The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses
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Product Description
"A comprehensive, thoughtful, and detailed book that will be of inestimable value to anyone struggling with the complex details of designing, building, and maintaining an enterprise-wide decision support system. Highly recommended." -Robert S. Craig, Vice President, Application Architectures, Hurwitz Group, Inc.
In his bestselling book, The Data Warehouse Toolkit, Ralph Kimball showed you how to use dimensional modeling to design effective and usable data warehouses. Now, he carries these techniques to the larger issues of delivering complete data marts and data warehouses. Drawing upon their experiences with numerous data warehouse implementations, he and his coauthors show you all the practical details involved in planning, designing, developing, deploying, and growing data warehouses. Important topics include:
* The Business Dimensional Lifecycle(TM) approach to data warehouse project planning and management
* Techniques for gathering requirements more effectively and efficiently
* Advanced dimensional modeling techniques to capture the most complex business rules
* The Data Warehouse Bus Architecture and other approaches for integrating data marts into super-flexible data warehouses
* A framework for creating your technical architecture
* Techniques for minimizing the risks involved with data staging
* Aggregations and other effective ways to boost data warehouse performance
* Cutting-edge, Internet-based data warehouse security techniques
The CD-ROM supplies you with:
* Complete data warehouse project plan tasks and responsibilities
* A set of sample models that demonstrate the Bus Architecture
* Blank versions of the templates and tools described in the book
* Checklists to use at key points in the project
Product Details
- Amazon Sales Rank: #306250 in Books
- Published on: 1998-08-13
- Original language: English
- Dimensions: .3 pounds
- Binding: Paperback
- 800 pages
Editorial Reviews
From Amazon.com
In The Data Warehouse Lifecycle Toolkit, authors Ralph Kimball, Laura Reeves, Margy Ross, and Warren Thornthwaite present a structure for undertaking the awesome task of implementing a data warehouse. As part of a rather select group of professionals actually experienced in building data warehouses, the authors attempt to convey their expertise about how to approach the job. The book focuses on the "Star Lifecycle"--a high-level project-planning approach to evolving existing information systems into an ever-changing data-warehouse solution. --Stephen Plain
Review
(...)
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 Publisher
Ralph Kimball's bestselling The Data Warehouse Toolkit explained how to design data warehouses using the dimensional modeling technique he developed. This perfect complement to that book shows how dimensional design fits in the overall lifecycle of planning, designing, developing, and deploying data marts and data warehouses. Based on the real experiences of four leading data warehouse developers and offering timely case studies, this book provides developers with the fine details on how to master each step in the process. It also covers how to design data marts that are well integrated with the overall data warehouse design.
