Learn By Example: Hadoop, MapReduce for Big Data problems

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

What you'll 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

Taught bya 4 person team including 2Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

This course is a zoom-in, zoom-out,hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.

Let’s parse that.

Zoom-in, Zoom-Out:This course is both broad anddeep. It covers the individual components of Hadoop in great detail, and alsogives you a higher level picture of how they interact with each other.

Hands-on workout involving Hadoop, MapReduce :This course will get you hands-on with Hadoop very early on.You'll learn how toset up your owncluster using both VMs and the Cloud. All the major features of MapReduce are covered - including advanced topics like Total Sort and Secondary Sort.

The art of thinking parallel:MapReduce completelychanged the way people thought about processing Big Data. Breaking down any problem into parallelizable units isan art. The examples in this coursewill train you to "think parallel".

What's Covered:

Lot's of cool stuff ..

• Using MapReduce to

  
  

  • Recommend friends ina Social Networking site:Generate Top 10 friend recommendations using a Collaborative filtering algorithm.
  • Build an Inverted Index for Search Engines:Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.
  • GenerateBigrams from text:Generate bigrams and computetheir frequency distribution in a corpus of text.

• Build yourHadoop cluster:

  
  

  • InstallHadoop in Standalone, Pseudo-Distributed and Fully Distributed modes
  • Setup a hadoop cluster using Linux VMs.
  • Set up a cloud Hadoopcluster on AWSwith Cloudera Manager.
  • UnderstandHDFS, MapReduce and YARNand their interaction

• Customize your MapReduce Jobs:

  
  

  • Chain multiple MRjobs together
  • Write your ownCustomized Partitioner
  • Total Sort:Globally sorta large amount of data by sampling input files
  • Secondary sorting
  • Unit tests with MRUnit
  • Integrate with Python using the Hadoop Streaming API

.. and of course all the basics:

• MapReduce :Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort

• HDFS &YARN:Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARNScheduling,Configuring HDFSand YARNto performance tuneyour cluster.