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Star Schemas and OLAP Cubes

Dimensional models focus on process measurement events, dividing data into either measurements or the “who, what, where, when, why, and how” descriptive context.

Dimensional models can be instantiated in both relational databases, referred to as star schemas, or multidimensional databases, known as online analytical processing (OLAP) cubes. Star schemas characteristically consist of fact tables linked to associated dimension tables via primary/foreign key relationships. OLAP cubes can be equivalent in content to, or more often derived from, a relational star schema. An OLAP cube contains dimensional attributes and facts, but it is accessed via languages with more analytic capabilities than SQL, such as XMLA. OLAP cubes are included in this list of basic techniques because a cube is often the final deployment step of a dimensional DW/BI system, or may exist as an aggregate structure based on a more atomic relational star schema.

The word “Kimball” is synonymous with dimensional modeling. Ralph didn’t invent the original basic concepts of facts and dimensions, however, he established an extensive portfolio of dimensional techniques and vocabulary, including conformed dimensions, slowly changing dimensions, junk dimensions, mini-dimensions, bridge tables, periodic and accumulating snapshot fact tables, and the list goes on. Over the past nearly 30 years,  Ralph and his Kimball Group colleagues have written hundreds of articles and Design Tips on dimensional modeling, as well as the seminal text, The Data Warehouse Toolkit, Third Edition (Wiley, 2013).

While Ralph led the charge, dimensional modeling is appropriate for organizations who embrace the Kimball architecture, as well as those who follow the Corporate Information Factory (CIF) hub-and-spoke architecture espoused by Bill Inmon and others. Dimensional modeling best practices are architecture-neutral.

For a brief overview of dimensional modeling, we suggest starting with the following series of articles. Full coverage is available in The Data Warehouse Toolkit, Third Edition.