Java

Course details

Overview

This advanced course provides Java programmers a deep-dive into Hadoop application development. Students will learn how to design and develop efficient and effective MapReduce applications for Hadoop using the Hortonworks Data Platform, including how to implement combiners, partitioners, secondary sorts, custom input and output formats, joining large datasets, unit testing, and developing UDFs for Pig and Hive. Labs are run on a 7-node HDP cluster running in a virtual machine that students can keep for use after the ;

Duration  

4 days

Course Objectives

• Describe Hadoop 2 and the Hadoop Distributed File System
• Describe the YARN framework
• Develop and run a Java MapReduce application on YARN
• Use combiners and in-map aggregation
• Write a custom partitioner to avoid data skew on reducers
• Perform a secondary sort
• Recognize use cases for built-in input and output formats
• Write a custom MapReduce input and output format
• Optimize a MapReduce job
• Configure MapReduce to optimize mappers and reducers
• Develop a custom RawComparator class
• Distribute files as LocalResources
• Describe and perform join techniques in Hadoop
• Perform unit tests using the UnitMR API
• Describe the basic architecture of HBase
• Write an HBase MapReduce application
• List use cases for Pig and Hive
• Write a simple Pig script to explore and transform big data
• Write a Pig UDF (User-Defined Function) in Java
• Write a Hive UDF in Java
• Use JobControl class to create a MapReduce workflow
• Use Oozie to define and schedule workflows

Prerequisites

Students must have experience developing Java applications and using a Java IDE. Labs are completed using the Eclipse IDE and Gradle. No prior Hadoop knowledge is ;

Format

50% Lecture/Discussion  
50% Hands‐on Labs 

  Updated on 27 June, 2018