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