Learn By Example: Hadoop, MapReduce for Big Data problems

A hands-on workout in Hadoop, MapReduce and the art of thinking "parallel"

Created by: Loony Corn
An ex-Google, Stanford and Flipkart team

What Will I Learn?

• Develop advanced MapReduce applications to process BigData
• Master the art of "thinking parallel" - how to break up a task into Map/Reduce transformations
• Self-sufficiently set up their own mini-Hadoop cluster whether it's a single node, a physical cluster or in the cloud.
• Use Hadoop + MapReduce to solve a wide variety of problems : from NLP to Inverted Indices to Recommendations
• Understand HDFS, MapReduce and YARN and how they interact with each other
• Understand the basics of performance tuning and managing your own cluster

Requirements

• You'll need an IDE where you can write Java code or open the source code that's shared. IntelliJ and Eclipse are both great options.
• You'll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc
• A bit of exposure to Linux/Unix shells would be helpful, but it won't be a blocker

Target audience

• Yep! Analysts who want to leverage the power of HDFS where traditional databases don't cut it anymore
• Yep! Engineers who want to develop complex distributed computing applications to process lot's of data
• Yep! Data Scientists who want to add MapReduce to their bag of tricks for processing data