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
Title Page
About the Authors
End User License Agreement

Retail Schema in Action

With our retail POS schema designed, let’s illustrate how it would be put to use in a query environment. A business user might be interested in better understanding weekly sales dollar volume by promotion for the snacks category during January 2013 for stores in the Boston district. As illustrated in Figure 3-12, you would place query constraints on month and year in the date dimension, district in the store dimension, and category in the product dimension.


Figure 3-12: Querying the retail sales schema.

If the query tool summed the sales dollar amount grouped by week ending date and promotion, the SQL query results would look similar to those below in Figure 3-13. You can plainly see the relationship between the dimensional model and the associated query. High-quality dimension attributes are crucial because they are the source of query constraints and report labels. If you use a BI tool with more functionality, the results would likely appear as a...