Dimention Data Modeling

What is dimensional modeling?

Dimensional model consists of dimension and fact tables. Fact tables store different transactional measurements and the foreign keys from dimension tables that qualifies the data. The goal of Dimensional model is not to achive high degree of normalization but to facilitate easy and faster data retrieval.
Ralph Kimball is one of the strongest proponents of this very popular data modeling technique which is often used in many enterprise level data warehouses.

The key benefits of dimensional models and data warehouses include ….

•  Separate environment from transactional systems

•  Allows for high-performance of select/read queries

•  Insulated from changes in source systems

•  Intuitive to developers and business users of queries

•  Contains data from multiple source systems

•  Optimized format for data warehouses and data marts

Defination:

Dimensional modeling is a specific discipline for modeling data that is an alternative to entity-relationship (E/R) modeling. A dimension model contains the same information as an E/R model but packages the data in symmetric format whose design goals are user understandability, query performance, and resilience to change.
Ralph Kimball, PhD, The Data Warehousing Lifecycle Toolkit, 1998

Dimensional modeling is a data modeling technique used to support on-line analytical processing (OLAP) systems and is implemented in databases that host either an enterprise data warehouses or data marts. The key point on the design of dimensional models is to resolve questions in the format “measures by dimensions.”  In addition, dimensional models are commonly referred to as star schema as they comprised of a central fact table surrounded by several dimension tables.

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