Network Data Analytics

A Hands-On Approach for Application Development

  • K. G. Srinivasa
  • Siddesh G. M.
  • Srinidhi H.

Part of the Computer Communications and Networks book series (CCN)

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Introduction to Data Analytics

    1. Front Matter
      Pages 1-1
    2. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 3-28
    3. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 29-53
    4. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 55-72
    5. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 73-83
    6. Pig
      K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 85-94
    7. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 95-107
    8. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 109-123
  3. Machine Learning

    1. Front Matter
      Pages 125-125
    2. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 127-138
    3. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 139-154
    4. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 155-175
    5. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 177-216
  4. Advanced Analytics

    1. Front Matter
      Pages 217-217
    2. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 219-264
    3. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 265-281
    4. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 283-302
    5. K. G. Srinivasa, Siddesh G. M., Srinidhi H.
      Pages 303-318
  5. Data Visualization

    1. Front Matter
      Pages 319-319

About this book

Introduction

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts.
 
Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

Keywords

Hadoop Data Analytics Data Visualization High Performance Computing Machine Learning Algorithms

Authors and affiliations

  • K. G. Srinivasa
    • 1
  • Siddesh G. M.
    • 2
  • Srinidhi H.
    • 3
  1. 1.Department of Information TechnologyCh. Brahm Prakash Government Engineering CollegeJaffarpurIndia
  2. 2.Department of Information Science and EngineeringRamaiah Institute of TechnologyBangaloreIndia
  3. 3.Department of Computer Science and EngineeringRamaiah Institute of TechnologyBangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-77800-6
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-77799-3
  • Online ISBN 978-3-319-77800-6
  • Series Print ISSN 1617-7975
  • Series Online ISSN 2197-8433
  • About this book
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