NewSale

Learn By Example: Hadoop, MapReduce for Big Data problems

Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.
    • Learning Style
      Self-Paced Learning
    • Difficulty
      Intermediate
    • Course Duration
      14 Hours
Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.
Start FREE Subscription Trial
Get started with our Learn Subscription Plan that includes this course, PLUS:

  • 328 high impact technical, end user and learning & business management courses
  • 100% online self-paced courses
  • Course completion certificates
  • Live tech support and you will be assigned your personal Learning Concierge
  • 7-Day FREE Trial
    Then Billed
    $24.99
    Every Month Until Canceled
  • Start FREE Trial
Purchase As Individual Course
  • Self-Paced Online Content
  • Attend Course Any Day or Any Time
  • Reports & Statistics
  • Certificate Upon Completion
  • Now Only $50.00 Regular Price $70.00
    Self-Paced Learning
  • Enroll Now
Purchase For Teams
Team Pricing Available - Request A Quote Today!

  • Group Discounts & Private Training Available
  • Free Learning Management Center
  • Group Reporting & Tracking
  • Author / Publish Your Own Courses
  • Request Team Enrollment

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". 

Course Objective:

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.
Audience:
  • 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 dat
Prerequisite:
  • 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
More Information
Lab Access No
Learning Style Self-Paced Learning
Difficulty Intermediate
Course Duration 14 Hours
Language English
Write Your Own Review
You're reviewing:Learn By Example: Hadoop, MapReduce for Big Data problems
Your Rating