Distributed Primitives

Concepts covered: paDistributedPrimitives

What They Want to Hear 'A transformation defines a new dataset from an existing one without executing anything. An action triggers execution and returns a result. Narrow transformations like filter and map process each partition independently. Wide transformations like groupBy and join require data to move between executors, which creates a shuffle.' That is the answer. Narrow = no data movement. Wide = shuffle. This distinction is the foundation of Spark performance.

About This Interactive Section

This section is part of the Distributed Compute: 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.

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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.