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

Conformed Facts

Thus far we have considered the central task of setting up conformed dimensions to tie dimensional models together. This is 95 percent or more of the data architecture effort. The remaining 5 percent of the effort goes into establishing conformed fact definitions.

Revenue, profit, standard prices and costs, measures of quality and customer satisfaction, and other key performance indicators (KPIs) are facts that must also conform. If facts live in more than one dimensional model, the underlying definitions and equations for these facts must be the same if they are to be called the same thing. If they are labeled identically, they need to be defined in the same dimensional context and with the same units of measure from dimensional model to dimensional model. For example, if several business processes report revenue, then these separate revenue metrics can be added and compared only if they have the same financial definitions. If there are definitional differences, then...