Book Image

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
Book Image

The Data Warehouse Toolkit - Third Edition

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

Summary

This chapter was your first exposure to designing a dimensional model. Regardless of the industry, we strongly encourage the four-step process for tackling dimensional model designs. Remember it is especially important to clearly state the grain associated with a dimensional schema. Loading the fact table with atomic data provides the greatest flexibility because the data can be summarized “every which way.” As soon as the fact table is restricted to more aggregated information, you run into walls when the summarization assumptions prove to be invalid. Also it is vitally important to populate your dimension tables with verbose, robust descriptive attributes for analytic filtering and labeling.

In the next chapter we’ll remain within the retail industry to discuss techniques for tackling a second business process within the organization, ensuring your earlier efforts are leveraged while avoiding stovepipes.