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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 (13) Chapters Close

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

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 https://issues.apache.org/jira/browse/HADOOP-9902.

There are more details available in the documentation at https://hadoop.apache.org/docs/r3.0.0/hadoop-project-dist/hadoop-common/UnixShellGuide.html. The documentation present at https://hadoop.apache.org/docs/r3.0.0/hadoop-project-dist/hadoop-common/UnixShellAPI.html 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 https://issues.apache.org/jira/browse/HADOOP-11804. 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.

You have been reading a chapter from
Big Data Analytics with Hadoop 3
Published in: May 2018
Publisher: Packt
ISBN-13: 9781788628846
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