Cache vs Checkpoint vs Persist: Which Solves What

Three operations get confused because they all hold onto a result, though they solve different problems. The confusion is understandable: cache and persist both keep a result around for reuse, and checkpoint also writes a result to storage. What separates them is what they are FOR, and specifically whether they cut the lineage. cache and persist are about speed of reuse. They keep a computed result in memory, or memory and disk, so that the next action reusing it does not recompute the chain, exactly the re-execution problem from the beginner tier. The key point is that they do NOT cut the lineage. A cached result is held for convenience, and if the cached blocks are evicted under memory pressure or lost with a dead executor, Spark falls back to the lineage and recomputes them. Cache speed

About This Interactive Section

This section is part of the Lineage as Fault Tolerance 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.