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Learn By Example: Hadoop, MapReduce for Big Data Problems

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

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

Learning Outcomes

Develop advanced MapReduce applications to process Big Data.
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.

Pre-requisites:

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

Who is this course intended for?
Analysts who want to leverage the power of HDFS where traditional databases don't cut it anymore.
Engineers who want to develop complex distributed computing applications to process lot's of data.

Data Scientists who want to add MapReduce to their bag of tricks for processing data.


Your Instructor


Loonycorn
Loonycorn
Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years 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

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 on Learnsector!

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

Class Curriculum


  Run a MapReduce Job
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  Appendix
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

Get started now!