# Backfill Playbook: From Request to Release

> A backfill needs more than a rerun. I use five stages: define the scope, choose a strategy, run by partition, validate the result, and publish atomically.

March 2026 | Pipelines

Tags: Backfill, ETL, Release

Canonical page: https://datasketch.caesarmar.io/sketches/backfill-playbook/

![Data Sketch Episode 1: Backfill Playbook from Request to Release](https://datasketch.caesarmar.io/images/data-sketch/data-sketch-episode-01.jpg)

## Reading the sketch

### Intake

Define the scope first: the tables, date range, and owner.

### Plan

Choose the backfill strategy: recompute, rebuild, or patch.

### Run

Run by partition, track progress, and store the run ID.

### Validate

Check counts and uniqueness, then reconcile the metrics.

### Publish

Publish atomically by swapping tables or rebuilding marts, then monitor the release.

## Takeaway

Treat a backfill like a release: define the scope, control the run, validate the result, and publish only when the gates pass.

## Sources

- [Original LinkedIn post](https://www.linkedin.com/posts/caesarmario_dataengineering-datapipelines-etl-activity-7434768333952020480-jUOO)
- [Mario Caesar](https://caesarmar.io/index.md)