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
Title Page
About the Authors
End User License Agreement


In this chapter, we focused exclusively on the customer, beginning with an overview of customer relationship management (CRM) basics. We then delved into design issues surrounding the customer dimension table. We discussed name and address parsing where operational fields are decomposed to their basic elements so that they can be standardized and validated. We explored several other types of common customer dimension attributes, such as dates, segmentation attributes, and aggregated facts. Dimension outriggers that contain a large block of relatively low-cardinality attributes were described.

This chapter introduced the use of bridge tables to handle unpredictable, sparsely populated dimension attributes, as well as multivalued dimension attributes. We also explored several complex customer behavior scenarios, including sequential activities, timespan fact tables, and tagging fact events with indicators to identify abnormal situations.

We closed the chapter by discussing alternative...