Author(s) Name:  Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi

About the Book:

   Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data.
   You-ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You-ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work.
   Big Data also covers the central topic of big data visualization with Tableau, and you-ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software.
   Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include:
   • The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns
   • Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases
   • The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization.

TABLE OF CONTENTS

1. Introduction to the World of Big Data

  1.1 Understanding Big Data
  1.2 Evolution of Big Data

2. Big Data Storage Concepts

  2.1 Cluster Computing
  2.2 Distribution Models

3. NoSQL Database

  3.1 Introduction to NoSQL
  3.2 Why NoSQL

4 Processing, Management Concepts, and Cloud Computing

  4.1 Data Processing
  4.2 Shared Everything Architecture

5. Driving Big Data with Hadoop Tools and Technologies

  5.1 Apache Hadoop
  5.2 Hadoop Storage
  5.3 Hadoop Computation

6. Big Data Analytics

  6.1 Terminology of Big Data Analytics
  6.2 Big Data Analytics

7. Big Data Analytics with Machine Learning

  7.1 Introduction to Machine Learning
  7.2 Machine Learning Use Cases

8. Mining Data Streams and Frequent Itemset

  8.1 Itemset Mining
  8.2 Association Rules
  8.3 Frequent Itemset Generation

9. Cluster Analysis

  9.1 Clustering
  9.2 Distance Measurement Techniques
  9.3 Hierarchical Clustering

10. Big Data Visualization

  10.1 Big Data Visualization
  10.2 Conventional Data Visualization Techniques

ISBN:  978-1-119-70182-8

Publisher:  John Wiley & Sons

Year of Publication:  2021

Book Link:  Home Page Url