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Learn Hadoop, MapReduce for Big Data problems by Example

Learn Hadoop MapReduce for Big Data problems, and learn the art of 'thinking parallel' in this hands-on tutorial rich in examples

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34 learners
Course Introduction

MapReduce is a programming model for distributed computing based on Java, and is also a processing technique. The algorithm contains two vital tasks, Map and Reduce. As the name "MapReduce" suggests, the Map task is always performed before the Reduce task. You can use Hadoop MapReduce to unravel surprising trends in real world data.

This Hadoop tutorial covers the width and depth of all components in Hadoop in a very detailed manner. It will also give you a bird's eye view of how they interact with each other.

Practical workout with Hadoop and MapReduce:

This MapReduce tutorial will teach you hands-on with Hadoop from the get go. You will learn how to install your cluster using VMs as well as the Cloud. All the essential features of MapReduce, as well as advanced features such as Total Sort and Secondary Sort, are covered.

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Course Objectives

By subscribing to this HBase tutorial, you will:

  • Be able to Process BigData by developing advanced MapReduce applications
  • Master the art of 'thinking parallel' - Break a task into Map/Reduce transformations
  • Be able to independently set up your own mini-Hadoop cluster: be it a single node, a physical cluster or in the cloud
  • Be able to utilize Hadoop and MapReduce to solve a variety of problems: right from NLP to Inverted Indices to Recommendations

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Prerequisites and Target Audience

The prerequisites for this Hadoop tutorial:

  • An IDE where you can write Java code or open the source code such as IntelliJ and Eclipse
  • Some background in Object-Oriented Programming, preferably in Java is necessary. The source code used in the course is in Java and you will dive right into it without going into Objects, Classes etc
  • Experience in Linux/Unix shells would be helpful, but it wouldn't restrict your learning.

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Course Plan
Certificate of completion

1. Introduction
1 video
You, this course and Us 01:53
2. Why is Big Data a Big Deal
6 videos
What is Hadoop? 07:25

HDFS or the Hadoop Distributed File System 11:00

MapReduce Introduced 11:39

YARN or Yet Another Resource Negotiator 04:00
3. Installing Hadoop in a Local Environment
4 videos
Hadoop Standalone mode Install 09:33

Hadoop Pseudo-Distributed mode Install 14:25
4. The MapReduce "Hello World"
7 videos
The basic philosophy underlying MapReduce 08:49

MapReduce - Visualized And Explained 09:03

MapReduce - Digging a little deeper at every step 10:21

"Hello World" in MapReduce 10:29

The Mapper 09:48

The Reducer 07:46

The Job 12:28
5. Run a MapReduce Job
2 videos
Get comfortable with HDFS 10:59

Run your first MapReduce Job 14:30
6. Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
6 videos
Parallelize the reduce phase - use the Combiner 14:39

Not all Reducers are Combiners 14:31

How many mappers and reducers does your MapReduce have? 08:23

MapReduce is not limited to the Java language - Introducing the Streaming API 05:05

7. HDFS and Yarn
7 videos
HDFS - Name nodes and why they're critical 06:48

HDFS - Checkpointing to backup name node information 11:10

Yarn - Basic components 08:33

Yarn - Submitting a job to Yarn 13:10

Yarn - Plug in scheduling policies 14:21

Yarn - Configure the scheduler 12:26
8. Setting up a Hadoop Cluster
3 videos
Manually configuring a Hadoop cluster (Linux VMs) 13:50

Start a Hadoop Cluster with Cloudera Manager on AWS 13:04
9. MapReduce Customizations For Finer Grained Control
4 videos
Setting up your MapReduce to accept command line arguments 13:47

The Tool, ToolRunner and GenericOptionsParser 12:36

Customizing the Partitioner, Sort Comparator, and Group Comparator 15:16
10. The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!
6 videos
The heart of search engines - The Inverted Index 14:41

Generating the inverted index using MapReduce 10:25

Custom data types for keys - The Writable Interface 10:23

Represent a Bigram using a WritableComparable 13:13

MapReduce to count the Bigrams in input text 08:26

Test your MapReduce job using MRUnit 13:41
11. Input and Output Formats and Customized Partitioning
7 videos
Introducing the File Input Format 12:48

Text And Sequence File Formats 10:21

Data partitioning using a custom partitioner 07:11

Make the custom partitioner real in code 10:25

Total Order Partitioning 10:10

Input Sampling, Distribution, Partitioning and configuring these 09:04

Secondary Sort 14:34
12. Recommendation Systems using Collaborative Filtering
4 videos
Introduction to Collaborative Filtering 07:25

Friend recommendations using chained MR jobs 17:15

Get common friends for every pair of users - the first MapReduce 14:50

Top 10 friend recommendation for every user - the second MapReduce 13:46
13. Hadoop as a Database
7 videos
Running an SQL Select with MapReduce 15:31

Running an SQL Group By with MapReduce 14:02

A MapReduce Join - The Map Side 14:20

A MapReduce Join - The Reduce Side 13:08

14. K-Means Clustering
7 videos
What is K-Means Clustering? 14:04

A MapReduce job for K-Means Clustering 16:33

K-Means Clustering - Measuring the distance between points 13:52

K-Means Clustering - Custom Writables for Input/Output 08:26

K-Means Clustering - Configuring the Job 10:50

K-Means Clustering - The Mapper and Reducer 11:23

K-Means Clustering : The Iterative MapReduce Job 03:39

Meet the Author

4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
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