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The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling 2nd Edition

63 customer reviews
ISBN-13: 978-0471200246
ISBN-10: 0471200247
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The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling + The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data + The Data Warehouse Lifecycle Toolkit
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Editorial Reviews

Review

"...this is the daddy of data warehousing process books. No other material available so concisely and precisely explains what is required from a data warehousing solution...this is a great book..." (Enterprise Server Magazine, July 2002)

From the Back Cover

The latest edition of the single most authoritative guide on dimensional modeling for data warehousing!

Dimensional modeling has become the most widely accepted approach for data warehouse design. Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.

The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:
* Retail sales and e-commerce
* Inventory management
* Procurement
* Order management
* Customer relationship management (CRM)
* Human resources management
* Accounting
* Financial services
* Telecommunications and utilities
* Education
* Transportation
* Health care and insurance

By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.

Wiley Computer Publishing. Timely. Practical. Reliable.

Visit our Web site at www.wiley.com/compbooks/
Visit Kimball's Web site at www.kimballuniversity.com

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Product Details

  • Paperback: 464 pages
  • Publisher: Wiley; 2 edition (April 26, 2002)
  • Language: English
  • ISBN-10: 0471200247
  • ISBN-13: 978-0471200246
  • Product Dimensions: 7.4 x 1 x 9.2 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (63 customer reviews)
  • Amazon Best Sellers Rank: #95,384 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

120 of 126 people found the following review helpful By Jim Stagnitto on May 5, 2002
Format: Paperback
There are a lot of data warehousing books out there that try to answer the question: 'Why'? Why data warehouses are needed to help businesses make better decisions - why the OLTP systems that run the business can't do this - and sometimes even why businesses ought to invest in data warehouses. These books were terrifically useful to us years ago, when we needed help (and scholarly footnotes) in our data warehouse project proposals. This book is not one of those - it is all about:

How

How to actually design and build a repository that will deliver real value to real people. In this reviewer's opinion, Ralph Kimball's many contributions related to the 'how' of data warehousing stand alone.

An engineer wishing to jump-start his or her data warehouse education would need to read Ralph's Data Warehouse Toolkit first edition, his Data Webhouse Toolkit... a bunch of "Data Warehouse Designer" Intelligence Enterprise magazine articles... AND lurk on the Data Warehousing List Server...for a few years (all terrific resources - by the way) - in order to stockpile the knowledge that is crisply presented here.

No shortcuts taken by the authors that I can spot: all of the toughest dimensional design issues that I've tripped on - and that I can remember surfacing on in discussion groups over the past few years - are addressed in this significantly updated text. Not all of the solutions are 'pretty' - but it is clear that they thoughtfully address the problem. This approach, in my opinion, instills student confidence - and lets us know that we are getting sound instruction - not dogma.
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45 of 48 people found the following review helpful By A Customer on August 27, 2002
Format: Paperback
If you want to understand data warehouse design either as user, architect or developer, you need to read this book cover to cover.
Things I like about this book:
* Coverage of all core principles in dimensional data modeling using examples. Ralph does not just lecture to you -- he shows you how to put it into practice
* Coverage of a vast variety of domains. This alone makes the book a must-read
* Recap of major principles at the end of the book to bring it all together
* Excellent writing -- Ralph does not treat you like a dummy; neither does he assume that you have an IQ north of 200
* When you purchase this book, you are in effect purchasing a sliver of the combined knowledge of both authors in the data warehousing field. Highly recommended
I implemented a data warehouse using some of these principles back in 1999. The project was a resounding success and is the most popular application in the financial services firm that I implemented it in. (Infact when I lost my job at an Internet company, they immediately offered me a job based on this implementation). The only sad part to the whole story is that we made a few mistakes in implementation that are now very difficult to correct because the data warehouse has become core to the business -- we have too many end-user applications riding on it!
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22 of 23 people found the following review helpful By Paul A. Chernoch on June 7, 2003
Format: Paperback
After six years of creating data warehouse applications, making a plethora of mistakes and learning stuff the hard way, I wish I had had this book at the start! Every other page offers a solution to some problem or other that I have had. In the project I am just starting I am facing new challenges and am finding help with them as well. The best part is how solutions I used in the past which were appropriate for those problems are contrasted with solutions for problems like the ones I am facing now. Almost as bad as solving a problem the wrong way (or overlooking it entirely) is reusing an old solution that does not fit the new problem. This book clearly spells out when each solution is appropriate. I can not speak too highly about how useful this book will be for you!
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13 of 13 people found the following review helpful By Dimitri Shvorob on January 19, 2013
Format: Paperback
The book's complete title is "Data warehouse toolkit: the complete guide to dimensional modeling". What is dimensional modeling? Chapter 1, "Dimensional modeling primer", will surely explain. Page 1 - nothing, page 2 - nothing... page 8 - nothing, page 9 - "By default, normalized databases are excluded from the presentation area, which should be strictly dimensionally structured". What is "dimensionally structured" though? Have I missed the definition? Leafing back... no, Kimball is just using a concept before he defined it, moving on... Page 10: "Dimensional modeling is a new name for an old technique for making databases simple and understandable". Great, what is it then? Page 11 - "Dimensional modeling is quite different from third-normal-form (3NF) modeling". Yees? Page 12 - nothing, page 13 - "If the presentation area is based on a relational database, then these dimensionally modeled tables are referred to as star schemas". Finally! Now, this sort-of-definition would not help someone who did not know about star schema, but thankfully I do, and anyway, this is the closest thing to a definition that you get - although things start to get clearer on page 16, where fact tables and dimension tables are introduced. The essence of dimensional modeling, it seems, is "Star is good; snowflake is bad".

A couple of pages later, on page 18, I see this passage. "The fact table itself generally has its own primary key made up of a subset of the foreign keys. This key is often called a composite or concatenated key. Every fact table in a dimensional model has a composite key, and conversely, every table that has a composite key is a fact table. Another way to say this is that in a dimensional model, every table that expresses a many-to-many relationship must be a fact table".
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