Picking the Right Storage Shape

Concepts covered: paStorageSelection, paLayeredStorage

Three shapes, three jobs, one rule. The rule is short and worth memorizing: warehouses for queries people read, lakes for raw and bulk, operational databases for the app. Most architectural confusion at junior levels collapses once that rule sits in working memory. The rest of this section unpacks the rule into the questions that select between the three when the choice is not obvious. The Selection Question Tree The tree is not exhaustive. Real architectures often store the same logical data in two of the three layers: raw events in the lake, conformed marts in the warehouse, and a small denormalized copy in the operational database for the application to read. The tree is a starting point, not a verdict. Most workloads land cleanly on one shape; a few span two; very few demand all three.

About This Interactive Section

This section is part of the Storage Layers and Table Formats: Beginner 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.