Pipeline Ran vs Data Is Good

Concepts covered: paDataQuality, paSilentFailure

Pipelines have two distinct success criteria. One criterion is operational: did the code execute, did the writes commit, did the orchestrator mark the run green. The other criterion is semantic: does the data the pipeline produced actually describe the world correctly. Operational success is necessary but not sufficient for semantic success. The most expensive production incidents in mature data organizations are the ones where operational success and semantic failure coexist, because nobody is alerted until a human notices a number that looks wrong. Internal postmortems at Netflix, Uber, and Stripe across the last decade share the same shape: the pipeline ran, the gates were green, the dashboard updated, and the underlying data was wrong for hours or days before anyone investigated. The s

About This Interactive Section

This section is part of the Data Quality and Contracts: 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.