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This course provides an introduction to dimensional modeling in building enterprise and business intelligence systems. Topics include dimensional model design steps, logical data warehouse design techniques, and the anatomy of dimensional models. Students will learn how to identify facts and dimensions and create fact and dimension tables.
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ACCTG 6910Building Enterprise & Business Intelligence Systems(e.bis) Dimensional Modeling I Olivia R. Liu Sheng, Ph.D.Emma Eccles Jones Presidential Chair of Business
TechnicalArchitectureDesign ProductSelection &Installation End-UserApplicationSpecification End-UserApplicationDevelopment The Business Dimensional Lifecycle Business Requirement Definition DimensionalModeling PhysicalDesign Data StagingDesign &Development ProjectPlanning Deployment MaintenanceandGrowth Project Management
Outline • Table structure, types, characteristics and terminology • Design steps • Dimensional models with varying types of fact and dimension tables
Introduction to Dimensional Modeling • A logical data warehouse design technique • Objectives of Dimensional Modeling: • Intuitive: easy to understand and query • High performance OLAP
Anatomy of Dimensional Models • Facts or Fact Tables • Key – uniquely identifies a record • Attributes • Dimensions or Dimension Tables • Keys • Attributes • Connections • Between dimensions and facts • Cardinality: mostly one to many
Fact and Dimension Tables • There are two types of tables in dimensional models: • Fact table: attributes in fact tables are measurements for analysis or main contents in reports. • Dimension table: attributes in dimension tables are constraints for the measurements or headers in reports. Dimensions Facts
Fact and Dimension Fact table Dimension tables
Facts and Dimensions • How to identify facts and dimensions? • Top-down approach (Requirements Analysis): • Report Sales in terms of – total amt, total qty or avg. price • Report Sales by PRODUCT name and/or category name • Report Sales by CUSTOMER name, city and/or or state • Report using a combination of the measures and constraints • Bottom-up approach (Select from meta data of data sources) • Characteristics of fact and dimension attributes