Hadoop Programming Training

Answers to Popular Questions:

Yes, this class can be tailored to meet your specific training needs.
Yes, we provide Cloud consulting services.
Yes, group discounts are provided.

Course Description

This intense course is designed to give developers hands-on working knowledge for harnessing the power of Hadoop. The course begins with a discussion of the Hadoop ecosystem and then works its way to hands-on exploration of MapReduce with datasets and live clusters. The course also goes over some common configuration mechanisms, tools and debugging.
Course Length: 3 Days
Course Tuition: $1190 (US)


This course is intended for Java software developers who need to write applications that use distributed systems and MapReduce.

Course Outline

Course Topics
• MapReduce Jobs
• Counters
• Distributed Cache
• Combiner/Partitioner
• Configuration
• Debugging
• Input and Output Formats
• Tuning and Optimizing MapReduce
• Joins
• MapReduce Streaming
• Unit Testing
• Workflows
• Library Classes
• Filters and Sorting (if time permits)
• Other Hadoop Tools (if time permits)
• Architecting Solutions (if time permits)
Course Objectives
Upon completion of this course attendees will be able to:
• Describe the anatomy of a MapReduce job
• Perform joins by writing MapReduce code in Java
• Implement common algorithms in Hadoop
• Apply best practices for Hadoop development and debugging
• Describe other Hadoop tools including Hive and Pig
Course Outline
I. DAY 1
A. Review of MapReduce Basics
B. Anatomy of a MapReduce Job
C. MapReduce Web UI
D. Counters
E. Distributed Cache
A. Combiner/Partition
B. Configuration
C. Debugging
D. Writing Fool-Proof MapReduce Code
E. Input and Output Formats
F. Tuning and Optimizing MapReduce
G. Joins
A. MapReduce Streaming (using Ruby or Python if possible)
B. Unit Testing
C. Work Flow Tools
D. Useful Library Classes
A. Filters
B. Sorting
C. Higher Level MapReduce (Pig & Hive) and Other Tools
1. Advanced HQL
2. Serdes
3. Pig Basics
4. Mahout Basics
D. Architecting Solutions with MapReduce and Case Studies

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