"A methodology for logically modeling data for query performance and ease of use that starts from a set of base measurement events. In the relational DBMS environment, a fact table is constructed generally with one record for each discrete measurement. This fact table is then surrounded by a set of dimension tables describing precisely what is known in the context of each measurement record. Because of the characteristic structure of a dimensional model, it is often called a star schema." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)
"A formal data modeling technique that is used to organize and represent data for analytical and reporting use. The focus is on the business perspective and the representation of data." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)
"A generally accepted practice in the data warehouse industry to structure data intended for user access, analysis, and reporting in dimensional data models" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)
"The most frequently used data model for data warehouses." (Jan L Harrington, "Relational Database Design and Implementation" 3rd Ed., 2009)
"With dimensional data modeling or denormalization, data is collapsed, combined, or grouped together. Within dimensional data modeling, the concepts of facts (measures) and dimensions (context) are used. If dimensions are collapsed into single structures, the data model is also often called a star schema. If the dimensions are not collapsed, the data model is called snowflake. The dimensional models are typically seen within data warehouse systems." (Piethein Strengholt, "Data Management at Scale", 2020)
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