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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (18) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services Index

Chapter 10. Visualizing Big Data

This chapter explores one of the most important activities in big data processing and analysis, which is creating a powerful visualization of data and insights. We tend to understand anything graphical better than anything textual or numerical. During the analytical process, you will need to constantly make sense of data and manipulate its usage and interpretation; this will be much easier if you can visualize the data instead of reading it from tables, columns, or text files. When you have used one of the many ways of analyzing data and generated insights that we have seen so far (such as through Python, R, Spark, Flink, Hive, MapReduce, and so on), anyone trying to make sense of the insights will want to understand those in the context of the data. For this purpose, you need some pictorial representation for that as well.

In a nutshell, the following topics will be covered throughout this chapter:

  • Introduction
  • Tableau
  • Chart types
  • Using Python
  • Using R
  • Data visualization...
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