One Big Table (OBT)

Concepts covered: dmOneBigTable

Flatten Everything Into One Table One Big Table (OBT) is exactly what it sounds like: pre-join all your dimensions into the fact table and serve everything from a single wide table. No JOINs at query time. Every column an analyst might need is on the same row. Simple, fast, and easy to understand. OBT is a consumption pattern, not a source-of-truth pattern. You build the OBT from normalized source tables. The OBT is a derived, denormalized view optimized for a specific set of queries. If the source data changes, you rebuild the OBT. OBT works well for BI tools that struggle with JOINs (some Tableau configurations, Google Sheets connected to BigQuery). It also works for ML feature tables where the model needs a flat input. It does not work well as a general-purpose analytical model because

About This Interactive Section

This section is part of the Design Patterns 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.