Learn By Example: Hadoop, MapReduce for Big Data problems

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

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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.


Your Instructor(s)


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 Udemy!

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


Course Curriculum


  Introduction
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  Why is Big Data a Big Deal
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  Installing Hadoop in a Local Environment
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  The MapReduce "Hello World"
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  Run a MapReduce Job
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  Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
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  HDFS and Yarn
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  MapReduce Customizations For Finer Grained Control
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  The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!
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  Input and Output Formats and Customized Partitioning
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  Recommendation Systems using Collaborative Filtering
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  Hadoop as a Database
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  K-Means Clustering
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  Setting up a Hadoop Cluster
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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.