Learn By Example: Hadoop, MapReduce for Big Data problems [Video]

More Information
  • Develop advanced MapReduce applications to process BigData
  • Master the art of thinking parallel and how to break up a task into Map/Reduce transformations
  • Self-sufficiently set up your 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

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. It's a 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. 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 in parallel.

Style and Approach

Hands-on workout involving Hadoop, MapReduce.

  • 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.
Course Length 13 hours 44 minutes
ISBN 9781788994491
Date Of Publication 25 Jan 2018



Loonycorn is Janani Ravi and Vitthal Srinivasan. Between them, they 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 spent 7 years at Google (New York, Singapore); Studied at Stanford and also worked at Flipkart and Microsoft. Vitthal also worked at Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too. They think they might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why they are so excited to be here. They hope you will try their offerings, and you'll like them.