Entity Relational Modeling
vs.
Dimensional Modeling

Dimensional Modeling

• It shows that data can be sliced across all dimensions and again it is possible for the data to be aggregated across multiple dimensions. "Sales Dollar" in sales fact table can be calculated across all dimensions independently or in a combined manner which is explained below.
  
  
  • Sales Dollar value for a particular product
    
    
  • Sales Dollar value for a product in a location
    
    
  • Sales Dollar value for a product in a year within a location
    
    
  • Sales Dollar value for a product in a year within a location sold or serviced by an employee

Dimensional Data Modeling

• In Dimensional data modeling, hierarchies for the dimensions are stored in the dimensional table itself.
  
  
  • For example, the location dimension will have all of its hierarchies from country, state, county to city. There is no need for the individual hierarchial lookup like country lookup, state lookup, county lookup and city lookup to be shown in the model.
    
    
• Uses of Dimensional Data Modeling
  Dimensional Data Modeling is used for calculating summarized data. The data can then be referred to as aggregate data. Aggregation is synonymous with summarization, and aggregate data is synonymous with summary data.
  
  
• The performance of dimensional data modeling can be significantly increased when materialized views are used.
  
  
• Materialized view is a pre-computed table comprising aggregated or joined data from fact and possibly dimension tables which also known as a summary or aggregate table.
  
  
• Dimension Table
  Dimension table is one that describe the business entities of an enterprise, represented as hierarchical, categorical information such as time, departments, locations, and products. Dimension tables are sometimes called lookup or reference tables.

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