I am happy to announce that my book on data virtualization is available. It was a lot of hard work, but I am proud of the final result.
Early on in the writing process I decided to go for the business intelligence angle and not for a generic book on data virtualization. This explains the title Data Virtualization for Business Intelligence Systems. It allowed me to dive deep with respect to BI-related topics, such as data quality, data integration, effects on data marts, and the impact on data profiling.
Writing a book requires a lot of studying. It means you have to seriously structure and order your knowledge of a topic. Some of that studying leads to great insights. I got some very useful insights when studying for the chapter on design guidelines for data virtualization. In the IT industry many new technologies are introduced every year. Just think about big data, NoSQL, cloud, and so on. But what strikes me is that most of these new technologies are introduced without giving customers any design guidelines. It's up to them to use a trial and error approach to find the proper guidelines, which is an expensive and time-consuming way of discovering how to use new technology the best way.
Therefore I decided to include a chapter in the book on design guidelines when using data virtualization servers. Examples of guidelines are:
It was fun to write this book, and I hope it will help to introduce data virtualization in the BI industry, because this technology deserves more attention.
Early on in the writing process I decided to go for the business intelligence angle and not for a generic book on data virtualization. This explains the title Data Virtualization for Business Intelligence Systems. It allowed me to dive deep with respect to BI-related topics, such as data quality, data integration, effects on data marts, and the impact on data profiling.
Writing a book requires a lot of studying. It means you have to seriously structure and order your knowledge of a topic. Some of that studying leads to great insights. I got some very useful insights when studying for the chapter on design guidelines for data virtualization. In the IT industry many new technologies are introduced every year. Just think about big data, NoSQL, cloud, and so on. But what strikes me is that most of these new technologies are introduced without giving customers any design guidelines. It's up to them to use a trial and error approach to find the proper guidelines, which is an expensive and time-consuming way of discovering how to use new technology the best way.
Therefore I decided to include a chapter in the book on design guidelines when using data virtualization servers. Examples of guidelines are:
- How to handle incorrect data.
- Dealing with different users using different definitions for the same concepts.
- Retrieving data from production systems and the potential interference that results from that.
It was fun to write this book, and I hope it will help to introduce data virtualization in the BI industry, because this technology deserves more attention.
Posted September 10, 2012 12:35 AM
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We have four copies on the way for our team. As both Kenny and I will be at the October 10 "Data Virtualization Day" in is New York, I plan to have it read by then. Thank you for putting your insights on to paper, Rick. You prior writings and presentations have been extremely influential in the way we are developing our data virtualization layer. I am looking forward with eager anticipation to reading your new book and expect to begin applying what I learn pretty much immediately.
Andy