The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Anglais) Broché – 25 avril 2002
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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
∗ Order management
∗ Customer relationship management (CRM)
∗ Human resources management
∗ Financial services
∗ Telecommunications and utilities
∗ 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.
This book is also available as part of the Kimball′s Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:
The Data Warehouse Toolkit, 2nd Edition (9780471200246)
The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)
The Data Warehouse ETL Toolkit (9780764567575)
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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.
The authors have been listening to and addressing the data warehouse community's 'pain' through periodicals and posts for years - but this book pulls these point solutions together very nicely. I learned a surprising number of really useful new techniques, and was genuinely enlightened by the 'Present Imperatives and Future Outlook' section.
As in the first edition, there is minimal philosophical lecturing, and zero religion. Instead, we get generous helpings of real-world case studies - aptly applied to progressively more advanced series of design concepts.
This style absolutely works for me. And I suspect that engineering mindsets typical of the folks that build these things will likely agree. In short, the Data Warehouse Toolkit Second Edition will significantly lighten the load of books that I carry between data warehouse engagements.
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!
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". I am confused, for three reasons. A fact table's primary key is "generally" made up of a subset of foreign keys? This is not the case with Kimball's own first fact table on page 36 - "POS Transaction Number" definitely should be part of a primary key (he does not define one, so I assume), but it does not foreign-key into anything. Oh, and Sentence 3 means it's "always", not "generally", if we follow the "conversely" path. Is the "another way to say this" part true? ... And overall, isn't this all just a confused way to say that fact tables have foreign keys and dimension tables don't? "Stars, no snowflakes". (What's wrong with snowflakes, apart from the increased design complexity? Among the reasons listed on page 60 - views are never mentioned - the technical and the scariest one is "snowflaking defeats the use of bitmap indexes").
The two examples above are representative of the book's style, and I am quite sure that it could use a lot more editing. I wish that somebody did a better job, but don't know a reasonable substitute. (Yes, I have seen Inmon's book - not a fan). Nonetheless, it's an impressive, concrete book that will give you a lot of practical ideas, and, when it suggests something that looks suboptimal or incomplete or self-contradictory, will make you think about schema design. Not a sufficient reference on the subject, but a very necessary one.
PS. I recommend "Kimball Group Reader" as the alternative to this book: I believe that it covers the material here, and offers a lot of additional information.
There isn't a standard blueprint that can come close to solving most data issues. Data Warehousing (DW) involves constant tweaking and the goal of good DW project management is minimizing the associated operational cost.
I have been a fan of Ralph Kimball as he writes as a person who has been through many implementations. With Mr. Kimball there isn't a miracle cure being touted - stay away from publications that claim such a cure.
Mr. Kimball approached the subject with good advices and encourages the readers to watch out for the pitfalls and follow best-practices in design implementation. It is similar to working with a well experienced supervisor.
The core to successful DW implementations is - LISTENING. Listening to the users on their needs and gauging the software resources available at your disposal.
Trade-offs in design versus cost/performance are a must. You will never have all the resources you need to implement the DW of your dreams. And if you did, chances are very high that once the DW is ready for use the business cases have changed making the design redundant.
Mr. Kimball will help in passing these information and much more. It also goes in good technical detail for suggested modeling of data.
I hope this review is helpful, please let me know if you have any questions or suggestions.
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