Hadoop Fundamentals LiveLessons 2nd Edition

Hadoop Fundamentals LiveLessons 2nd Edition

7+ Hours of Video Instruction. Hadoop Fundamentals LiveLessons provides users, developers, and administrators with a practical introduction to the many facets of Hadoop Versions 1 and 2. 
Hadoop Fundamentals LiveLessons provides 7+ hours of video introduction to the Apache Hadoop (Versions 1 and 2) Big Data ecosystem. The tutorial includes background information and explains the core components of Hadoop, including Hadoop Distributed File Systems (HDFS), MapReduce, the new YARN resource manager, and YARN Frameworks. In addition, it demonstrates how to use Hadoop at several levels, including the native Java interface, C++ pipes, and the universal streaming program interface. Examples include how to use benchmarks and high-level tools, including the Apache Pig scripting language, Apache Hive “SQL-like” interface, Apache Flume for streaming input, Apache Sqoop for import and export of relational data, and Apache Oozie for Hadoop workflow management. The steps for easily installing a working Hadoop system on a desktop/laptop, in a Cloud environment, and on a local stand-alone cluster are presented. Installation and administration examples using the powerful Ambari GUI are also included. All software used in these LiveLessons is open source and freely available for your use and experimentation. Finally, the important differences between Hadoop Version 1 MapReduce and the new Hadoop Version 2 MapReduce Framework are highlighted.
Skill Level
Beginner
Intermediate
What You Will Learn
Hadoop design and components
How the MapReduce process works in Hadoop
The differences between and advantages of Hadoop Versions 1 and 2
Key aspects of the new YARN design and Frameworks
How to use, administer, and program HDFS
How to run and administer Hadoop programs
How to write basic MapReduce programs
How to install Hadoop on a laptop/desktop, on a cluster, or in the Cloud
How to run Apache Pig, Hive, Flume, Sqoop, and Oozie applications
How to install and administer Hadoop with the Apache Ambari GUI tool

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