The DAG: Your Plan as a Graph

When you build a chain of transformations, Spark records it as a DAG, a directed acyclic graph. Directed because the data flows one way, from source to result. Acyclic because it never loops back on itself. Each transformation you wrote is a node, and the edges show how data flows from one operation into the next. The DAG is the concrete form of the plan that laziness let Spark assemble: it is everything you described, captured as a graph, waiting for an action to execute it. The reason the DAG matters, rather than being an internal detail, is that its shape predicts cost. Not all edges in the graph are equal. Some operations let data flow straight through, each output piece built from one input piece, with nothing crossing the network. Others force a reorganisation where data has to be re

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.

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.