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HDP Developer: Java

Overview

This 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 2.1 cluster running in a virtual machine that students can keep for use after the training.

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 required.

Target Audience

Experienced Java software engineers who need to develop Java MapReduce applications for Hadoop.

1
Day

Understanding Hadoop and MapReduce

Objectives

  • Understanding Hadoop 2.0 and HDFS
  • Writing MapReduce Applications
  • Map Aggregation

Labs

  • Demonstration: Understanding Block Storage
  • Configuring a Hadoop Development Environment
  • Putting Files in HDFS with Java
  • Demonstration: Understanding Map Reduce
  • Word Count
  • Distributed Grep
  • Inverted Index
  • Using a Combiner
  • Computing an Average

Working with Sorting and Input/Output Formats

Working with MapReduce

Working with Pig and Hive Programming

Live Training

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