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Big Data Science & Analytics: A Hands-On Approach, by Arshdeep Bahga Vijay Madisetti
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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.
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Product details
Paperback: 542 pages
Publisher: VPT; 1 edition (April 15, 2016)
Language: English
ISBN-10: 0996025537
ISBN-13: 978-0996025539
Product Dimensions:
7 x 1.2 x 10 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#604,433 in Books (See Top 100 in Books)
There are very few comprehensive and current books about the broadrange of Big Data Analytics which are suitable for use as universitytextbooks for the the subject. Bahga and Madisetti's book is anexception, introducing the characteristics of Big Data projects,surveying modern analytic concepts and methods, and including avariety of illustrative and relevant case studies. While not goinginto great depth on the use of specific software tools, the bookdoes provide enough introductory guidance for further explorationof such topics as Hadoop, NoSQL, and many of their related toolsfor data management and analysis. Additional tool-specific referencescan then be used to augment their coverage.The authors' explanations are clear with numerous helpful diagramsand code examples using Python and other programs. They includebrief instructions for using online Big Data services such as thoseprovided by Hortonworks, Cloudera, Amazon Web Services, and Azure.As a textbook, this reference is quite suitable for introductoryData Analytics courses, as it presents the "big picture" of thisemerging and rapidly evolving area of technology. The instructors'website for the book does not yet include sample presentations norstudent exercises, but these are easily created from the basematerial.The authors clearly prefer Python and related tools over R foranalysis, and there is little mention of the latter in the book.However, that too is easily augmented by instructors who wish toinclude R in their courses for students with less programmingexperience.
I love this book. It is so much of a straightforward "how-to". One of the better intro books out there.
Really nice, good price
Not user friendly.I appreciate the content in the book, but it does not really say how to connect and such basic stuff with which readers will get stuck with for years.
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".
I like the way authors have presented the analytics patterns. There are individual chapters focused on batch, real-time and interactive analytics, that provide a good overview of the big data frameworks. The case studies are very helpful. Overall the book is easy to read.
Comprehensive and organized. Lots of examples and case studies in this relatively new area of big data analytics.
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