CHAPTER 10
PRINCIPLES OF DIMENSIONAL MODELING
CHAPTER OBJECTIVES
- Clearly unde rstand how the requirements definition determines data design
- Introduce dimensional modeling and contrast it with entity-relationship modeling
- Review the basics of the STAR schema
- Find out what is inside the fact table and inside the dimension tables
- Determine the advantages of the STAR schema for data warehouses
- Review examples of the STAR schema
FROM REQUIREMENTS TO DATA DESIGN
The requirements definition completely drives the data design for the data warehouse. Data design consists of putting together the data structures. A group of data elements form a data structure. Logical data design includes determination of the various data elements that are needed and combination of the data elements into structures of data. Logical data design also includes establishing the relationships among the data structures.
Let us look at Figure 10-1. Notice how the phases start with requirements gathering. The results of the requirements gathering phase is documented in detail in the requirements definition document. An essential component of this document is the set of information package diagrams. Remember that these are information matrices showing the metrics, business dimensions, and the hierarchies within individual business dimensions.
The information package diagrams form the basis for the logical data design for a data warehouse made up of data marts. The data design process results in a dimensional data ...
Get Data Warehousing Fundamentals for IT Professionals now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
