Introduction to MapReduce Programming

Take our free course

MapReduce

Introduction to MapReduce Programming

with Glen Mules, Warren Pettit

Audience:
Hadoop programmer beginners

Time to complete:
5 hours

Available in:
English

This course explains the use of the mapper and reducer classes that make up a MapReduce application and where they get invoked in the application process. The student is walked through the development of a simple MapReduce application using a development environment based on Eclipse. Then the student goes through the same process but using a MapReduce development wizard to speed up development

Audience: Hadoop programmer beginners

Time to complete: 5 hours

Available in: English

Big Data University has been chosen by IBM as one of the issuers of badges as part of the IBM Open Badge program. Share your achievements through LinkedIn, Facebook, Twitter, and more!

Big Data University leverages the services of Pearson VUE Acclaim to assist in the administration of the IBM Open Badge program.  By enrolling into this course, you agree to Big Data University sharing your details with Pearson VUE Acclaim for the strict use of issuing your badge upon completion of the badge criteria.

This course was recently tested and updated for BigInsights Quick Start 4.0

http://www-01.ibm.com/software/data/infosphere/biginsights/quick-start/

 

Course Syllabus

  • Introduction to MapReduce
  • MapReduce Programming
  • MapReduce Programming Using BigInsights

General Information

  • This course is free.
  • It is self-paced.
  • It can be taken at any time.
  • It can be taken as many times as you wish.
  • Labs can be performed on the Cloud, or using a 64-bit system. If using a 64-bit system, you can install the required software (Linux-only), or use the supplied VMWare image. More details are provided in the course.
  • Students passing the course (by passing the final exam) will have immediate access to printing their online certificate of achievement. Your name in the certificate will appear exactly as entered in your profile in BigDataUniversity.com.
  • If you did not pass the course, you can take it again at any time.

Pre-requisites

  • None

Recommended skills prior to taking this course

Grading Scheme

  • The minimum passing mark for the course is 60%, where the final test is worth 100% of the course mark.
  • You have 3 attempts to take the test.