Book Image

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
5 (2)
Book Image

The Data Warehouse Toolkit - Third Edition

5 (2)
By: Ralph Kimball, Margy Ross

Overview of this book

The volume of data continues to grow as warehouses are populated with increasingly atomic data and updated with greater frequency. Dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW/BI) systems. The goal of this book is to provide a one-stop shop for dimensional modeling techniques. The book is authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence. The book begins with a primer on data warehousing, business intelligence, and dimensional modeling, and you’ll explore more than 75-dimensional modeling techniques and patterns. Then you’ll understand dimension tables in-depth to get a good grip on retailing and moved towards the topics of inventory. Moving ahead, you’ll learn how to use this book for procurement, order management, accounting, customer relationship management, and many more business sectors. By the end of this book, you’ll be able to gather all the essential knowledge, practices, and patterns for designing dimensional models.
Table of Contents (31 chapters)
Free Chapter
1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

Retail Schema Extensibility

Let’s turn our attention to extending the initial dimensional design. Several years after the rollout of the retail sales schema, the retailer implements a frequent shopper program. Rather than knowing an unidentified shopper purchased 26 items on a cash register receipt, you can now identify the specific shopper. Just imagine the business users’ interest in analyzing shopping patterns by a multitude of geographic, demographic, behavioral, and other differentiating shopper characteristics.

The handling of this new frequent shopper information is relatively straightforward. You’d create a frequent shopper dimension table and add another foreign key in the fact table. Because you can’t ask shoppers to bring in all their old cash register receipts to tag their historical sales transactions with their new frequent shopper number, you’d substitute a default shopper dimension surrogate key, corresponding to a Prior to Frequent Shopper...