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We are living in the dawn of what has been termed as the "Fourth Industrial Revolution", which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.
- Sales Rank: #201306 in Books
- Published on: 2016-04-15
- Original language: English
- Dimensions: 10.00" h x 1.23" w x 7.00" l, 2.05 pounds
- Binding: Paperback
- 542 pages
About the Author
Arshdeep Bahga is a Research Scientist with Georgia Institute of Technology. His research interests include cloud computing and big data analytics. Arshdeep has authored several scientific publications in peer-reviewed journals in the areas of cloud computing and big data. Vijay Madisetti is a Professor of Electrical and Computer Engineering at Georgia Institute of Technology. Vijay is a Fellow of the IEEE, and received the 2006 Terman Medal from the American Society of Engineering Education and HP Corporation.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Suitable for use as an introductory Big Data Analytics textbook
By Harry J. Foxwell, PhD
There are very few comprehensive and current books about the broad
range of Big Data Analytics which are suitable for use as university
textbooks for the the subject. Bahga and Madisetti's book is an
exception, introducing the characteristics of Big Data projects,
surveying modern analytic concepts and methods, and including a
variety of illustrative and relevant case studies. While not going
into great depth on the use of specific software tools, the book
does provide enough introductory guidance for further exploration
of such topics as Hadoop, NoSQL, and many of their related tools
for data management and analysis. Additional tool-specific references
can then be used to augment their coverage.
The authors' explanations are clear with numerous helpful diagrams
and code examples using Python and other programs. They include
brief instructions for using online Big Data services such as those
provided by Hortonworks, Cloudera, Amazon Web Services, and Azure.
As a textbook, this reference is quite suitable for introductory
Data Analytics courses, as it presents the "big picture" of this
emerging and rapidly evolving area of technology. The instructors'
website for the book does not yet include sample presentations nor
student exercises, but these are easily created from the base
material.
The authors clearly prefer Python and related tools over R for
analysis, and there is little mention of the latter in the book.
However, that too is easily augmented by instructors who wish to
include R in their courses for students with less programming
experience.
2 of 2 people found the following review helpful.
Buy it: This is a great intro and reference into Big Data Sciednce and Analytics
By dtm
I love this book. It is so much of a straightforward "how-to". One of the better intro books out there.
1 of 2 people found the following review helpful.
Good coverage of subject
By A. K.
The book was used but it looks like new.
A very good coverage of the subject. I would highly recommend this book for anyone interested in knowing more about Big Data.
This is a good book in terms of "foundation".
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