Data Backfill

A data backfill populates or recomputes a selected historical range. It can initialize a new table, apply a corrected calculation, or repair missed input. Specify the output dates, historical input version, transformation and reference-data versions, and publication plan. A date range alone does not identify a reproducible computation.

Rebuilding March 1–7 for a trailing seven-day feature requires input from February 23 onward. The calculation for March 1 needs the six preceding dates as well. Rebuilding only the output dates as input silently changes the feature. Inventory downstream aggregates and caches so consumers do not combine old derived values with new facts.

Reuse versioned business logic, but limit concurrency and coordinate overlapping daily loads. Publishing an old backfill last can overwrite fresher data even when every transaction is atomic. Per-day commits may expose mixed versions while the range is rebuilt; use a versioned publication boundary when consumers need a consistent range. An orchestrator schedules runs but does not supply missing source history.

Reference: Airflow backfill.