Deduplication

Concepts covered: sqlWindowDedup

Data Quality Patterns The most common use of window functions in production is cleaning and deduplicating data. These patterns form the foundation of reliable data pipelines. Dedup with ROW_NUMBER Funnel Analysis Funnel analysis tracks how users progress through a sequence of steps: signup, activation, first purchase, repeat purchase. The goal is to measure drop-off at each stage. Window functions enable ordered funnel tracking per user. This is one of the most requested analytics patterns in product companies, because it directly answers the question: "Where are we losing users?" Gap-and-Island Detection Which real-world pattern matches your analysis goal? Performance at Scale Production workloads require careful attention to memory usage and data distribution. These techniques keep windo

About This Interactive Section

This section is part of the Window Functions: Advanced 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.