Section 1 : Introduction

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

Section 2 : Default structure of various classes in Mapreduce

Lecture 7 Mapper Class structure 6:52
Lecture 8 Reducer Class structure 3:23
Lecture 9 Driver Class structure 5:55
Lecture 10 Partitioner Class structure 3:48
Lecture 11 Shuffling, Sorting & Partitioning in Detail 3:4
Lecture 12 Hadoop Installation 8:18

Section 3 : Word Count program in Mapreduce

Lecture 13 What are Writables in Hadoop 5:26
Lecture 14 Word Count program in Mapreduce 10:31
Lecture 15 Word count program Code run 9:11
Lecture 16 What is Combiner in Hadoop Mapreduce 7:14
Lecture 17 Implementing Combiner in WordCount Mapreduce program 3:44

Section 4 : Set of Mapreduce programs

Lecture 18 Calculate Sum of Even Odd numbers 8:29
Lecture 19 Calculate success rate of Facebook ads
Lecture 20 Writables - Create our own datatype in Mapreduce 7:25
Lecture 21 Fraud customers of an Ecommerce website - part 1 8:59
Lecture 22 Fraud customers of an Ecommerce website - part 2 7:25

Section 5 : Distributed Cache Implementation

Lecture 23 What is Distributed Cache and it's uses in Mapreduce framework 4:2
Lecture 24 Using Distributed cache calculate average salary 11:54

Section 6 : Dealing with Input Split Class

Lecture 25 What are Input splits in Hadoop 5:55
Lecture 26 Input split Class in Mapreduce 1:45

Section 7 : Multiple Inputs & Output class

Lecture 27 Multiple Inputs class and its Implementation 8:50
Lecture 28 Multiple Output class and its Implementation 8:32

Section 8 : Joins in Mapreduce

Lecture 29 Pseudo code flow of Joins Mapreduce program 5:17
Lecture 30 Join 2 files in a Mapreduce program
Lecture 31 Performing Outer Join in Mapreduce 9:4
Lecture 32 What is Map Join and Where it is Used 5:29
Lecture 33 Implementing Map Join in a Mapreduce program 5:59

Section 9 : Counters in Mapreduce

Lecture 34 What are Counters in Hadoop 6:31
Lecture 35 Job Counters 5:42
Lecture 36 Create our own Custom Counters in Mapreduce program 10:53

Section 10 : Creating Custom Input Formatter

Lecture 37 File Input format Class's default structure in Mapreduce 7:30
Lecture 38 Custom Input Formatter Need & Problem statement 7:35
Lecture 39 Create custom Input Format class to read XML file Part 1 9:30
Lecture 40 Create custom Input Format class to read XML file Part 2 12:18
Lecture 41 Create custom Input Format class to read XML file Part 3

Section 11 : Different Types of Files in Hadoop

Lecture 42 Text, Sequence, Avro Files 5:34
Lecture 43 RC, ORC, Parquet Files
Lecture 44 Performance Test results of Various Files 2:39
Lecture 45 Which File Format to choose 1:56
Lecture 46 Sequence File Implementation in MapReduce 8:11

Section 12 : Chaining in Mapreduce

Lecture 47 Chain Mapper and its Implementation 5:32
Lecture 48 How to Chain Multiple MR Programs 5:22

Section 13 : Case study 1 - Bank Loyal Customers Identification

Lecture 49 Identifying Bank's Loyal Customers 11:2

Section 14 : Case study 2 - Predicting Churn customers

Lecture 50 Predicting Churn customers Part 1 5:2
Lecture 51 Predicting Churn customers Part 2 10:25

Section 15 : Case study 3 - Flight data Analysis

Lecture 52 Flight data Analysis Part 1 8:20
Lecture 53 Flight data Analysis Part 2