MapReduce Architecture for Big Data
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 180 MB
Duration: 38 mins | Genre: eLearning Video | Language: English

Importance of Combiner in Maprduce

What you'll learn

In this module, we will understand what is Map Reduce and what is the necessity of Map reduce in big data world

Requirements

Basic knowledge of Java
Knowledge of programming would be beneficial
Experience of coding

Description

MapReduce Architecture for Big Data:

In this module, we will understand what is Map Reduce and what is the necessity of Map reduce in big data world, We will learn how map reduce is different from traditional programming and map reduce framework as a whole. MapReduce is a programming model suitable for processing of huge data thus are very useful for performing large-scale data analysis using multiple machines in the cluster.

The tutorials will include the following :

1.Introduction to Mapreduce
2.Data Flow Architecture of Mapreduce
3.Concept of Mapper
4.How Reducer works
5.Importance of Combiner in Maprduce
6.Understanding Partitioner
7.Input Format
8.Speculative Execution
9.Input split VS Block
10.Counters
11.Job Optimization and Performance Tuning
12.Example on how Map and Reduce work
13.Implementing example of Map and Reduce programmatically.

Who this course is for:

Mapreduce Beginners
Professionals who want to learn Mapreduce
Graduates looking to build a career in Big Data Analytics
Anyone who wants to learn a Big Data



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