- Paperback: 672 pages
- Publisher: John Wiley & Sons; 2nd Revised edition edition (4 January 2008)
- Language: English
- ISBN-10: 0470149779
- ISBN-13: 978-0470149775
- Product Dimensions: 18.8 x 3.3 x 23.6 cm
- Average Customer Review: 1 customer review
- Amazon Bestsellers Rank: #9,71,807 in Books (See Top 100 in Books)
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The Data Warehouse Lifecycle Toolkit Paperback – 4 Jan 2008
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(...) 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
From the Back Cover
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.
Wiley Technology Publishing
Timely. Practical. Reliable.
Visit the book′s companion website at www.wiley.com/compbooks/kimball
Find templates, sample documents, checklists, and task lists on the book′s companion website at www.kimballgroup.com
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Most helpful customer reviews on Amazon.com
Our class lectures would cover the material pretty well but leave a lot of questions. I'd then read the matching chapters and it would walk me right thru it.
One thing; a DW concepts in general seem "Ivory Tower" that wouldn't survive the real world, but then , I'm just learning this stuff so I could be wrong about that. Rather; I wonder how much they still apply. For example; he states it takes 6 months to build a "Data Mart", and add 6 months for each additional data source. So with 40 data marts in a typical enterprise, we're talking 10 years! 20 years if there are 2 data sources in the enterprise. That simply doesn't add up. Somehow I think the industry would have to move quicker than that, but at least he lays out the methods.