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
Contributors
Preface
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Index

Streaming


In the modern world, an increasing number of people are becoming interconnected to one another via the internet. With the advent of the smartphone, this trend has skyrocketed. Nowadays, the smartphone can be used to do many things, such as check social media, order food online, and call a cab online. We are finding ourselves more reliant on the internet than ever before, and we will only become more reliant in the future. With this development comes a massive increase in data generation. As the internet began to boom, the very nature of data processing changed. Any time one of the apps or service is accessed on the phone, real-time data processing is taking place. Because there is a lot at stake in terms of the quality of their applications, companies are forced to improve data processing, and with improvements come paradigm shifts. One paradigm that is currently being researched and used is the idea of a highly scalable, real-time (or as close to real-time as possible) processing...