Mastering Data Warehouse Design: Relational and Dimensional Techniques / Edition 1 by Claudia Imhoff, Jonathan G. Geiger, Nicholas Galemmo | | 9780471324218 | Paperback | Barnes & Noble
Mastering Data Warehouse Design: Relational and Dimensional Techniques / Edition 1

Mastering Data Warehouse Design: Relational and Dimensional Techniques / Edition 1

5.0 5
by Claudia Imhoff, Jonathan G. Geiger, Nicholas Galemmo
     
 

ISBN-10: 0471324213

ISBN-13: 9780471324218

Pub. Date: 08/08/2003

Publisher: Wiley

  • A cutting-edge response to Ralph Kimball's challenge to the data warehouse community that answers some tough questions about the effectiveness of the relational approach to data warehousing
  • Written by one of the best-known exponents of the Bill Inmon approach to data warehousing
  • Addresses head-on the tough issues raised by Kimball and

Overview

  • A cutting-edge response to Ralph Kimball's challenge to the data warehouse community that answers some tough questions about the effectiveness of the relational approach to data warehousing
  • Written by one of the best-known exponents of the Bill Inmon approach to data warehousing
  • Addresses head-on the tough issues raised by Kimball and explains how to choose the best modeling technique for solving common data warehouse design problems
  • Weighs the pros and cons of relational vs. dimensional modeling techniques
  • Focuses on tough modeling problems, including creating and maintaining keys and modeling calendars, hierarchies, transactions, and data quality

Product Details

ISBN-13:
9780471324218
Publisher:
Wiley
Publication date:
08/08/2003
Edition description:
New Edition
Pages:
456
Product dimensions:
7.44(w) x 9.16(h) x 1.00(d)

Related Subjects

Table of Contents

Acknowledgments.

About the Authors.

PART ONE: CONCEPTS.

Chapter 1. Introduction.

Chapter 2. Fundamental Relational Concepts.

PART TWO: MODEL DEVELOPMENT.

Chapter 3. Understanding the Business Model.

Chapter 4. Developing the Model.

Chapter 5. Creating and Maintaining Keys.

Chapter 6. Modeling the Calendar.

Chapter 7. Modeling Hierarchies.

Chapter 8. Modeling Transactions.

Chapter 9. Data Warehouse Optimization.

PART THREE: OPERATION AND MANAGEMENT.

Chapter 10. Accommodating Business Change.

Chapter 11. Maintaining the Models.

Chapter 12. Deploying the Relational Solution.

Chapter 13. Comparison of Data Warehouse Methodologies.

Glossary.

Recommended Reading.

Index.

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Mastering Data Warehouse Design: Relational and Dimensional Techniques 5 out of 5 based on 0 ratings. 5 reviews.
Guest More than 1 year ago
An excellent book for all Corporate Information Factory and Data Warehouse Designers and Devlopers. It provides detailed, practical information that provides insight and guidance or solving many problems or issues encountered in designing or building Data Warehouses. Blends the two different Data Warehouses methods well and shows how both can be best used in building the Corporate Information or Data Warehouse. A must read for all individuals involved in designing or building Data Warehouses. Highly recommended for the 'hands on' techniques to resolve issues that one will encounter in designing and building Data Warehouses
Guest More than 1 year ago
'Mastering Data Warehouse Design' is an excellent book to help readers understand how to take maximum advantage of the strengths of diverse approaches associated with Bill Inmon and Ralph Kimball. The main reason I bought a copy of this book, even before it arrived in bookstores, was that I was leading a team to figure out how to merge Inmon and Kimball views for data modelling standards. We had already developed a DW architecture using Inmon's approach, with its associated relational/ERD method, but believed that it lacked rigour in the area of data marts. We also reviewed Kimball's books, and acknowledged the strengths of his dimensional modelling approaches, but were concerned that it lacked rigour for the diversity of analytical requirements in the manufacturing environment, e.g. data exploration/mining on a massive scale. We were struggling to figure out to combine the best of both - and then we discovered the imminent release of 'Mastering Data Warehouse Design'. After checking the Table of Contents on the publisher's web site, we had the book couriered directly from the publishers warehouse because it would not be available in local bookstores fast enough to meet our work schedule. Chapter 1 has an impressive 'sound bite' version of Inmon's DW architecture thinking, but extended to include broader Business Intelligence concepts. Chapter 2 does a commendable job of explaining a tiered approach to data models, e.g. subject area model, business model, Operational system model, DW model. At first, this chapter was confusing because we had just finished a rigourous definition of data modelling standards, using more conventional terminology, e.g. logical/entity model, physical/table model. So the book's terminology didn't seem to fit in with our thinking. But after re-reading it, we realized that it added value in forcing us to look at the whole issue of modelling from a deliverables or outcomes perspective, rather than a modelling process perspective. Chapter 4 discusses how to develop a DW data model. The content outlines the sequence or steps involved in developing a DW data model, and it's rare that I've been able to find as good coverage of the topic as I found in this chapter. Chapters 5 - 11 cover topics like keys, modelling time/hierarchies/transactions, with some solid content on how to model for on-going business change and how to maintain the tiered models. However, I'm not fully conversant with some of these topics, so am not in a good position to evaluate their content. Chapter 12 has a very good discussion on how to deal with a proliferation of legacy data marts, and strategies for migrating to a central DW that feeds a variety of data marts. It also introduces Chapter 13 which has a classic discussion on comparing the relational and dimensional modelling approaches - including the best discussion I've ever seen on the strengths and weaknesses of each approach. While our team didn't buy into all this chapter's points, the clear logical explanation of strengths and weaknesses helped facilitate a consensus agreement among two groups aligned with the Inmon/relational and Kimball/dimensional approaches. The consensus solution, mostly based on Chapter 13's content, would have been difficult to achieve without this book, i.e. chapter 13's content alone was worth much more than the price of the book. So if you're struggling with the merits of the Inmon and Kimball architecture/modelling approaches, this book is a valuable resource to help take advantage of the best of both.
Guest More than 1 year ago
A refreshing power-packed package. Shows full spectrum of (infrastructure concepts) what you need to do right for (DW/BI) efficiency, with integrated discussion of how to do it in the real world (Expectations setting, pitfalls, organizational considerations, financial sponsorship).
Guest More than 1 year ago
Spans the entire spectrum of technical and management considerations in DW design. It is a brilliant blueprint for to do's and what not to-do. A practical guide, text book, and reference.
Guest More than 1 year ago
This book provides a lot of practical and useful advice for modeling and implementing a complex data warehouse environment. Use of realistic case studies helps in understanding the reason why things are done the way they are.