The Data Warehouse

Concepts covered: paDataWarehouse, paColumnarVsRow

A data warehouse is the storage layer optimized for analytics. The shapes that win in a warehouse are very different from the shapes that win in an operational database. A warehouse stores data column by column rather than row by row. It enforces schema before data is written. It scales compute and storage independently so an analyst can run a thousand-dollar query without buying a thousand-dollar machine. The dominant cloud warehouses in 2026 are Snowflake, Google BigQuery, and Amazon Redshift, with Databricks SQL Warehouse competing in the same lane. Columnar Storage in One Picture An analytical query that computes the total revenue for the last quarter touches two columns: order_date and order_amount. A row-store database has to read every column of every order to find the two it needs.

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.