Designing Backfills and Replays

When to use

• Loading history for a new model/pipeline.
• Reprocessing past windows after fixing a transformation bug.
• Replaying events or re-deriving a table from raw.
• Do NOT use for normal incremental runs (use building-dbt-models / authoring-airflow-dags).

Why it deserves care

Backfills are the most dangerous routine operation in data engineering: they rewrite history. Done wrong, they double-count, change yesterday's numbers, or overload production. Done right, they are boring and repeatable. The prerequisite is idempotency (see writing-idempotent-transformations).

Workflow

- [ ] Confirm the transform is idempotent (rerun == run once) BEFORE backfilling
- [ ] Define the exact window and partition granularity
- [ ] Dry-run one partition; verify counts/sums vs source
- [ ] Run partition-by-partition (bounded parallelism), not all at once
- [ ] Isolate backfill compute from production workloads
- [ ] Verify totals and reconcile; then resume normal scheduling

1. Prove idempotency first. If re-running a window can change results, fix that before touching history. Backfilling a non-idempotent job multiplies data.
2. Scope precisely. Exact start/end and the partition unit (day/hour/region).
3. Dry-run one partition and reconcile against source before scaling out.
4. Chunk the run. Process partitions in bounded batches so you can monitor, pause, and resume — never one giant unbounded job.
5. Isolate compute. Use a separate warehouse/cluster/pool so the backfill doesn't starve production SLAs.
6. Verify + resume. Reconcile totals over the backfilled range, then hand back to the normal schedule.

Patterns

Idempotent per-partition backfill — each partition overwrite is independent and safe to retry:

# Bounded parallelism, one day at a time; each day overwrites its own partition.
for day in $(seq_dates 2026-01-01 2026-01-31); do
  run_transform --run-date "$day"   # delete-insert / MERGE for that day only
done

Airflow — catchup/backfill reruns intervals; only safe when tasks are idempotent and parameterized by the data interval. dbt — filter the incremental model to the target window, or --full-refresh a bounded window.

Isolate + throttle — dedicated warehouse/cluster and a concurrency cap so the backfill can't degrade live dashboards.

Common pitfalls

• Backfilling a non-idempotent job — duplicates or shifting totals; make it idempotent first.
• One giant unbounded run — can't monitor or resume, and it overloads production; go partition-by-partition.
• Sharing production compute — backfills spike load and blow SLAs; isolate.
• No verification — assuming success; reconcile counts/sums over the range.
• Changing logic mid-backfill — inconsistent history; pin the code version for the whole run.
• Forgetting downstream — backfilling a base table without refreshing dependent marts/aggregates leaves them inconsistent.