The Star Schema

Concepts covered: dmStarSchema, dmSnowflakeSchema

Facts in the Center, Dimensions Around Them A star schema has one table in the middle (the fact table) surrounded by several tables around it (dimension tables). The fact table stores measurable events: a sale happened, a click occurred, a payment was processed. Dimension tables store the descriptive context: who (customer), what (product), when (date), where (store). The fact table is tall and narrow: billions of rows, each with a few FK columns pointing to dimensions plus a few numeric measure columns. Dimension tables are short and wide: thousands to millions of rows, each with many descriptive columns. The star shape emerges because every dimension connects to the central fact. Queries always start from the fact table and join outward to dimensions for filtering and grouping: 'total sa

About This Interactive Section

This section is part of the Star Schemas 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.