Counting Stages Is Counting Shuffles

The DAG has a structure the Spark UI exposes directly, and it is the most useful thing to understand about reading a job: the graph is divided into stages, and the boundaries between stages fall at the wide operations. A stage is a run of work that needs no data movement, a maximal chain of narrow operations that can all pipeline together. The moment a wide operation appears and data must shuffle, the current stage ends and a new one begins on the far side of the shuffle. This gives you a rule you can apply on sight, and interviewers ask for it constantly: the number of stages equals the number of shuffles plus one. One shuffle splits a job into two stages. Two shuffles, three stages. A job with no wide operations is a single stage. So when you open the Spark UI and see a job broke into fo

About This Interactive Section

This section is part of the Reading the Plan: DAG, Stages, and explain() 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.

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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.