The Data Warehouse Lifecycle Toolkit, Second Edition 9780470149775 mobi 下载 网盘 caj lrf pdf txt 阿里云

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:

Identify and prioritize data warehouse opportunities

Create an architecture plan and select products

Design a powerful, flexible, dimensional model

Build a robust ETL system

Develop BI applications to deliver data to business users

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:

The Data Warehouse Toolkit, 2nd Edition

(9780471200246)

The Data Warehouse Lifecycle Toolkit, 2nd Edition

(9780470149775)

The Data Warehouse ETL Toolkit (9780764567575)

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 Selecting

Products.

Chapter 6 Introducing Dimensional Modeling.

Chapter 7 Designing the Dimensional Model.

Chapter 8 Designing the Physical Database and Planning for

Performance.

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 Intelligence

Applications.

Chapter 13 Deploying and Supporting the DW/BI System.

Chapter 14 Expanding the DW/BI System.

Glossary.

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.

暂无出版社相关信息，正在全力查找中！

暂无相关书籍摘录，正在全力查找中！

在线阅读地址：The Data Warehouse Lifecycle Toolkit, Second Edition 9780470149775在线阅读

在线听书地址：The Data Warehouse Lifecycle Toolkit, Second Edition 9780470149775在线收听

在线购买地址：The Data Warehouse Lifecycle Toolkit, Second Edition 9780470149775在线购买

暂无原文赏析，正在全力查找中！

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