Big Data with Hadoop MapReduce

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A01=Anand Paul
A01=Ganeshkumar Pugalendhi
A01=Rathinaraja Jeyaraj
Apache Hadoop
Author_Anand Paul
Author_Ganeshkumar Pugalendhi
Author_Rathinaraja Jeyaraj
AWS cloud computing
Big Data
big data certification prep
Big Data Framework
Big Data Infrastructure
Big Data Processing
big data processing tools
Big Data Systems
Category=UNC
cluster setup guide
Compute Intensive Tasks
Data Block
data science fundamentals
distributed computing
Driver Function
Eclipse Project
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File Input Format
GB Memory
Hadoop Cluster
Hadoop MapReduce market
HDFS
Local File System
Logical Cores
Map Tasks
Mapreduce
MapReduce Jobs
MapReduce programming exercises
Perform Text Analytics
Public Void Map
Reduce Tasks
Slave Node
virtual machine deployment
Volunteer Computing

Product details

  • ISBN 9781774634844
  • Weight: 790g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Mar 2022
  • Publisher: Apple Academic Press Inc.
  • Publication City/Country: CA
  • Product Form: Paperback
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The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have 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 the 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 features and weaknesses of MapReduce

• set up Hadoop clusters with 100s of physical/virtual machines

• create a virtual machine in AWS

• write MapReduce with Eclipse in a simple way

• understand other big data processing tools and their applications

Rathinaraja Jeyaraj is 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. His research interests include big data processing tools, cloud computing, IoT, and machine learning.

Ganeshkumar Pugalendhi, PhD, is an Assistant Professor in the Department of Information Technology, Anna University Regional Campus, Coimbatore, India. 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.

Anand Paul, PhD, is an Associate Professor at the School of Computer Science and Engineering at Kyungpook National University, South Korea. He was a delegate representing South Korea for the M2M focus group in 2010–2012 and is serving as associate editor for the journals IEEE Access, IET Wireless Sensor Systems, ACM Applied Computing Reviews, Cyber Physical Systems, Human Behaviour and Emerging Technology, and the Journal of Platform Technology. 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).

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