Comprehensive MapReduce Certification Training

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Enroll now to become a MapReduce expert with CertZip Comprehensive MapReduce Certification Training, upgrade your skills, and lead your professional life.

Course Description

Hadoop Experts designed this course to provide the knowledge and skills in MapReduce Framework and help you solve the use cases using MapReduce concepts.

The comprehensive MapReduce course is designed for the learners to understand and implement various frameworks of MapReduce.

MapReduce is a programming paradigm that helps tremendous scalability across hundreds or thousands of servers in a Hadoop cluster. As the processing element, MapReduce is the heart of Apache Hadoop. "MapReduce" refers to two distinct and unlike tasks that Hadoop programs perform.

Mapping is the core technique of processing a list of data elements that come in pairs of keys and values. The map function links individual elements represented as key-value pairs of a list and has a new list.

MapReduce is a software framework and programming model operated for processing vast amounts of data. MapReduce program operates in two phases, Map and Reduce. Map tasks deal with splitting and mapping data while Reducing tasks shuffling and reducing the data.

The MapReduce algorithm includes two essential tasks, i.e., Map and Reduce. The map takes a data set and converts it into another collection of data, where individual elements are broken down into tuples (key/value pairs).

MapReduce serves two essential functions: It filters and parcels work to various nodes within the cluster or map. A process sometimes referred to as the mapper. It manages and lessens the effects from each node into a cohesive answer to a question, directed to as the reducer.

MapReduce is a programming model or pattern within the Hadoop framework used to access big data stored in the Hadoop File System (HDFS). It is the main component integral to the functioning of the Hadoop framework.

What you'll learn

  • In this course, you will learn: MapReduce Framework, Hadoop MapReduce Framework, Advance MapReduce

Requirements

  • No, requirement to pursue this course.

Curriculam

In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will learn about YARN concepts in MapReduce.

MapReduce Use Cases,
Traditional way Vs MapReduce way,
Why MapReduce,
Hadoop 2.x MapReduce Architecture,
Hadoop 2.x MapReduce Components,
YARN MR Application Execution Flow,
YARN Workflow,
Anatomy of MapReduce Program,
Demo on MapReduce.

In this module, you will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.

Input Splits in MapReduce,
Combiner,
Partitioner,
Demos on MapReduce.

In this module, you will learn Advance MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and how to deal with complex MapReduce programs.

Counters,
Distributed Cache,
MRunit,
Reduce Join,
Custom Input Format,
Sequence Input Format

FAQ

The biggest power of the MapReduce framework is scalability. Once a MapReduce program is noted, it can effortlessly be extrapolated to operate over a cluster with hundreds or even thousands of nodes. In this framework, analysis is sent to where the data is stored.

To better understand the Comprehensive MapReduce Certification Training, one must learn as per the curriculum.

MapReduce is suitable for iterative computation involving large quantities of data requiring parallel processing, and it represents a data flow rather than a procedure. It's also ideal for large-scale graph analysis; MapReduce was initially developed to determine the PageRank of web documents.

MapReduce Architecture is a programming model and a software framework utilized to prepare enormous data measures. MapReduce program works in two phases, Map and Reduce. Map requests arrange with mapping and splitting data while Reduce tasks reduce and shuffle the data.

MapReduce is a processing method and a program model for dispersed computing established on java. The map takes a data set and converts it into another collection of data, where individual elements are broken down into tuples (key/value pairs).

CertZip Support Team is for a lifetime and will be available 24/7 to help with your questions during and after completing the Comprehensive MapReduce Certification Training.

By enrolling in Comprehensive MapReduce and completing the module, you can get the CertZip Analytics for Retail Banks Training Certification.

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Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and CertZip will provide you with a completion certification.

Comprehensive MapReduce Certification Training

Comprehensive MapReduce Training demonstrates that the holder has the proficiency and aptitudes to work with Comprehensive MapReduce.

MapReduce assigns fragments of data across the nodes in a Hadoop cluster. The goal is to split a dataset into chunks and use an algorithm to process those chunks simultaneously. The parallel processing on multiple machines dramatically increases the speed of handling even petabytes of data.

If you're ready for a career in a stable and high-paying field, the Comprehensive MapReduce might be proper for you, and this Certification is the place to start.

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