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Star Schema The Complete Reference Paperback – 1 Aug 2010
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About the Author
Christopher Adamson is the founder of Oakton Software LLC and a faculty member at The Data Warehousing Institute. He works with customers in all industries to establish data warehousing strategies, define and prioritize projects, and design solutions. Chris has taught dimensional design to thousands of students worldwide, and has written numerous books and articles.
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Top customer reviews
It sets the scene very well for example explaining the difference between operational and analytic systems. It has the clearest comparison I've ever read of the two rival architectures, the Inmon `Corporate Information Factory' and the Kimball `Dimensional Data Warehouse'.
It describes the basic anatomy of a star schema and defines and explains all the key terms clearly (e.g. junk dimensions, surrogate keys). It then expands to more complex designs; several fact tables, slowly changing dimensions, bridge tables. It discusses the pros and cons of various design options such as the use of an outrigger tables and snowflake schemas.
It covers advanced topics including using designs using bridge tables for navigating hierarchies which I found very useful. There is a good section on factless fact tables.
I have designed several BI systems and their underlying star schemas and this was a useful book to me. I would agree with the book's title that it is a complete reference.
Getting the right data model and knowing about different modelling methodologies to reference and quote in meetings is far far more powerful than people realise.
Most helpful customer reviews on Amazon.com
So to get to the point, Adamson offers great advice and explicit step-by-step explanations for many star schema topics. The only area that the author does not delve into is material around specific database products, but readers should be able to research this information after following along with the presentation that the author offers with this work. After discussing the fundamentals of analytic databases and dimensional design, data warehouse architectures, and stars and cubes, the author offers detailed presentations on fact tables, dimensions, hierarchies, bridges, snowflakes, and slowly changing dimensions, followed by discussions on performance and additional topics of interest for developers of dimensional models, including an exploration of some common dimensional features that often strain business intelligence tools, techniques to mitigate any shortcomings, and short discussions on process, design, and documentation.
One great example of the thoroughness of this book is the presentation that the author offers in Chapter 10 ("Recursive Hierarchies and Bridges"). After discussing types of recursive hierarchies (balanced versus unbalanced and attribute-based versus instance-based), reporting challenges associated with recursive hierarchies, and the process and implications with flattening such hierarchies, Adamson delves into the hierarchy bridge, providing a detailed walkthrough of how to construct it and use it, how to avoid double-counting, how to hide the bridge from novice users, how to resolve the many-to-many relationship, and how to handle Type 2 changes to the hierarchy. The step-by-step explanation of this last item is especially well done, with great diagrams and a detailed discussion of the ripple effect of such changes and why you should not resist the ripple effect, despite the additional data involved. This explanation contrasts markedly with other material on this subject. While a single design tip called "Help for Hierarchies" from The Kimball Group, for example, discusses this subject, it is done so at a very high level.
As a data architect working through this book for the first time during a project, most of my time was spent in Part 3 ("Dimensional Design"), which breaks down into Chapter 6 ("More on Dimensional Tables"), Chapter 7 ("Hierarchies and Snowflakes"), Chapter 8 ("More Slow Change Techniques"), Chapter 9 ("Multi-Valued Dimensions and Bridges"), and Chapter 10 ("Recursive Hierarchies and Bridges"), although I did spend significant time with Chapter 4 ("A Fact Table for Each Process"), Chapter 11 ("Transactions, Snapshots, and Accumulating Snapshots"), and Chapter 12 ("Factless Fact Tables"). While I will likely continue to reference a number of other resources in this space, this is the one book currently in the marketplace that has staying power for the long-term. Highly recommended.
II. Multiple Stars
III. Dimension Design
IV. Fact Table Design
VI. Tools and Documentation
This is a very natural and logical progression through data modeling and ETL implications. You can read the book from beginning to end and/or use it as a reference when faced with a particular challenge. Star Schema covers a wide range of scenarios with examples that can be easily extrapolated to other industries or your particular data.
Even being in IT, IT books can be dry. However, Christopher’s writing style is that of a mentor. He walks you through each topic with examples. Often, I came up with a question only to be answered on the next page. This keeps the reader engaged and that makes for a deeper understanding of the material. Not every Customer Dimension or Sales Fact is modeled the same. After you know the concepts and the data, you can produce the best design.
From Conformed Dimensions to Recursive Hierarchies and Bridges, you will see your data and your data model in a new light. Every system needs a strong foundation. This book will help you develop a solid dimensional model that will support and grow as your business does.
Not only is the content very precise and comprehensive, but it is also very well written.
Data Warehousing concepts are notoriously difficult to explain. That's why so many well-meaning DW books comes short. If you can only read one book on DW design, then make it this one.
Contrary to the title, the book covers Snowflake Schema quite adeptly, and the author is careful to list all the pros and cons of going from Star to Snowflake. That leap from star to snowflake should always be taken with considerable thought.
The book is light both on data analytics and ETL. This book is almost all about Star and Snowflake schemas.
1)The best book on data warehousing design I have ever read.
2)Comprehensive AND in-depth coverage of both Star and Snowflake schemas.
3)Very well written.
1)Doesn't cover much outside of data warehousing. It has light coverage of data analytics and ETL.
This book does not show how to use a tool. It is purely design concepts. If you are looking for a book that shows how to use a tool, you are looking at the wrong stuff.