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Big data imperatives : enterprise big data warehouse, BI implementations and analytics
Mohanty S., Jagadeesh M., Srivatsa H., Apress, Berkeley, CA, 2013. 320 pp. Type: Book (978-1-430248-72-9)
Date Reviewed: Mar 31 2014

Those interested in big data and developing, implementing, or managing systems to analyze big data for business applications will find this book very useful. As noted by the authors, it is not a manual for Hadoop, software designed to process large quantities of data in multiple formats, so there are almost no programming examples provided. The book has 296 pages including a good index, and is organized into three parts with a total of nine chapters.

The first chapter in Part 1, “‘Big Data’ in the Enterprise,” is an introduction to big data and how it fits in the enterprise. The second chapter, “The New Information Management Paradigm,” presents material on why big data analysis is needed for enterprises, including why current methodology can’t provide the needed scale of analysis. Chapter 3, “Big Data Implications for Industry,” discusses the use of big data and its implementation in industry, with examples in such fields as telecom and banking.

Part 2 of the book starts with chapter 4, “Emerging Database Landscape,” which provides some background on databases, with an emphasis on NoSQL databases and why they are important in the analysis of big data. Chapter 5, “Application Architectures for Big Data and Analytics,” discusses some of the architectures for implementing big data analytics, and includes a review of the use of Hadoop in the enterprise. Chapter 6, “Data Modeling Approaches for Big Data and Analytics Solutions,” covers how data models are used to analyze data, with examples of the different techniques used to extract information from the data.

Part 3 begins with chapter 7, “Big Data Analytics Methodology,” which discusses the basic methodology of analytics as applied to big data and then steps the reader through the process of developing a business hypothesis for using analytics in big data. Chapter 8, “Extracting Value from Big Data: In-Memory Solutions, Real-Time Analytics, and Recommendation Systems,” covers the technologies mentioned in the chapter title for performing more analysis of big data. Chapter 9, “Data Scientist,” describes the function of a data scientist, which is to take raw data and use the data scientist’s knowledge and expertise to convert it into valuable information. It covers what a data scientist is and what she should be able to do, and ends with a set of tests that can be used to evaluate someone who presents herself as a data scientist.

Overall, this is an interesting book that presents very valuable information. The age of analytics and big data is upon us, and big data analysis can be used as an essential tool of business to enhance market share, sales, profit, and viability in the industry. An interesting insight to the value of analytics and big data is the analysis performed by the Obama election team in 2008 and, to a greater extent, in 2012 to assess and identify potential voters and to develop methodologies to get them to vote. (It worked!)

On the negative side, this is not a book for beginners. There is an assumption of significant background knowledge by the reader in business, databases, and analytics. The material is well written, but the print is small. The book also comes in an e-book format, which I would recommend over the printed text simply for the option to increase the text size.

On the positive side, the authors offer an honest, in-depth analysis of converting to the use of big data, covering the various aspects of doing so. There are many figures throughout the book that help illustrate the concepts and interactions covered in the text. The book is a treasure trove of material on analytics and big data. I would recommend it to anyone who is thinking of moving into analytics and big data or whose company needs the capability provided by such analysis.

More reviews about this item: Amazon

Reviewer:  Michael Moorman Review #: CR142125 (1406-0410)
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