
• backfill and incremental logic are conflated
• downstream consumers are not considered during replay
• recovery steps are absent from the plan

Verification

• Execution, retry, and replay rules are documented
• Backfill behavior is explicitly designed and bounded
• A recovery path exists for partial or failed runs
• Rollout risk and cost impact have been considered