LAG and LEAD

Concepts covered: sqlLagLead

LAG and LEAD Fundamentals Understanding the syntax and behavior of these functions is essential before applying them to real analytics scenarios. The Syntax Why Not a Self-Join? Before window functions existed, comparing a row to its predecessor required joining a table to itself. You would match each row to the previous row by offsetting a date or sequence column. This works, but it has real costs. Month-Over-Month Comparison Practical Applications LEAD for Change Detection Detecting Sequential Gaps Larger Offsets Sorting in descending order puts the highest scores at rank 1, which matches the intuitive meaning of "first place" and is the convention used in leaderboards, competitive rankings, and compensation benchmarking tools.

About This Interactive Section

This section is part of the Window Functions: 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.

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.