Spark Execution Model

Concepts covered: paSparkExecutionModel

What They Want to Hear 'Spark splits work across a cluster. The driver is the coordinator: it plans the work, divides it into tasks, and sends those tasks to executors. Executors are the workers: each one processes a partition of the data in parallel. The key insight is that Spark is lazy. It builds a plan (the DAG) but does not execute anything until you call an action like .count() or .write().' That is the answer. Driver plans, executors execute, nothing happens until an action triggers it. The Vocabulary to Use

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.