Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop

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...