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Learn Hadoop, MapReduce for Big Data problems by Example
Learn Hadoop MapReduce for Big Data problems, and learn the art of 'thinking parallel' in this hands-on tutorial rich in examples
MapReduce is a programming model for distributed computing based on Java, and is also a processing technique. The algorithm contains two vital tasks, Map and Reduce. As the name "MapReduce" suggests, the Map task is always performed before the Reduce task. You can use Hadoop MapReduce to unravel surprising trends in real world data.
This Hadoop tutorial covers the width and depth of all components in Hadoop in a very detailed manner. It will also give you a bird's eye view of how they interact with each other.
Practical workout with Hadoop and MapReduce:
This MapReduce tutorial will teach you hands-on with Hadoop from the get go. You will learn how to install your cluster using VMs as well as the Cloud. All the essential features of MapReduce, as well as advanced features such as Total Sort and Secondary Sort, are covered.
By subscribing to this HBase tutorial, you will:
- Be able to Process BigData by developing advanced MapReduce applications
- Master the art of 'thinking parallel' - Break a task into Map/Reduce transformations
- Be able to independently set up your own mini-Hadoop cluster: be it a single node, a physical cluster or in the cloud
- Be able to utilize Hadoop and MapReduce to solve a variety of problems: right from NLP to Inverted Indices to Recommendations
Prerequisites and Target Audience
The prerequisites for this Hadoop tutorial:
- An IDE where you can write Java code or open the source code such as IntelliJ and Eclipse
- Some background in Object-Oriented Programming, preferably in Java is necessary. The source code used in the course is in Java and you will dive right into it without going into Objects, Classes etc
- Experience in Linux/Unix shells would be helpful, but it wouldn't restrict your learning.