Hadoop, MapReduce for Big Data problems : Learn By Example

​MapReduce is a programming model that is suitable for processing a large amount of data at a time. Hadoop is capable of running MapReduce programs written in various computer programming language such as Python, Ruby, C++ and Java. MapReduce programs are parallel in nature, thus are very helpful in performing large-scale data analysis using multiple machines and tools at a time in the cluster.

What you will learn in this course?

• Develop advanced MapReduce applications to process large-scale data analysis
• You develop parallel thinking and how to break up a task into Map/Reduce transformations
• How to use Hadoop and MapReduce at the same time to solve a wide variety of problems including NLP data assembling to Inverted Indices and Recommendations
• Understand HDFS, MapReduce and YARN and how they simultaneously interact with each other in one program
• Understand the basics of  managing your cluster
• Full concept development on performance tuning

About this course

Build your own Hadoop cluster: 

• Install Hadoop in Standalone mode , Pseudo-Distributed mode and Fully Distributed modes 
• Setting up a hadoop cluster using VMs in Linux.
• Set up a cloud Hadoop cluster on AWS with Cloudera Manager.
• Understand HDFS
• YARN and their interaction

By the end of this course on Mapreduce you will be able to do following stuff :

• You will be able to Generate Top 10 friend using a Collaborative filtering algorithm and Recommend those friends in a Social Networking site:
• You will be able to Build an Inverted Index for Search Engines that enables you to  to parallelize the humongous task of building an inverted index for a search engine. 
• You will be able to Generate bigrams from text and compute their frequency distribution in a corpus of text. 

Click the link or image below to access the course contents:
​Hadoop, MapReduce for Big Data problems : Learn By Example