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

Order Transactions

The natural granularity for an order transaction fact table is one row for each line item on an order. The dimensions associated with the orders business process are order date, requested ship date, product, customer, sales rep, and deal. The facts include the order quantity and extended order line gross, discount, and net (equal to the gross amount less discount) dollar amounts. The resulting schema would look similar to Figure 6-2.


Figure 6-2: Order transaction fact table.

Fact Normalization

Rather than storing the list of facts in Figure 6-2, some designers want to further normalize the fact table so there’s a single, generic fact amount along with a dimension that identifies the type of measurement. In this scenario, the fact table granularity is one row per measurement per order line, instead of the more natural one row per order line event. The measurement type dimension would indicate whether the fact is the gross order amount, order discount amount...