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Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses

4.16 ( 817 Ratings by Goodreads)
Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses

Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses

(Author)
4.16 (817 Ratings by Goodreads)
paperback
Published: 21 March, 1996
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Description

This definitive guide succinctly explains how to build a data warehouse by using actual case studies of existing data warehouses developed for specific types of business applications such as retail, manufacturing, banking, insurance, subcriptions and airline reservations. Describes a powerful new model of data warehouse design, the dimensional data warehouse, that provides readers with the ability to quickly analyze complex information in order to make sound decisions. The accompanying CD-ROM includes a toolkit for building dimensional data warehouses and examples of all the databases discussed in the text.
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More Details

Type Book
ISBN13 9780471153375
ISBN10 0471153370
Number Of Pages 416
Item Weight 725 g
Product Dimensions 190 x 28 x 231 mm
Publisher / Reseller John Wiley & Sons
Format paperback
Edition Pap/Cdr
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Media Reviews

Ralph Kimball provides clear-cut guidelines on how to model data and design data warehouses to support advanced multidimensional decision support systems. With Kimball's step-by-step instructions, readers will have mastered the full range of powerful techniques for creating, controlling and navigating dimensional business databases that are easy to understand and navigate. (Fatbrain.com; Ganthead.com, 9/01)

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Author's Bio

RALPH KIMBALL currently designs large data warehouses for a living, in addition to teaching and consulting. He also writes the Data Warehouse Architect column for DBMS magazine and was co-inventor of the Xerox Star workstation, the first commercial product to use mice, icons, and windows. Dr. Kimball was vice president of applications at Metaphor Computer Systems, and founder and CEO of Red Brick Systems. He earned his PhD in electrical engineering from Stanford University.

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