Dimensional Modeling - nkreimer/BI GitHub Wiki

Dimensional modeling places and emphasis on the end user’s experience. The goal is to create a data model that performs well and is simple to query.

Dimensional modeling divides the world into measurements and context.

Measurements are usually numeric values; we refer to them as facts. Facts are surrounded by largely textual context that is true at the moment the fact is recorded. This context is intuitively divided into independent logical clumps called dimensions. Dimensions describe the "who, what, when, where, why and how" context of the measurement.

The fact table's grain is the business definition of the measurement event that produces the fact row. Declaring the grain meansu saying exactly what a fact table row represents by filling in the blank in the following phrase: A Fact row is created when ____ occurs.​

Dimensional models stored in a relational database platform are typically referred to as star schemas.

Dimensional models store in multidimensional online analytical processing (OLAP) structures are called cubes​.​