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


One of the most valuable means through which we can make sense of big data, and thus make it more useful to most people, is data visualization. Visualization of data depends a lot on the use cases. Graphs and charts are visual representations of data. They provide a powerful means of summarizing and presenting data in a way that most people find easier to comprehend. Charts and graphs enable us to see the main features or characteristics of some data. They not only enable us to present the numerical findings of a study but also provide the shape and pattern of the data, which is critical in data analysis and decision making. There are many key considerations you need to keep in mind when developing data visualizations:

  • What type of graphical representation to use for which type of data
  • How to design a visualization approach that allows interactive features
  • How to search and modify datasets graphically
  • How to differentiate between data and the resultant insights
  • How to develop a visualization...