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

General Design Review Considerations

Before we discuss the specific issues and potential recommendations for the Figure 11-2 schema, we’ll outline the design issues commonly encountered when conducting design reviews. Not to insinuate that the DW/BI team in this case study has stepped into all these traps, but it may be guilty of violating several. Again, the design review exercise will be a more effective learning tool if you take a moment to jot down your personal ideas regarding Figure 11-2 before proceeding.

Balance Business Requirements and Source Realities

Dimensional models should be designed based on a blended understanding of the business’s needs, along with the operational source system’s data realities. While requirements are collected from the business users, the underlying source data should be profiled. Models driven solely by requirements inevitably include data elements that can’t be sourced. Meanwhile, models driven solely by source system...