Computing Percent Change Safely

Concepts covered: sqlCrossJoin

The biggest bug in a beginner period-over-period query is missing periods. The query looks right, the SQL is clean, the numbers in the dashboard look plausible , but a region that had no transactions in February shows March's growth computed against January, not against zero. The bug is silent and the dashboard's number is wrong by an unknown factor. This section is about recognizing the bug and fixing it the right way. The bug, stated plainly, and the fix At Airbnb in 2021, the weekly active host dashboard for a then-new market silently shifted week-over-week growth comparisons every time the market had a holiday-driven booking gap. The query was a clean LAG over a weekly CTE with no date spine; a week of zero activity dropped out of the CTE, and the LAG paired the next active week with t

About This Interactive Section

This section is part of the Period-over-Period: 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 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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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.