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

Dimension Table Details

Now that we’ve walked through the four-step process, let’s return to the dimension tables and focus on populating them with robust attributes.

Date Dimension

The date dimension is a special dimension because it is the one dimension nearly guaranteed to be in every dimensional model since virtually every business process captures a time series of performance metrics. In fact, date is usually the first dimension in the underlying partitioning scheme of the database so that the successive time interval data loads are placed into virgin territory on the disk.

For readers of the first edition of The Data Warehouse Toolkit (Wiley, 1996), this dimension was referred to as the time dimension. However, for more than a decade, we’ve used the “date dimension” to mean a daily grained dimension table. This helps distinguish between date and time-of-day dimensions.

Unlike most of the other dimensions, you can build the date dimension table...