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Big data science & analytics : a hands-on approach
Bahga A., Madisetti V., VPT, Johns Creek, GA, 2016. 542 pp. Type: Book (978-0-996025-54-6)
Date Reviewed: Jun 20 2017

This book provides rich knowledge on big data analytics from theory to practical applications. Readers can get a sense of this from its subtitle. The book is organized in three parts.

Part 1, “Big Data Analytics Concepts,” consists of four chapters. Chapter 1 includes an introduction to big data from a general perspective. To avoid introducing dry concepts, the authors prepare domain-specific examples as teasers to big data analytics (BDA) topics. Chapter 2 includes mainstream big data stacks in various computing environments. Chapter 3 discusses patterns in big data. The authors summarize various patterns and operations used in BDA and big data processing. Chapter 4 is devoted to introducing NoSQL, which is the next-generation database to handle big data challenges. Importantly in Part 1, the authors introduce four analytics patterns: alpha, beta, gamma, and delta. These patterns, together with the corresponding frameworks and tools, form the “pedagogical foundation of this book.”

Part 2 discusses the software tools needed to implement BDA in practice. Chapter 5 introduces data acquisition software, including publish-subscribe messaging frameworks, source-sink connectors, database connectors, messaging queries, and customized REST-based connectors. Chapter 6 introduces the details of a popular big data file system, HDFS. Chapter 7 introduces batch analytic tools for big data. Chapter 8 further introduces real-time analytics tools for fast processing in data-intensive environments. Chapter 9 introduces interactive querying tools for BDA, and chapter 10 demos the tools working at the back end of BDA systems, including serving databases and web frameworks.

Part 3 focuses on advanced topics in BDA, including analytics algorithms, machine learning topics, and recommendation systems and data visualization. It introduces the fundamental knowledge of machine learning applied to BDA.

The “hands-on” style of this book will benefit senior undergraduate and graduate students who are interested in learning BDA. The authors include practical cases for readers to follow and practice, which will help them to learn real-world BDA. In addition, throughout the book, the authors use Python as the universal programming language when describing algorithms and demonstrating examples. This significantly eases the understanding of the programming content. Readers with basic knowledge of Python should be able to dive into the examples. Finally, the book includes a large collection of enterprise-level software for BDA, such as Sparks, Kafka, Cassandra, and so on.

As mentioned by the authors in the preface, the fourth Industrial Revolution is emerging and can be seen in the widespread adoption of BDA. I believe this book will tremendously benefit those who are interested in learning about BDA. Furthermore, the book can also be adopted as a standard textbook for a BDA course.

Reviewer:  Feng Yu Review #: CR145359 (1708-0510)
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