Implementing Combiner in WordCount Mapreduce program
Calculate Sum of Even Odd numbers
Calculate success rate of Facebook ads
Fraud customers of an Ecommerce website - part 1
Fraud customers of an Ecommerce website - part 2
What is Distributed Cache and it's uses in Mapreduce framework
Using Distributed cache calculate average salary
What are Input splits in Hadoop
Input split Class in Mapreduce
Multiple Inputs class and its Implementation
Multiple Output class and its Implementation
Pseudo code flow of Joins Mapreduce program
Join 2 files in a Mapreduce program
Performing Outer Join in Mapreduce
What is Map Join and Where it is Used
Implementing Map Join in a Mapreduce program
What are Counters in Hadoop
File Input format Class's default structure in Mapreduce
Custom Input Formatter Need & Problem statement
Create custom Input Format class to read XML file | Part 1
Create custom Input Format class to read XML file | Part 2
Create custom Input Format class to read XML file | Part 3
Basic knowledge of HDFS.
Basic knowledge of Core Java.
Rest everything about Hadoop Mapreduce is covered in this course with Practicals.
Mapreduce framework is closest to Hadoop in terms of processing Big data. It is considered as atomic processing unit in Hadoop and that is why it is never going to be obsolete.
Knowing only basics of MapReduce (Mapper, Reducer etc) is not at all sufficient to work in any Real-time Hadoop Mapreduce project of companies. These basics are just tip of the iceberg in Mapreduce programming. Real-time Mapreduce is way more than that. In Live Big data projects we have to override lot many default implementations of Mapreduce framework to make them work according to our requirements.
This course is an answer to the question "What concepts of Hadoop Mapreduce are used in Live Big data projects and How to implement them in a program ?" To answer this, every Mapreduce concept in the course is explained practically via a Mapreduce program.
Every lecture in this course is explained in 2 Steps.
Step 1 : Explanation of a Hadoop component | Step 2 : Practicals - How to implement that component in a MapReduce program.
The overall inclusions and benefits of this course:
Complete Hadoop Mapreduce explained from scratch to Real-Time implementation.
Each and Every Hadoop concept is backed by a HANDS-ON Mapreduce code.
Advance level Mapreduce concepts which are even not available on Internet.
For non Java backgrounder's help, All Mapreduce Java codes are explained line by line in such a way that even a non technical person can understand.
Mapreduce codes and Datasets used in lectures are attached for your convenience.
Includes a section 'Case Studies' that are asked generally in Hadoop Interviews.
Who this course is for:
Students who want to learn Hadoop Mapreduce from SCRATCH to its Live Project Implementation.
Techies who have only basic theoretical knowledge of Mapreduce and need In-depth knowledge of it to work in Real-time projects.
Techies who have fear of Java should take this course as the Java Mapreduce codes are explained in very simple and easy manner.
Engineers who want to switch their career to Hadoop.
At Hadoop Real time Learning, the courses are made keeping in mind the Real-time implementation of Big data technologies in Live Projects. We make courses which majorly consist of Hands-On & Practicals. All our courses contain a detailed knowledge of a technology from Scratch to Advance level. Course's lectures explain the codes in such a way that even a Non-technical person can understand.