MapReduce and Hadoop Big Data Training in Bangalore

MapReduce and Hadoop Big Data Training in Bangalore


  • Weekend Classroom Course

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    INR 20000
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About The Event

About the Hadoop Big Data Course

Big Data is a buzzword making the tech circles today. About a decade back, analytics and processing of big data was thought to be unaffordable, infeasible and unnecessary. Big Data processing was prevalent in the realm of specific scientific computing requiring multi-billion dollar computing infrastructure like supercomputers. But modern systems pour in volumes of data and by exploiting previously known techniques in smart ways, Big Data analytics has become unavoidable, feasible and affordable using commodity computing infrastructure.


Though the adjective big? is associated with volumes of data, volumes are just one aspect of big data. In this course, we will explore other dimensions of Big Data. We will understand the concepts behind Map-Reduce, the most popular programming model for handling Big Data. To gain more insight and exposure to Map Reduce we will look into Hadoop, an open source Java-based MapReduce framework and work out some examples on this framework.


Course Objective

This is a well designed hands-on course focused on beginners and intermediate programmers. You will learn the following-

  1. How big data is different than traditional data?
  2. Limitations in existing database systems
  3. Advantages and Limitations of MapReduce
  4. Casting big data problems in MapReduce
  5. Coolest thing you will learn? Solving big data problems using MapReduce


Who Should Learn this Course?

This course is for students, professionals and entrepreneur interested in pursuing a career or building a company in big data. 

Prerequisite to attain this course

  1. Exposure to programming in any language
  2. Notebook with atleast 100GB Hard Disk, 2GB RAM preferably running Linux
  3. Curriculum for the MapReduce and Hadoop Big Data Course

Day 1:

  1. Introduction To Big Data: History of Data, Dimensions of Big Data, Brewers theorem and its relevance, Properties of Big Data Systems (Lecture #1)
  2. Map Reduce: Concepts in Functional Programming, Higher order functions, Map & Reduce Functions, MapReduce frameworks (Lecture #2)
  3. Cassandra case study with focus on trade offs under Brewer's theorem (Discussion #1)
  4. Solving problems using MapReduce (Lab #1)

Day 2:

  1. Introduction to Hadoop: Hadoop MapReduce Framework & HDFS (Lecture #3)
  2. Relational operators  using MapReduce (Discussion #2)
  3. Setup Hadoop on your laptop, Hadoop components (Lab #2:)
  4. Setup Hadoop on your laptop, Hadoop components (Lab #2)
  5. Project teams and proposals (Discussion#3)

Day 3:

Programming on Hadoop - Mappers, Reducers, Combiners, Partitioners (Lecture #4)
Hadoop Ecosystem covering Hive, Pig, Zookeeper etc. (Lecture#5)
Programming in Hadoop Exercises (Lab#3)
Guest Lecture on BigData (Lecture#6)

Day 4:

Final Quiz: 1.5 hours quiz
Project Midpoint and Feedback (Discussion#4)
Project Midpoint and Feedback (Discussion#5)
Resources, Quiz Answers, Course Feedback (Discussion#6)

Day 5:

Demo Day

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