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
1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

Lifecycle Roadmap

When driving to a place we’ve never been to before, most of us rely on a roadmap, albeit displayed via a GPS. Similarly, a roadmap is extremely useful if we’re about to embark on the unfamiliar journey of data warehousing and business intelligence. The authors of The Data Warehouse Lifecycle Toolkit drew on decades of experience to develop the Kimball Lifecycle approach. When we first introduced the Lifecycle in 1998, we referred to it as the Business Dimensional Lifecycle, a name that reinforced our key tenets for data warehouse success: Focus on the business’s needs, present dimensionally structured data to users, and tackle manageable, iterative projects. In the 1990s, we were one of the few organizations emphasizing these core principles, so the moniker differentiated our methods from others. We are still very firmly wed to these principles, which have since become generally-accepted industry best practices, but we renamed our approach to be...