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

Remodeling Existing Data Structures

It’s one thing to conduct a review and identify opportunities for improvement. However, implementing the changes might be easier said than done if the design has already been physically implemented.

For example, adding a new attribute to an existing dimension table feels like a minor enhancement. It is nearly pain-free if the business data stewards declare it to be a slowly changing dimension type 1 attribute. Likewise if the attribute is to be populated starting now with no attempt to backfill historically accurate values beyond a Not Available attribute value; note that while this tactic is relatively easy to implement, it presents analytic challenges and may be deemed unacceptable. But if the new attribute is a designated type 2 attribute with the requirement to capture historical changes, this seemingly simple enhancement just got much more complicated. In this scenario, rows need to be added to the dimension table to capture the historical...