Apache Hadoop 2.0: Developing Applications with the Hortonworks Data Platform using Java

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Course type Essentials (What does this mean?)

Course details
Course title Apache Hadoop 2.0: Developing Applications with the Hortonworks Data Platform using Java
Delivery method Classroom Classroom
Days/Duration 4

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course description

Print course outline | Download as PDF document | Link to page: www.qa.com/HWAHDJAV2

Course dates

Currently scheduled dates for this training course
Location SEP OCT NOV DEC View later dates


International House, E1W

19 17 23 show prices/book


This 4 days hands-on training course takes a deep-dive into developing Java MapReduce applications for Big Data deployed on the Hadoop Distributed File System (HDFS).

Students who attend this course will learn how to harness the power of Apache Hadoop™ and MapReduce to manipulate, analyze and perform computations on their Big Data.


This course assumes students have experience developing Java applications and using a Java IDE. Labs are completed using the Eclipse IDE and Maven

Target Audience

Experienced Java developers responsible for developing MapReduce applications and performing analysis of Big Data stored on Apache Hadoop

Delegates will learn how to

  • Write a Java MapReduce application using Eclipse and Maven
  • Develop a Combiner to perform map aggregation
  • Customize input and output formats of a MapReduce job
  • Compute mathematical computations on your Big Data files
  • Use best practices to optimize MapReduce jobs
  • Create JUnit tests for a MapReduce job
  • Discover trends in your Big Data
  • Define an Oozie workflow
  • Access Apache HBase™ data from a Java MapReduce job
  • Write custom, user-defined functions for Apache Pig™ and Apache™ Hive

Lab Content

Students will work through the following exercises using Eclipse, Maven and the Hortonworks Data Platform:

  • Configuring a Hadoop ™ Development Environment
  • Word Count
  • Distributed Grep
  • Inverted Index
  • Using a Combiner
  • Computing an Average
  • Writing a Custom Partitioner
  • Using a TotalOrderPartitioner
  • Custom Sorting
  • Writing a Custom InputFormat
  • Customizing Output
  • Simple Moving Average
  • Using Data Compression
  • Defining a RawComparator
  • A Map-Side Join
  • Using a Bloom Filter
  • Unit Testing
  • Defining an Oozie Workflow
  • Term Frequency-Inverse Document Frequency (TF-IDF)
  • Accessing HBase from Java MapReduce
  • Writing a User-Defined Pig Function
  • Writing a User-Defined Hive Function

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