Map Reduce Training
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About Map Reduce Training
MapReduce is a programming model used in Hadoop to allow massive scalability across thousands of servers in a Hadoop cluster. MapReduce generates and processes Big data on a cluster of computers using a parallel, distributed algorithm, providing for fault tolerance. It is one of the Core components of Apache Hadoop framework. It works with two basic procedures: mapping and reducing. First it maps the data, filtering and sorting it, splitting into key/value pairs. Then, the Reduce operation takes place and combining the pairs into smaller ones to provide meaningful output. It helps how to manage jobs with the Hadoop CLI and monitor them with MCS.
Map Reduce Training Course Objectives
MapReduce Course Objectives
After completion of the MapReduce Course, you will be expertise and eligible for:
- Complete knowledge of MapReduce component in Hadoop
- Able to work on MapReduce related Project independently
- Setup/Install/Configure MapReduce in Dev/Support/Test environment
- Understand architecture of MapReduce and various operations performed on it
- Familiar with standard solutions
Who should go for this course
- Any IT experienced Professional who is interested to work on IT job related to Data Science, Bigdata, Hadoop, etc.
- Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
- Fresh Graduates.
Pre-requisites
The course can learn by any IT professional having basic knowledge of:
- Unix/Linux Operating System
- Core Java
- Database concepts of SQL
COURSE SYLLABUS
Revisiting Hadoop
- Hadoop vs RDBMS - Core components of Hadoop - Hadoop Distributed File System - HDFS Architecture and MapReduce
Introduction to MapReduce
- MapReduce in Hadoop - History of MapReduce - MapReduce applications - Data Flow in MapReduce - Map and Reduce operations - Job submission flow of MapReduce
Understanding MapReduce
- Data Flow in MapReduce - MapReduce example - MapReduce Daemons - Job tracker - Task Tracker - Other phases in Map Reduce - Data Flow in single, multiple and no reduce task
MapReduce with YARN
- Hadoop 1.x architecture - Problem with Hadoop 1.x, Hadoop 2.x features, - YARN MR Application Execution Flow - YARN Workflow - Anatomy of MapReduce Program
Advanced MapReduce
- Input Splits in MapReduce - Combiner - Partitioner - Demos on MapReduce - Counters - Distributed Cache - MRunit - Reduce Join - Custom Input Format - Sequence Input Format
Training Features
Instructor-Led Training Sessions
We believe to provide our students the Best interactive experience as part of their learning
Flexible Schedule
Do not hesitate to ask… because we will work according to your calendar
Industry Specific Scenarios
Students are provided with all the Real-Time and Relevant Scenarios
Expert Trainers
We Constantly evaluate our trainers and only the “Best” Provides the Training
e-Learning Sessions
Online training sessions are held Live and we provide students with the Training Videos
Support
No Problem@ all…!!! Your Question will be answered by Us as soon as possible