Introduction to Apache Hadoop

Introduction to Apache Hadoop

Hadoop is a tool to handle the big data. Apache Hadoop is software, actually with open source software framework. These frameworks are trained by Hadoop Training Chennai institute. It’s modules designed to handle the hardware failure automatically by the framework. It is used for distributed processing and very large set of data storage. Here the main phenomenon is computer clusters, which is tightly or loosely connected computers and it works together, said as a single system. Hadoop has two parts of processing one is core processing and another one is Map Reduce. This core part processing denotes storage and it consists of the Hadoop Distributed File System (HDFS) and the Map Reduce use to locate large dataset and search for specific results.

Overview of Hadoop

Apache Hadoop contains framework and it is composed of some four categories,

  1. Hadoop Common
  2. Hadoop Distributed File System
  3. Hadoop YARN
  4. Hadoop Map Reduce
  5. Hadoop Common

Hadoop Common is a generic term which contains common libraries and utilities. These common packages are written in any programming language like Java, c, and shell script. The Java programming language has some extensions like JAR (Java Archive) files. This JAR file and shell script use to start up Hadoop.

  1. Hadoop Distributed File System

Hadoop Distributed File System (HDFS) Hadoop framework is written in the Java programming language which is scalable, portable, and distributed file system. This Hadoop doesn’t use another file system for storage purpose and also uses TCP/IP protocol and RPC (Remote procedure Call) to communicate with each other. This file system is also used as the DATA store. Our Training teaches you to learn HDFS in an efficient manner.

  1. Hadoop YARN

Hadoop YARN has abbreviated as Hadoop yet another Resource Negotiator. This framework undergoes control of resource management for computing clusters. This resource can handle petabytes of data storage.

  1. Hadoop MapReduce

Hadoop Map Reduce is another core framework which first locates the large data set and search for particular results. This framework is called a processing framework which is the second most important thing in Hadoop tool. By this, the search results are reduced and give the exact data.

Benefits of Apache Hadoop

There are five benefits of using Hadoop they are as follows,

  1. Flexible

This flexible in the sense, it can store a variety of data and can produce value from that. The data which are stored in a distributed file system are social media conversation, fraud detection, log processing, etc..,. It can able to maintain both structured and unstructured data in its large storage.

  1. Elastic to failure

It is similar to copy by value scheme of data storage. This means its use take copies of all data items and from this, we won’t lose data.

  1. Swift

It is the fastest file system while comparing to other file systems.

  1. Scalable

Scalability refers to the enormous structure of data storage. It can able to store some petabytes of data in parallel.

  1. Cost effective

Alike scalable we can store some thousands of gigabytes so; we store the inexpensive values of data and also offers to store more data. All this has done by single computing clusters only.

Advantages of learning Hadoop

There are some major reasons to learn Big Data Online Course

  1. Better career:
  2. Job opportunities:
  3. Better salary
  4. Big company hiring

It is a very rare career nowadays and over 90% of companies and organization is expecting the peoples with Hadoop knowledge. There are a number of institutes for Hadoop Training in Bangalore. Job opportunities from various organizations like social media, log maintenance, fraud detection, etc..,. All these organizations are considered as the application and recruitments are there all over the world. It is considered as the top technology and minimum salary will be given 0.1 million per month.

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