Big Data with Hadoop MapReduce: A Classroom Approach, 1st Edition (Hardback) book cover

Big Data with Hadoop MapReduce

A Classroom Approach, 1st Edition

By Rathinaraja Jeyaraj, Pugalendhi, PhD Ganeshkumar, Anand Paul

Apple Academic Press

339 pages | 8 Color Illus. | 100 B/W Illus.

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Hardback: 9781771888349
pub: 2020-01-15
Available for pre-order. Item will ship after 15th January 2020
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Description

The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines.

This book covers almost all necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc.

Ultimately, readers will be able to:

  • understand what big data is and the factors that are involved
  • understand the inner workings of MapReduce, which is essential for certification exams
  • learn the MapReduce program’s features along its weaknesses
  • set up Hadoop clusters with 100s of physical/virtual machines
  • create a virtual machine in AWS and set up Hadoop MapReduce
  • write MapReduce with Eclipse in a simple way
  • understand other big data processing tools and their applications
  • understand various job positions in data science

Regardless of the user’s domain and expertise level in Hadoop MapReduce, this volume will broaden their knowledge and understanding of writing MapReduce programs to process big data.

The authors advise that while it is not necessary to be an expert, readers should have some minimal knowledge of working in Ubuntu, Java, and Eclipse to set up clusters and write MapReduce jobs. The authors have emphasized more on Hadoop v2 when compared to Hadoop v1, in order to meet today’s trend.

Table of Contents

Preface. 1. Introduction to Big Data. 2. Hadoop Framework. 3. Hadoop 1.2.1 Installation. 4. Hadoop Ecosystem. 5. Hadoop 2.7.0. 6. Hadoop. 2.7.0 Installation. 7. Data Science. 8. MapReduce Exercise. 9. Case Study: Application Development for NYSE Dataset.

About the Authors

Rathinaraja Jeyaraj is currently a Research Scholar in the Department of Information Technology at the National Institute of Technology Karnataka, India. He recently worked as a visiting researcher at connected computing and media processing lab, Kyungpook National University, South Korea and supervised by Prof. Anand Paul. His research interests include big data processing tools, cloud computing, IoT, and machine learning. He completed his BTech and MTech at Anna University, Tamil Nadu, India. He has also earned an MBA in Information Systems and Management at Bharathiar University, Coimbatore, India.

Ganeshkumar Pugalendhi, PhD, is Assistant Professor in the Department of Information Technology, Anna University Regional Campus, Coimbatore, India. He received his BTech, MS (by research), and PhD degrees from Anna University, India, and did his postdoctoral work at Kyungpook National University, South Korea. He is the recipient of a Student Scientist Award from the Tamil Nadu State Council for Science and Technology; best paper awards from IEEE, the Institution of Engineering and Technology, and the Korean Institute of Industrial and Systems Engineers; travel grants from the Department of Biotechnology (DBT) (India) and the Council of Scientific & Industrial Research (India); and a workshop grant from DBT. He has visited many countries (Singapore, South Korea, USA, Serbia, Japan, and France) for research interaction and for presenting papers. He is the resource person for delivering technical talks and seminars sponsored by various organizations, including the University Grants Commission of India, All India Council for Technical Education, Technical Education Quality Improvement Programme of Government of India, Indian Council of Medical Research, and many others. He has written two research-oriented textbooks: Data Classification Using Soft Computing and Soft Computing for Microarray Data Analysis. His current research focus is on data analytics, machine learning, and IoT.

Anand Paul, PhD, is currently working in the School of Computer Science and Engineering at Kyungpook National University, South Korea, as Associate Professor. He earned his PhD in Electrical Engineering from the National Cheng Kung University, Taiwan, R.O.C. His research interests include big data analytics, IoT, and machine learning. He has done extensive work in big data and IoT-based smart cities. He was a delegate representing South Korea for the M2M focus group in 2010–2012 and has been an IEEE senior member since 2015. He is serving as associate editor for the journals IEEE Access, IET Wireless Sensor Systems, ACM Applied Computing Reviews, Cyber Physical Systems (Taylor & Francis), Human Behaviour and Emerging Technology (Wiley), and the Journal of Platform Technology. He has also guest edited various international journals. He is the track chair for smart human computer interaction with the Association for Computing Machinery Symposium on Applied Computing 2014–2019, and general chair for the 8th International Conference on Orange Technology (ICOT 2020). He is also an MPEG delegate representing South Korea.

Subject Categories

BISAC Subject Codes/Headings:
COM021000
COMPUTERS / Database Management / General
COM032000
COMPUTERS / Information Technology
COM039000
COMPUTERS / Management Information Systems
COM055000
COMPUTERS / Certification Guides / General
SCI000000
SCIENCE / General