Learn By Example: Hadoop, MapReduce for Big Data problems

Taught by a 4 person team including 2 Stanford-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 and deep. It covers the individual components of Hadoop in great detail, and also gives 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 to set up your own cluster 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 completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to "think parallel". 

 

What's Covered:

 

Lot's of cool stuff ..

 

Using MapReduce to 

 

 

Recommend friends in a 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. 

Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text. 

 

 

Build your Hadoop cluster: 

 

Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes 

Set up a hadoop cluster using Linux VMs.

Set up a cloud Hadoop cluster on AWS with Cloudera Manager.

Understand HDFS, MapReduce and YARN and their interaction 

 

 

Customize your MapReduce Jobs: 

 

Chain multiple MR jobs together

Write your own Customized Partitioner

Total Sort : Globally sort a large amount of data by sampling input files

Secondary sorting 

Unit tests with MR Unit

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, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster. 

 

 

Who is the 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

 

About the Instructor

 

Loony Corn

A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT

 

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

 

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

 

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

 

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

 

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

 

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here.

 

We hope you will try our offerings, and think you'll like them :-)