The Backfill Operation

Concepts covered: paBackfill, paFullVsIncremental

Backfill is the act of running a pipeline over historical date ranges, usually to fix data that was wrong or to populate a new pipeline with history it was not built to capture in real time. Backfill is the operational payoff of idempotency. A pipeline that is idempotent supports backfill almost for free: pass a different date range, run the pipeline, get the right answer. A pipeline that is not idempotent does not support backfill at all; running it on a historical date corrupts whatever data is currently there. The asymmetry is large enough that a single backfill request from finance or product can determine whether the pipelines it touches are operationally healthy or operationally hostile, and that determination shows up in retention numbers for the data engineering team that has to do

About This Interactive Section

This section is part of the Idempotency and Backfill: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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