Hadoop Online Training in Hyderabad

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

This brief tutorial provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.

This tutorial has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

Prerequisites :

Before you start proceeding with this tutorial, we assume that you have prior exposure to Core Java, database concepts, and any of the Linux operating system flavors.

 

Hadoop Online Training & Corporate Training

Hadoop Development course teaches the skill set required for the learners how to setup Hadoop Cluster, how to store Big Data using Hadoop (HDFS) and how to process/analyze the Big Data using Map-Reduce Programming or by using other Hadoop ecosystems. Attend Hadoop Training demo by Real-Time Expert.

Hadoop Training Course Prerequisites :

Basic Unix Commands

Core Java (OOPS Concepts, Collections , Exceptions ) for Map Reduce Programming

SQL Query knowledge for Hive Queries

Hadoop Course System Requirements :

Any Linux flavor OS (Ex: Ubuntu/Cent OS/Fedora/RedHat Linux) with 4 GB RAM (minimum), 100 GB HDD

Java 1.6+

Open-SSH server & client

MYSQL Database

Eclipse IDE

VMWare (To use Linux OS along with Windows OS)

Best Hadoop Online Training

The base Apache Hadoop framework is composed of the following modules:

Hadoop Common – contains libraries and utilities needed by other Hadoop modules;

Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;

Hadoop YARN – a platform responsible for managing computing resources in clusters and using them for scheduling users’ applications; and

Hadoop MapReduce – an implementation of the MapReduce programming model for large-scale data processing.