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Data warehousing in the age of big data
Krishnan K., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2013. 370 pp. Type: Book (978-0-124058-91-0)
Date Reviewed: Sep 20 2013

Big data is becoming a major topic in several areas, from academic research to enabling operational technologies and businesses. Since big data is a vast topic that comprehends many architectural, programmatic, operational, and information technology-specific solutions, a book on it would need to cover many potential and interrelated areas. The author of this book has attempted to do just that.

Chapters 1 through 3 start with an introduction to the rationale behind the current shift to big data, highlighting several existing data sources and defining the properties of big data. It is not only about high volumes of data. Even though such volumes are typically encountered in big data processing, big data systems also involve the added business and service-specific value generated by turning data into income-generating services. In 14 chapters, the book covers topics such as Hadoop and its ecosystem, Hive, HBase, and several NoSQL databases, including Cassandra and Riak.

I draw your attention especially to chapter 4, which is a must-read for anyone interested in the different technologies that can be used for big data. This chapter is by far the longest in the book (45 pages), containing a tremendous wealth of detail on the underlying architectural components. The author describes both early and mature projects in a prototyping phase. This chapter is currently the best state-of-the-art introduction to this area, and I recommend it to every reader interested in big data. Chapter 5 addresses the business perspective by discussing several application domains and introducing some companies that have monetized big data analysis.

In the second part of the book, the author introduces data warehousing (chapter 6), describes several common warehouse architectures, and shows how data management has been done in traditional warehousing approaches (chapters 7 and 8). However, the advent of big data pushes the latter toward their limits, such that major reengineering is required, supported by cloud-based virtualized data, machine learning, and big data-driven processing. Chapters 9 to 14 show how these building blocks can be leveraged to build the future data warehousing architectures.

Content-wise, the book targets two audiences. Readers coming from a data warehousing background will learn where big data fits in and how specific challenges can be addressed. For readers working in a big data community, the book will be very valuable for understanding the link between big data and data warehousing. For both groups, the book is an excellent and welcome addition to the literature. The fluent writing style, updated technical content, and fresh perspectives on a hot and important topic in computing will make it invaluable to a large reader community including business consultants, aspiring data scientists, and computer science graduate students.

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Reviewer:  Radu State Review #: CR141579 (1312-1069)
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