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Summary: Second Time is Charmed
Review: A good book, but a bit tough reading, especially when you get to the examples and building of fact tables and dimensions. I would suggest reading the content and skipping the examples the first time and the read the entire book. It helped to have that understanding first. A few of my colleagues have put the book aside for lack of wanting to "Plow through" the examples. I managed to read through the book this way in a matter of a few days. It helped to clarify what pieces I was missing ans strengthened what I knew.
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Summary: Beginning a Data Warehouse Project? BUY THIS BOOK!
Review: All the good reviews are true: this is a FANTASTIC book. Within a couple of days you will understand DW design completely, especially if you have experience in normal OLAP database design, as it makes a great contrast. Kimball's writing style -- showing how to apply DW design to real-world business problems -- makes the material really sink in, unlike reading a reference book. (You need to actually read this book cover-to-cover -- and it's a great read if you are a Database geek!) And yet you can still use it as a reference book. The companion books by him are fantastic additions to this..
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Summary: Practically Useless CD
Review: As a systems consultant trying to learn how to build a data warehouse, I found it extremely frustrating that the DW application on the CD does not support any higher version of MS Access than 2.0 and the author no longer upgrades the application. However, the content of the book is useful and has given me some insight, if not hands-on experience.
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Summary: Dimensional Modeling Made Easy
Review: Data warehosuing is a difficult subject to grasp. There are many disciplines that have to come together to make data warehousing work : database design, business understanding, management expertise, data mining, creating reports, project management, OLAP and more.Finding a book that can full explain the full data warehousing picture is not possible, what one must look for is a series of books or materials that will together paint the full picture. Building Dimensional Data Warehouses is one of the books that can help readers in the process of painting the whole data warehousing picture. Ralph Kimball is an experienced data warehousing leadder who has stayed away from hype and concentrates on facts. The focus of this is book is creating the data structure for repositories in the data warehouse set-up. This is a highly recommended book for technical readers looking to enhance on their database design skills for building large data repositories that are optimized for providing analysis. Business readers may obtain more value from reading some of the other of Mr. Kimball's books. Please let me know if you have found this review helpful.
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Summary: Insightful, fundamental, indispensable for developers
Review: Data warehouse construction is driven by the need to understand customers, products, and key business events. In this way, data warehousing completes the promise of the client-server initiative. The promise is to provide access to data in a timely, flexible, and accurate manner. For those wondering what is a data warehouse, whether they might already be operating one, how to tell if one is needed, and how to build one, Ralph Kimball provides the answers. The fundamental distinction is between operations and decisions. This is the difference between day-to-day operations and management decisions (business strategy and tactics). The processes making up operations include highly granular transactions, stored at the detail level, and on-line transactions processing (OLTP). This is the stuff of classic order entry, inventory control, and general ledger. Decisions require insight and vision about the performance and objectives of the business. Insight and vision require facts about the business delivered "at the speed of business" (fast). Thus, data warehousing consists of modeling the business in terms of basic central facts (units sold or delivered, captured and summarized from operations) in relation to the fundamental dimensions that constitute the business over time. Typically, this results in a multi-dimensional model: a fact structure surrounded by product, customer (market), and time (history). This is the celebrated "star schema" being discussed in the popular trade journals. Kimball claims his work is consistent with the OLAP movement (p. 19) with one difference. His approach is "open," employing de facto industry standard relational technology; whereas OLAP is still proprietary and (more importantly) not robust enough to scale to the enterprise level. This work is remarkably easy to summarize. Chapters Two through Nine take the reader by the hand through a series of progressively more abstract examples of dimensional data modeling in the grocery store, the warehouse, shipments, financial services (banking), subscription services (cable TV), and insurance (casualty). A word of caution, however. If one is interested in insurance, one cannot jump immediately to that chapter. It builds on groundwork laid in preceding chapters. So, for example, to understand why products in insurance and banking are so diverse they do not belong in the same relational table, it is useful to appreciate the homogeneity and comparability of canned products on a grocery store shelf. For computing professionals, the wealth of functional business distinctions, especially in connection with the relational database model, is instructive. This results in useful suggestions on how the relational model can be extended as well as measures needed in application code until such extensions occur. For example, measures that record a static level (inventory, financial account balances) are not additive across time. Balances cannot simply be added, but must be averaged by time period. Since the SQL AVG function considers rows returned, not time periods relevant (PERIODAVG), average period sum must be calculated in an application or proprietary SQL extension. The performance challenge of data warehousing is large. This can be appreciated by considering product, customer, and time dimensions of an average of 10,000 distinctions each. Without sparcity (not all combinations occur), the result is a combination on the order of 100 billion rows. Naturally, the problem is made worse for phone companies and banks which have millions of customers (see Chapter 6: "The Big Dimensions"). Kimball claims that the limits of current relational technology (circa 1995) are reached at about 1 billion rows or about 100 gigabytes. The answer is considered in Chapter 13 on Aggregation. Since an endless horizon of business days tends to cause combinatorial explosion of the facts at an elementary level, it is useful to define aggregations (summations) which group twenty or more facts together. Combine and store the data on a weekly or monthly basis rather than daily. The trade-off is between more work transforming data in long-running batch process prior to loading and quicker on-line response time to queries submitted interactively. The work contains a wealth of practical advice for the information technology practitioners. For example, when the relative size of the central fact table is compared to that of the surrounding dimension structures (differences of orders of magnitude are common), it is clear that little disk space can be saved in normalizing the latter. As a text, the book is superbly prepared. It comes with a CD-ROM containing an ACCESS version of the databases described in the book and sample queries against the databases. The reader is provided with a complete glossary of terms, appendixes, index (no bibliography) and a useful summary of design principles of a dimensional data warehouse. For this reviewer, the continuity with the discipline of data modeling, data administration, and data mining (customarily called "logic"), is useful and productive. Much of what occurs in decomposing data into relational structure by means of the process of applying Codd's "normal forms" is relevant here. But with a new "spin". The structure of the data gives us insight into the kinds of questions might be asked. Thus, the prospect of packaging a large, but finite, set of SQL queries can be envisioned. Kimball is to be congratulated on taking the "hype" out of data warehousing and showing its importance as an application of relational technology to business practices. -- excerpt from my review originally published in Computing Reviews, November 1996
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Summary: An excellent introduction in dimensional modelling
Review: Every college who is just thinking of designing a data warehouse should read this book. The descriptions of the dimensional modelling techniques are clear and accurate, and there are some very good examples of different industries. The book have logical structure and is ease to understand. What I missed is the loading part so as the design of staging areas. Extraction and stating can easy turn to a nightmare. So if you are looking for an approach concerning the backend of a data warehouse, this book is of little use. But anyway I consider this book as one of the classics in data warehousing.
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Summary: My peers call it "The Data Warehouse Bible"
Review: Every once in a while a technical book comes along that changes the way the world looks. "The Data Warehouse Toolkit" is just such a book. If you have spent your career in the world of relational database models, you owe yourself a read of this book to see a new and different paradigm. This book shows where the "rubber meets the road" for data warehouses accessed by end-users to answer real business questions. As a technology consultant specializing in data warehouses, I don't leave home with out the book I have now used to build data warehouses on three continents.. Kimball presents not only a new way to view data organization, but also provides the practical information necessary to apply it to the real world (with examples on CD-ROM including a live OLAP tool). The multidimensional model with relational database management systems described by Kimball is the key to success for any sizable data warehouse. We don't call it "The Bible" for nothin'.
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Summary: My peers call it "The Data Warehouse Bible"
Review: Every once in a while a technical book comes along that changes the way the world looks. "The Data Warehouse Toolkit" is just such a book. If you have spent your career in the world of relational database models, you owe yourself a read of this book to see a new and different paradigm. This book shows where the "rubber meets the road" for data warehouses accessed by end-users to answer real business questions. As a technology consultant specializing in data warehouses, I don't leave home with out the book I have now used to build data warehouses on three continents.. Kimball presents not only a new way to view data organization, but also provides the practical information necessary to apply it to the real world (with examples on CD-ROM including a live OLAP tool). The multidimensional model with relational database management systems described by Kimball is the key to success for any sizable data warehouse. We don't call it "The Bible" for nothin'.
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Summary: Wonderful Introduction to Data Warehousing
Review: Excellent examples and a logical layout lead the reader through a thorough introduction to data warehouseing, its benefits, limitations, and promise. A very readable text that even non-IT folks can follow and profit from.
Rating:
Summary: Wonderful Introduction to Data Warehousing
Review: Excellent examples and a logical layout lead the reader through a thorough introduction to data warehouseing, its benefits, limitations, and promise. A very readable text that even non-IT folks can follow and profit from.
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