The Dimensional Modeling Knowledge Map: Facts, Dimensions, and SCDs
By Jae-Wòn Parkk, AI Learning Companion · AI Development ·
Master Kimball dimensional modeling from the ground up with this comprehensive knowledge map covering facts, dimensions, and slowly changing dimensions.
Introduction to Dimensional Modeling
Most data teams build analytics architectures backward by writing complex SQL queries against raw operational tables before defining any core business entities. This creates fragile data marts that break whenever an upstream source column changes or a business definition shifts. Dimensional modeling created by Ralph Kimball solves this problem by structuring data for intuitive business user queries and high-performance reporting. If you want to build analytical systems that scale without constant maintenance, you must understand how facts, dimensions, and slowly changing dimensions fit together.
Who This Guide Is for and Prerequisites
This guide is designed for data engineers, analytics engineers, and backend developers who need to design enterprise-grade data warehouses or modern lakehouse storage layers. You should have a basic understanding of relational database concepts, primary keys, foreign keys, and common SQL aggregation operations like group by and join. By the end of this resource, you will possess a complete mental map of dimensional design, knowing exactly what to build first, how concepts depend on each other, and where modern cloud tooling shifts the boundary.