Section 1 : Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Certification
Lecture 3 Traditional approach VS Hadoop approach 00:02:26 Duration
Lecture 4 Basic Flow of a Mapreduce program 00:04:41 Duration
Lecture 5 Mapreduce Program flow with Example 00:04:48 Duration
Lecture 6 Types of File Input formats in Mapreduce 00:06:57 Duration

Section 2 : Default structure of various classes in Mapreduce
Lecture 1 Mapper Class structure 00:06:52 Duration
Lecture 2 Reducer Class structure 00:03:23 Duration
Lecture 3 Driver Class structure 00:05:55 Duration
Lecture 4 Partitioner Class structure 00:03:48 Duration
Lecture 5 Shuffling, Sorting & Partitioning in Detail 00:03:04 Duration
Lecture 6 Hadoop Installation 00:08:18 Duration

Section 3 : Word Count program in Mapreduce
Lecture 1 What are Writables in Hadoop 00:05:26 Duration
Lecture 2 Word Count program in Mapreduce 00:10:31 Duration
Lecture 3 Word count program Code run 00:09:11 Duration
Lecture 4 What is Combiner in Hadoop Mapreduce 00:07:14 Duration
Lecture 5 Implementing Combiner in WordCount Mapreduce program 00:03:44 Duration

Section 4 : Set of Mapreduce programs
Lecture 1 Calculate Sum of Even Odd numbers 00:08:29 Duration
Lecture 2 Calculate success rate of Facebook ads
Lecture 3 Writables - Create our own datatype in Mapreduce 00:07:25 Duration
Lecture 4 Fraud customers of an Ecommerce website - part 1 00:08:59 Duration
Lecture 5 Fraud customers of an Ecommerce website - part 2 00:07:25 Duration

Section 5 : Distributed Cache Implementation
Lecture 1 What is Distributed Cache and it's uses in Mapreduce framework 00:04:02 Duration
Lecture 2 Using Distributed cache calculate average salary 00:11:54 Duration

Section 6 : Dealing with Input Split Class
Lecture 1 What are Input splits in Hadoop 00:05:55 Duration
Lecture 2 Input split Class in Mapreduce 00:01:45 Duration

Section 7 : Multiple Inputs & Output class
Lecture 1 Multiple Inputs class and its Implementation 00:08:50 Duration
Lecture 2 Multiple Output class and its Implementation 00:08:32 Duration

Section 8 : Joins in Mapreduce
Lecture 1 Pseudo code flow of Joins Mapreduce program 00:05:17 Duration
Lecture 2 Join 2 files in a Mapreduce program
Lecture 3 Performing Outer Join in Mapreduce 00:09:04 Duration
Lecture 4 What is Map Join and Where it is Used 00:05:29 Duration
Lecture 5 Implementing Map Join in a Mapreduce program 00:05:59 Duration

Section 9 : Counters in Mapreduce
Lecture 1 What are Counters in Hadoop 00:06:31 Duration
Lecture 2 Job Counters 00:05:42 Duration
Lecture 3 Create our own Custom Counters in Mapreduce program 00:10:53 Duration

Section 10 : Creating Custom Input Formatter
Lecture 1 File Input format Class's default structure in Mapreduce 00:07:30 Duration
Lecture 2 Custom Input Formatter Need & Problem statement 00:07:35 Duration
Lecture 3 Create custom Input Format class to read XML file Part 1 00:09:30 Duration
Lecture 4 Create custom Input Format class to read XML file Part 2 00:12:18 Duration
Lecture 5 Create custom Input Format class to read XML file Part 3

Section 11 : Different Types of Files in Hadoop
Lecture 1 Text, Sequence, Avro Files 00:05:34 Duration
Lecture 2 RC, ORC, Parquet Files
Lecture 3 Performance Test results of Various Files 00:02:39 Duration
Lecture 4 Which File Format to choose 00:01:56 Duration
Lecture 5 Sequence File Implementation in MapReduce 00:08:11 Duration

Section 12 : Chaining in Mapreduce
Lecture 1 Chain Mapper and its Implementation 00:05:32 Duration
Lecture 2 How to Chain Multiple MR Programs 00:05:22 Duration

Section 13 : Case study 1 - Bank Loyal Customers Identification
Lecture 1 Identifying Bank's Loyal Customers 00:11:02 Duration

Section 14 : Case study 2 - Predicting Churn customers
Lecture 1 Predicting Churn customers Part 1 00:05:02 Duration
Lecture 2 Predicting Churn customers Part 2 00:10:25 Duration

Section 15 : Case study 3 - Flight data Analysis
Lecture 1 Flight data Analysis Part 1 00:08:20 Duration
Lecture 2 Flight data Analysis Part 2