Decomposing the Prompt

Concepts covered: paBatchVsStreaming

When an interviewer says "design a pipeline to ingest clickstream data from our mobile app into our analytics warehouse," they are not asking you to start writing Spark code. They're asking: can you think in layers? The single biggest mistake candidates make is diving into implementation before establishing scope. The Five-Layer Framework Every pipeline decomposes into five layers. Naming them explicitly in the first 60 seconds of your answer signals seniority. Say: "I'll walk through this in five parts: ingestion, transformation, serving, orchestration, and quality." Then pause. Let the interviewer redirect if they want depth on a specific layer. You've just demonstrated that you see the whole system, not just the Spark job. Clarifying Questions for Seniority Before designing anything, as

About This Interactive Section

This section is part of the Design a Pipeline: Intermediate 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.