Hadoop Made Very Easy

Learn Hadoop, Pig, Hive and Mahout with a hands on approach without spending too much time and boost your career
3.8 (23 ratings)
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  • Lectures 47
  • Length 8 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 2/2015 English

Course Description

This course teaches you Hadoop, Pig, Hive and Apache Mahout from scratch with an example based and hands on approach.

"From Scratch to Practical"
"This course is hell awesome, if you are new to Hadoop this course is for you, from theory to hands on experience , plus a Mahout and recommended system as Project. This course is a five star.!!!" - Aakash


"Easy to understand, makes Hadoop & Mahout simple"
"This course has helped me crack a couple of Big Data engineer interviews as the basics are well explained here. The video/audio quality is fine and the instructor knows his stuff!"- Shipra


"Brilliant course for Data Engineers"

"This is course is well structured. I would like to call this Big Data and Hadoop for Dummies. It covers basics as well as advanced concepts in a very unique way. Hands on examples gave me clear direction about how to use Hadoop in production environment. I strongly recommend this course to all levels of data engineers and Big data enthusiasts.Production quality is good." - Ashrith


Master the Fundamental Concepts of Big Data, Hadoop and Mahout with ease

  • Understand the Big Data & Apache Hadoop landscape
  • Learn HDFS & MapReduce concepts with examples and hands on labs
  • Learn Hadoop Streaming
  • Understand Analytics with Hadoop using Pig and Hive
  • Machine Learning Concepts
  • Collaborative Filtering with Apache Mahout
  • Real world Recommender System with Mahout and Hadoop

Big Data and Data Science Foundation to empower you with the most specialized skills

The core concepts are stressed upon and the focus is on building a solid foundation of the key Hadoop, Map Reduce and collaborative filtering concepts upon which you can learn just about every other technology in the same space. Preliminary Java and Unix knowledge is expected.

Contents & Overview

Through 47 lectures and 8 hours of content, we will take a step-by step approach to understanding Big Data and related concepts from scratch.

The first few topics will focus on the rise of Big Data and how Apache Hadoop fits in. We will focus on the fundamentals of Hadoop and its core components: HDFS and Map Reduce. We will then setup and play around with Hadoop and HDFS and then deep dive into MapReduce programming with hands on examples. We will also spend time on Combiners and Partitioners and how they can help. We will also spend time on Hadoop Streaming: a tool that helps non-Java professionals to leverage the power of Hadoop and do POCs on it.

Once we have a solid foundation of HDFS and MapReduce, in the next couple of topics we will explore higher level components of the Hadoop ecosystem: Hive and Pig. We will go into the details of both Hive and Pig by installing them and working with examples. Hive and Pig can make your life easy by shielding you from the complexity of writing MR jobs and yet leveraging the parallel processing ability of the Hadoop framework.

In the next few lectures we will look at something very interesting: Apache Mahout and Machine Learning. Apache Mahout is a Java library that lets you write machine learning applications with ease. We will learn the basics of Machine Learning and go deeper into Collaborative Filtering and recommender systems, something that Mahout excels that.

We will look at some similarity algorithms, understand their real-life implications and apply them when we will build together a real world movie recommender system using Mahout and Hadoop.

After taking this course, which includes slides, examples, code and data sets, you will be at ease with playing aroundwith HDFS, writing MapReduce jobs, analyzing data with Hive and Pig, and building a recommender system using Apache Mahout. So go ahead and enroll to crack that Big Data/Data Science interview and clear that certification exam!

What are the requirements?

  • A basic knowledge of Unix will make the course smoother
  • Basic knowledge of Object Oriented programming and Java will be handy

What am I going to get from this course?

  • Easily crack Big Data interviews & get ready for Hadoop Certification exams!
  • Get practical knowledge of Big Data, Hadoop and Mahout without spending too much time and money and become a high paying Big Data resource!
  • Program in Map Reduce, Hive, Pig and Apache Mahout
  • Pick up deeper details of Map Reduce, Machine Learning and Data Science quickly
  • Learn about personalization with Apache Mahout and building recommendation systems

Who is the target audience?

  • Any one interested in learning about Big Data and Hadoop concepts
  • Any one interested in learning about Apache Mahout and how to build recommendation systems using Mahout over Hadoop
  • Any one wanting to clear the Hadoop Certification exam
  • Any one wanting to clear Big Data related interviews with ease

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Course Introduction
Introduction to the Course
Course Outline and Agenda
Section 2: Introduction to Big Data
Big Data - What does it mean?
Who uses Big Data, the 4Vs and trends
Section 3: Introduction to Hadoop
Need & Introduction to Hadoop
History & Overview of the Architecture
Section 4: HDFS & MapReduce overview
Introduction to HDFS
Introduction to the MapReduce Model
Introduction to the MapReduce Architecture
Section 5: Hadoop Installation
Requirements, Virtual Machine install
Install Java, SSH & download Hadoop
Hadoop setup modes, configuring conf files
Hadoop configure and start HDFS, troubleshooting tips
MapReduce Daemons
Section 6: HDFS & Map Reduce Architecture
HDFS Architecture
HDFS Playing Around - Hands on
Map Reduce Word Count program
Hadoop Reading/Writing Files & Job execution
Section 7: Map Reduce Programming
MapReduce Word Count Code Overview - Hands On
MapReduce Code in detail - Hands On
MapReduce Word Count Program Create Jar and Run - Hands On
MapReduce Code and Concepts - Revision
Combiner and Partitioner
Section 8: Hadoop Streaming
Hadoop Streaming with Example- Hands On
Section 9: Hive
Hive and Pig - Need & Introduction
Hive Introduction
Hive Architecture
Hive Download & Install
Hive Configuring, Partitions and Buckets
Hive Playing Around With Patent Data - Hands On
Hive: Some More Querying - Hands On
Hive Managed and External Tables
Section 10: Pig
Pig Introduction
Pig Setup Modes, Pig Latin and the Grunt Shell
Pig Example - Hands On
Pig Data Types & Diagnostic Operators - Hands On
Pig Operators & User Defined Functions(UDFs)
Pig Vs Hive - When to use Which
Section 11: Introduction to Apache Mahout
Introduction to Machine Learning & Apache Mahout
Introduction to Recommendation Systems
Collaborative Filtering Explained
How to create a recommendation system using Apache Mahout
Commonly used Similarity Algorithms
Mahout over Hadoop Movie Recommendation Example setup- Hands On
Run the Movie Recommendation system - Hands On
Mahout Conclusion
Section 12: Thank You
Thank You and Feel Free to Reach Out

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Instructor Biography

Abhishek Roy, Experienced Big Data trainer, technologist & entrepreneur

With over 11 years of tech industry experience, I have been lucky to pick the brains of some of the best minds in tech and have been learning and teaching Big Data and related technologies for about 4 years now. Post becoming an entrepreneur at FeetApart, I have learnt the value of time and truly believe learning something you are passionate about is the best investment of time. I have a passion for teaching/sharing that you will see in my lectures and I go the extra mile when needed. I love long distance running and inspiring others to pick it up.

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