The Retry That Doubled the Rows

Concepts covered: paIdempotencyProblem

Pipelines fail. Networks blink, instances die, upstream APIs return 500s, the warehouse runs out of memory, a credential expires, a Spark executor gets evicted from a spot pool, an S3 bucket policy changes overnight, a DNS record propagates slowly. Failure is not the exception in production data pipelines; it is the background hum. The right response to a failed run is almost always to run it again. The wrong response is to run it again on a pipeline that does not handle being run twice. The wrong response produces duplicate rows, broken aggregates, and revenue charts that lie. Every senior data engineer has at least one story about a duplicate-row incident that was traced back to a routine retry on a pipeline that was never built to be retried, and most have several. The Anatomy of the Fa

About This Interactive Section

This section is part of the Idempotency and Backfill: Beginner 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.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.