Book Image

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
5 (1)
Book Image

The Data Warehouse Toolkit - Third Edition

5 (1)
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

Resisting Normalization Urges

In this section, let’s directly confront several of the natural urges that tempt modelers coming from a more normalized background. We’ve been consciously breaking some traditional modeling rules because we’re focused on delivering value through ease of use and performance, not on transaction processing efficiencies.

Snowflake Schemas with Normalized Dimensions

The flattened, denormalized dimension tables with repeating textual values make data modelers from the operational world uncomfortable. Let’s revisit the case study product dimension table. The 300,000 products roll up into 50 distinct departments. Rather than redundantly storing the 20-byte department description in the product dimension table, modelers with a normalized upbringing want to store a 2-byte department code and then create a new department dimension for the department decodes. In fact, they would feel more comfortable if all the descriptors in the original...