Data Lake or Warehouse?

Concepts covered: paDataLake

This question tests whether you understand the economics and tradeoffs, not just the definitions. The answer has shifted dramatically since 2022. The interviewer wants to hear you reason about it, not recite a comparison chart. The Traditional Split Data warehouses couple storage and compute into a managed service. You load structured data, it's optimized for SQL analytics, and you pay per query or per compute-second. Data lakes (object storage + Spark) store raw files in any format. You bring your own compute engine and pay for storage + compute separately. The Lakehouse Convergence The lakehouse combines open file formats on object storage with warehouse-grade query performance. The term was coined by one vendor, but every major cloud provider and warehouse moved here. The idea: store ev

About This Interactive Section

This section is part of the The Storage Question: 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.