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

Hybrid Slowly Changing Dimension Techniques

In this final section, we’ll discuss hybrid approaches that combine the basic SCD techniques. Designers sometimes become enamored with these hybrids because they seem to provide the best of all worlds. However, the price paid for greater analytic flexibility is often greater complexity. Although IT professionals may be impressed by elegant flexibility, business users may be just as easily turned off by complexity. You should not pursue these options unless the business agrees they are needed to address their requirements.

These final approaches are most relevant if you’ve been asked to preserve the historically accurate dimension attribute associated with a fact event, while supporting the option to report historical facts according to the current attribute values. The basic slowly changing dimension techniques do not enable this requirement easily on their own.

We’ll start by considering a technique that combines type 4...