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

Other changes

There are other changes coming up in Hadoop 3, which are mainly to make it easier to maintain and operate. Particularly, the command-line tools have been revamped to better suit the needs of operational teams.

Minimum required Java version 

All Hadoop JARs are now compiled to target a runtime version of Java 8. Hence, users that are still using Java 7 or lower must upgrade to Java 8.

Shell script rewrite

The Hadoop shell scripts have been rewritten to fix many long-standing bugs and include some new features. 

Incompatible changes are documented in the release notes. You can find them at

There are more details available in the documentation at The documentation present at will appeal to power users, as it describes most of the new functionalities, particularly those related to extensibility.

Shaded-client JARs

The new hadoop-client-api and hadoop-client-runtime artifacts have been added, as referred to by These artifacts shade Hadoop's dependencies into a single JAR. As a result, it avoids leaking Hadoop's dependencies onto the application's classpath.

Hadoop now also supports integration with Microsoft Azure Data Lake and Aliyun Object Storage System as an alternative for Hadoop-compatible filesystems.