The Data Warehouse Lifecycle Toolkit, Second Edition 9780470149775pdf/txt下载_在线阅读全文

内容简介

A thorough update to the industry standard for designing,developing, and deploying data warehouse and business intelligencesystemsThe world of data warehousing has changed remarkably since thefirst edition of The Data Warehouse Lifecycle Toolkit waspublished in 1998. In that time, the data warehouse industry hasreached full maturity and acceptance, hardware and software havemade staggering advances, and the techniques promoted in thepremiere edition of this book have been adopted by nearly all datawarehouse vendors and practitioners. In addition, the term"business intelligence" emerged to reflect the mission of the datawarehouse: wrangling the data out of source systems, cleaning it,and delivering it to add value to the business.
Ralph Kimball and his colleagues have refined the original setof Lifecycle methods and techniques based on their consulting andtraining experience. The authors understand first-hand that a datawarehousing/business intelligence (DW/BI) system needs to change asfast as its surrounding organization evolves. To that end, theywalk you through the detailed steps of designing, developing, anddeploying a DW/BI system. You'll learn to create adaptable systemsthat deliver data and analyses to business users so they can makebetter business decisions.

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<div>A thorough update to the industry standard for designing,
developing, and deploying data warehouse and business intelligence
systems
The world of data warehousing has changed remarkably since the
first edition of The Data Warehouse Lifecycle Toolkit was
published in 1998. In that time, the data warehouse industry has
reached full maturity and acceptance, hardware and software have
made staggering advances, and the techniques promoted in the
premiere edition of this book have been adopted by nearly all data
warehouse vendors and practitioners. In addition, the term
"business intelligence" emerged to reflect the mission of the data
warehouse: wrangling the data out of source systems, cleaning it,
and delivering it to add value to the business.
Ralph Kimball and his colleagues have refined the original set
of Lifecycle methods and techniques based on their consulting and
training experience. The authors understand first-hand that a data
warehousing/business intelligence (DW/BI) system needs to change as
fast as its surrounding organization evolves. To that end, they
walk you through the detailed steps of designing, developing, and
deploying a DW/BI system. You'll learn to create adaptable systems
that deliver data and analyses to business users so they can make
better business decisions.
With substantial new and updated content, this second edition of
The Data Warehouse Lifecycle Toolkit again sets the standard
in data warehousing for the next decade. It shows you how to:
<ul>
<li>Identify and prioritize data warehouse opportunities</li>
<li>Create an architecture plan and select products</li>
<li>Design a powerful, flexible, dimensional model</li>
<li>Build a robust ETL system</li>
<li>Develop BI applications to deliver data to business users</li>
<li>Deploy and sustain a healthy DW/BI environment
The authors are members of the Kimball Group. Each has focused
on data warehousing and business intelligence consulting and
education for more than 15 years; most have written other books in
the Toolkit series. Learn more about the Kimball Group and Kimball
University at www.kimballgroup.com.
This book is also available as part of the Kimball's Data
Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with
the following 3 books:
 
</li>
<li>The Data Warehouse Toolkit, 2nd Edition
(9780471200246)</li>
<li>The Data Warehouse Lifecycle Toolkit, 2nd Edition
(9780470149775)</li>
<li>The Data Warehouse ETL Toolkit (9780764567575)</li>
</ul>
</div>
</div>
</div>

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作者简介

The authors’ professional careers have followed remarkablysimilar paths. Each author has focused on data warehousing andbusiness intelligence (DW/BI) consulting and education for morethan fifteen years. Most worked together at Metaphor ComputerSystems, a pioneering decision support vendor, in the 1980s. Allthe authors are members of the Kimball Group and teach forKimballUniversity. They contribute regularly to IntelligentEnterprise magazine and other industry publications; most havepreviously written books in the Toolkit series.
Ralph Kimball founded the Kimball Group. Since the mid1980s, he has been the DW/BI industry’s thought leader on thedimensional approach and trained more than 10,000 IT professionals.Ralph has his Ph.D. in Electrical Engineering from StanfordUniversity.
Margy Ross is President of the Kimball Group. She hasfocused exclusively on DW/BI since 1982 with an emphasis onbusiness requirements analysis and dimensional modeling. Margygraduated with a BS in Industrial Engineering from NorthwesternUniversity.

The authors’ professional careers have followed remarkably
similar paths. Each author has focused on data warehousing and
business intelligence (DW/BI) consulting and education for more
than fifteen years. Most worked together at Metaphor Computer
Systems, a pioneering decision support vendor, in the 1980s. All
the authors are members of the Kimball Group and teach for
KimballUniversity. They contribute regularly to Intelligent
Enterprise magazine and other industry publications; most have
previously written books in the Toolkit series. 
Ralph Kimball founded the Kimball Group. Since the mid
1980s, he has been the DW/BI industry’s thought leader on the
dimensional approach and trained more than 10,000 IT professionals.
Ralph has his Ph.D. in Electrical Engineering from Stanford
University.
Margy Ross is President of the Kimball Group. She has
focused exclusively on DW/BI since 1982 with an emphasis on
business requirements analysis and dimensional modeling. Margy
graduated with a BS in Industrial Engineering from Northwestern
University.
Warren Thornthwaite began his DW/BI career in 1980. After
managing Metaphor’s consulting organization, he worked for Stanford
University and WebTV. Warren holds a BAin Communications Studies
from the University of Michigan and anMBA from the University of
Pennsylvania’sWharton School.
JoyMundy has focused onDW/BIsystems since 1992 with
stints at Stanford, Web TV, and Microsoft’s SQL Server product
development organization. Joy graduated from Tufts University with
a BA in Economics, and from Stanford University with an MS in
Engineering Economic Systems.
Bob Becker has helped clients across a variety of
industries with their DW/BI challenges and solutions since 1989,
including extensive work with health care organizations. Bob has a
BSB in Marketing from the University of Minnesota’s School of
Business.

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目　　录

Acknowledgments.
Introduction.
Chapter 1 Introducing the Kimball Lifecycle.
Chapter 2 Launching and Managing the Project/Program.
Chapter 3 Collecting the Requirements.
Chapter 4 Introducing the Technical Architecture.
Chapter 5 Creating the Architecture Plan and SelectingProducts.
Chapter 6 Introducing Dimensional Modeling.
Chapter 7 Designing the Dimensional Model.
Chapter 8 Designing the Physical Database and Planning forPerformance.
Chapter 9 Introducing Extract, Transformation, and Load.
Chapter 10 Designing and Developing the ETL System.
Chapter 11 Introducing Business Intelligence Applications.
Chapter 12 Designing and Developing Business IntelligenceApplications.

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媒体评论

(...)
Das Buch richtet sich primdr an Designer und Informations-Manager im Umfeld von Data-Warehouse-Anwendungen. Diese Zielgruppe erhdlt Ratschldge und Handlungsanleitungen, um deren Anforderungen optimal zu bewdltigen. Dabei ist der ganzheitliche Ansatz zu betonen, der eine evolutiondre Weiterentwicklung besser als singuldre Schnellsch]sse unterst]tzen kann. auch der zugrundeliegende Architekturcharakter trdgt zur Festigung dieser Intension bei. Zu knapp werden die Themen Data Mining und Parallelverarbeitung angesprochen; ebenfalls fehlen zur Vertiefung weiterf]hrende Literaturhinweise. Insbesondere SMP und MPP bieten f]r die praktische Realisierung wichtige Unterst]tzungshilfen. Wobei zur Verbesserung der Performance die Fdhigkeit einiger DBMS Abfragen zu optimieren und parallel zu verarbeiten n]tzlich ist.
IT-Director 10/2000

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