1. Dimensional Modeling Business Intelligence Solutions

2. Definition According to Dr. Kimball, Dimensional Modeling is a design technique for databases intended to support end- user queries in a data warehouse.

3. Dimensional Design VS Normalized Design Dimensional Modeling Normalized Modeling Reporting and Analysis Operational Inserts and Updates Data Warehouse Transactional Data Source(s)

4. Transactional System and Normalized Design  Enable consistent and fast writing  Insert or update touch the database in one place  Too complicated for data warehouse queries and performance issue  3NF (third-normal-form) Normalized Design

5. Data Warehouse and Dimensional Design  Star schema: when a fact table is joined to a set of dimension tables  Simplicity  Symmetry  Performance benefits Dimensional Design

6. Dimension Model Elements  Fact Table  Dimension Tables

7. Dimension Model Elements: Fact Table  Stores numerical performance measurements  Contains: o Dimension keys o Facts: business measures  Grain: defined by a list of dimensions  3 categories of fact table grains: o Transaction o Periodic snapshot o Accumulative snapshot Dimensions Facts

8. Dimension Model Elements: Fact Table  Looks ‘thin’ in shape  Primary key: subset of the foreign keys  Expresses a many-to-many relationships

9.  Stores textual descriptors  Contains: attributes  Highly denormalized  Represent hierarchical relationships  Looks ‘fat’ in shape Dimension Model Elements: Dimension Tables

10. Dimension Model: bring Facts and Dimensions together  Surrogate keys: sequentially assigned integers  Benefits: o Environment buffer o Allow data integration o Support SCD (slow changing dimension)

11. Handling Changes: SCD Type 1 SCD: Overwrite the value

12. Handling Changes: SCD Type 2 SCD: Add a Dimension Row

13. Handling Changes: SCD Type 3 SCD: Add a Dimension Column

14. Dimensional Modeling Design Process Choose the Business Process Declare the Grain Identify the Dimensions Identify the Facts

15.  Easy to understand  Optimized for performance  Historical tracking of information  Extensible to accommodate changes Summary

16. Questions?