Pipeline DAG Design

Structuring Pipeline Dependencies A pipeline DAG (Directed Acyclic Graph) defines the execution order of your data transformations. Table A depends on Table B. Table B depends on Table C. The DAG ensures C runs before B, and B runs before A. Getting the DAG wrong means either: tables run before their dependencies are ready (wrong data), or the entire pipeline serializes unnecessarily (slow). The DAG should mirror the medallion layers. Bronze tasks (ingestion) have no dependencies on each other. Silver tasks depend on their bronze source. Gold tasks depend on their silver inputs. No gold task should depend directly on bronze. No task should run backward through the layers. DAG Design Principles Common DAG Anti-Patterns The practical test: draw your DAG on a whiteboard. If it looks like a ta

About This Interactive Section

This section is part of the Design Patterns 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.