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Building, Using, and Managing the Data Warehouse (Data Warehousing Institute Series from...
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by Ramon C. Barquin and Herbert C. Edelstein
Sales Rank: 1660185
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$0.84
At Amazon
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Hardcover: 317 pages
Publisher: Prentice Hall PTR; 1st edition February 1997
Language: English
ISBN-10: 0135343550
ISBN-13: 978-0135343555
Product Dimensions:
9.6 x 7.3 x 1.3 inches
Shipping Weight: 2 pounds
Product Description
Teaches you how to decide which data to include, optimize the quality of new data, manage and staff your data warehouse for maximum effectiveness and deliver solutions that serve the real needs of users. DLC: Database design.
Publisher Description
Presents practical, real-world problems and solutions for a variety of issues related to information systems. Each separate issue is presented as an individual essay. They are all written by professionals representing both technological and user organizations.
Customer Reviews & Comments
Building a data warehouse is based in the traditions of decision support, data modeling, and information center computing. Yet it is not reducible to any of these and supersedes them. Drawing on practices, technologies, and challenges that were not dreamt of until the mid 1990s. As George Zagelow's introductory essay makes clear, the data warehouse is a transformation of operational data into a from that provides business information, intelligence, and knowledge. His recommendation is to drive out nconsistencies in legacy data spanning data marts (smaller departmental assemblies). The enterprise- wide warehouse is built as the UNION of data marts where company-wide questions can be answered. If it works, this is a tactic for constructing the larger warehouse department by department. It does presuppose acceptance (or imposition) of an enterprise perspective. Mark Sweiger provides a thorough technical briefing for management on the role of massive parallel processing (MPP) as a method for tackling the large amount of data stored in warehouses. Technology is not a silver bullet here. But it is an important component of the answer. The author lays out the terms of the debate of the function shipping model (move the query to the data) versus dynamic data redistribution (rebalance skewed data by moving the data). These are not really on a collision course; and savvy administrators will look for a relational optimizer that can cost out the differences in access method. Next essays by Paul Barth and Robert Small / Herb Edelstein address examples, issues and tools in data mining applications. The first level data warehouse answers questions about which customers are buying which products or services and where and when they are doing so. But what if you either don't know what to ask or want to drill down as to why by chasing statistically significant correlation with demographic data. What if help is needed in formulating hypotheses? Then the advance to data mining is in order. Unlike the relational database model, which, in its many implementations is arguably an open standard, data mining tools are still exclusively proprietary (vendors specific lock-in is implied). Decision trees, neural networks, clustering and class analysis are the order of the day. Here the embedded technical function are pattern matching, bottom up rather drilling down through aggregates, and fuzzy logic rather than symbolic. If you recall the advertisement criticizing the consultant for quoting Sun Tzu on THE ART OF WAR, but being missing in action at implementation time, then you will want to study Bernard Boar on Understanding Data Warehousing Strategically. Without the strategic dimension, the use of the warehouse is without vision; and Boar provides that in good measure, including the references to Sun Tzu. According to Boar, the data warehouse provides the basis for a "rising tide" strategy. In this case, all the boats that are lifted by the rising tide of useable, accessible information are the knowledge workers of the enterprise. This furnishes the cherished leverage of a multiplier effect in infusing actions and roles with meaning ("informating" in S. Zuboff's sense (p. 288). Boar provides one of the most insightful and engaging essays in this excellent collection; and as a solid piece, it is capable of sustaining criticism. To be sure, the ART OF WAR is applicable to relations with business competitors - except that today the model is compete in the morning, cooperate in the afternoon. As far as relations with customers, the model is more likely to be from a different ancient Chinese sage, Lao Tzu, whose TAO DE CHING presents the sage (data warehouse consultant?) as the sea, receiving the homage (bookable revenue?) of a thousand rivers, because he places himself below them. Also included in this volume are useful essays on data quality (the "sweat" component of genius) by George Burch, Dennis Berg / Christopher Heagele; the perspective of the end-user (Katherine Glassey-Edholm); legacy systems (Katherine Hammer); object-oriented OLAP (David Menninger); staffing considerations (Narsim Ganti); updating the data warehouse (J.D. Welch). The volume is nicely edited, printed without error, and furnished with big wide margins for notes and thoughts. There are a significant number of tables, graphs, pictures, and illustrations (in attractive gray scale) which add value to the presentation. This text would make a nice addition to the library of managers, technicians, and business experts charged with the task of building and operating the enterprise data warehouse. Finally, if any doubt exists that data warehouse technology is its own separate domain of expertise, then the essay by Ramon Barquin, also one of the editors, will dispel it. He proposes a model curriculum for data warehouse training and practice. Both extensive and deep, one must wish him well with its implementation and acceptance by the information supply chain management industry. -- excerpt from my review published in Computing Reviews, April 1998
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Building, Using, and Managing the Data Warehouse (Data Warehousing Institute Series from...
Available from Amazon
Price: $0.84
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