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

Dimensional Modeling Myths

Despite the widespread acceptance of dimensional modeling, some misperceptions persist in the industry. These false assertions are a distraction, especially when you want to align your team around common best practices. If folks in your organization continually lob criticisms about dimensional modeling, this section should be on their recommended reading list; their perceptions may be clouded by these common misunderstandings.

Myth 1: Dimensional Models are Only for Summary Data

This first myth is frequently the root cause of ill-designed dimensional models. Because you can’t possibly predict all the questions asked by business users, you need to provide them with queryable access to the most detailed data so they can roll it up based on the business question. Data at the lowest level of detail is practically impervious to surprises or changes. Summary data should complement the granular detail solely to provide improved performance for common queries...