Section 1 : Introduction

Lecture 1 You, this course and Us copy 1:52

Section 2 : Why is Big Data a Big Deal

Lecture 2 The Big Data Paradigm 14:18
Lecture 3 Serial vs Distributed Computing 8:32
Lecture 4 What is Hadoop
Lecture 5 HDFS or the Hadoop Distributed File System 10:51
Lecture 6 MapReduce Introduced 11:33
Lecture 7 YARN or Yet Another Resource Negotiator 3:58

Section 3 : Installing Hadoop in a Local Environment

Lecture 8 Hadoop Install Modes 8:22
Lecture 9 Hadoop Standalone mode Install 15:39
Lecture 10 Hadoop Pseudo-Distributed mode Install

Section 4 : The MapReduce Hello World

Lecture 11 The basic philosophy underlying MapReduce 8:45
Lecture 12 MapReduce - Visualized And Explained 9:0
Lecture 13 MapReduce - Digging a little deeper at every step 10:17
Lecture 14 Hello World in MapReduce 10:23
Lecture 15 The Mapper 9:46
Lecture 16 The Reducer 7:44
Lecture 17 The Job 12:21

Section 5 : Run a MapReduce Job

Lecture 18 Get comfortable with HDFS 10:45
Lecture 19 Run your first MapReduce Job 14:22

Section 6 : Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API

Lecture 20 Parallelize the reduce phase - use the Combiner 14:30
Lecture 21 Not all Reducers are Combiners 13:30
Lecture 22 How many mappers and reducers does your MapReduce have
Lecture 23 Parallelizing reduce using Shuffle And Sort 14:32
Lecture 24 MapReduce is not limited to the Java language - Introducing the Streaming API 5:2
Lecture 25 Python for MapReduce 12:13

Section 7 : HDFS and Yarn

Lecture 26 HDFS - Protecting against data loss using replication 15:30
Lecture 27 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 28 HDFS - Checkpointing to backup name node information 11:10
Lecture 29 Yarn - Basic components 8:32
Lecture 30 Yarn - Submitting a job to Yarn
Lecture 31 Yarn - Plug in scheduling policies 14:11
Lecture 32 Yarn - Configure the scheduler 12:29

Section 8 : MapReduce Customizations For Finer Grained Control

Lecture 33 Setting up your MapReduce to accept command line arguments 13:43
Lecture 34 The Tool, ToolRunner and GenericOptionsParser 12:30
Lecture 35 Configuring properties of the Job object 10:39
Lecture 36 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 9 : The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!

Lecture 37 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 38 Generating the inverted index using MapReduce 10:29
Lecture 39 Custom data types for keys - The Writable Interface
Lecture 40 Represent a Bigram using a WritableComparable 13:15
Lecture 41 MapReduce to count the Bigrams in input text 8:27
Lecture 42 Setting up your Hadoop project Text
Lecture 43 Test your MapReduce job using MRUnit 13:42

Section 10 : Input and Output Formats and Customized Partitioning

Lecture 44 Introducing the File Input Format 12:19
Lecture 45 Text And Sequence File Formats 10:19
Lecture 46 Data partitioning using a custom partitioner 6:56
Lecture 47 Make the custom partitioner real in code 10:22
Lecture 48 Total Order Partitioning 10:7
Lecture 49 Input Sampling, Distribution, Partitioning and configuring these 9:2
Lecture 50 Secondary Sort 14:24

Section 11 : Recommendation Systems using Collaborative Filtering

Lecture 51 Introduction to Collaborative Filtering 7:20
Lecture 52 Friend recommendations using chained MR jobs 17:12
Lecture 53 Get common friends for every pair of users - the first MapReduce 14:43
Lecture 54 Top 10 friend recommendation for every user - the second MapReduce 13:39

Section 12 : Hadoop as a Database

Lecture 55 Structured data in Hadoop 14:0
Lecture 56 Running an SQL Select with MapReduce 15:27
Lecture 57 Running an SQL Group By with MapReduce 13:55
Lecture 58 A MapReduce Join - The Map Side 14:13
Lecture 59 A MapReduce Join - The Reduce Side 13:1
Lecture 60 A MapReduce Join - Sorting and Partitioning 8:44
Lecture 61 A MapReduce Join - Putting it all together 13:41

Section 13 : K-Means Clustering

Lecture 62 What is K-Means Clustering 13:59
Lecture 63 A MapReduce job for K-Means Clustering 16:28
Lecture 64 K-Means Clustering - Measuring the distance between points 13:22
Lecture 65 K-Means Clustering - Custom Writables for InputOutput 8:13
Lecture 66 K-Means Clustering - Configuring the Job 10:45
Lecture 67 K-Means Clustering - The Mapper and Reducer 11:15
Lecture 68 K-Means Clustering The Iterative MapReduce Job 3:35

Section 14 : Setting up a Hadoop Cluster

Lecture 69 Manually configuring a Hadoop cluster (Linux VMs) 12:52
Lecture 70 Getting started with Amazon Web Servicies 6:20
Lecture 71 Start a Hadoop Cluster with Cloudera Manager on AWS 12:59

Section 15 : Appendix

Lecture 72 Setup a Virtual Linux Instance (For Windows users) 15:50
Lecture 73 [For LinuxMac OS Shell Newbies] Path and other Environment Variables 8:21