Apache Hadoop & its Components

Apache Hadoop is an open source framework which is used mainly for big data operations, this software framework provides the facility to store and run the data which is large in size. Hadoop consists of different types of modules which makes the task easier.

This article will explain you about the different components of Hadoop, benefits, and features of Hadoop, complete topics of Hadoop is listed down at the end. You can find the complete Hadoop Topics to enhance your big data skills for a successful career.

Hadoop Components

Different modules of Hadoop is listed below,

• Distributed File System
• MapReduce
• YARN

Distributed File System:

Hadoop Distributed File System (HDFS) runs on commodity hardware which is designed by using low-cost hardware. The file system of Hadoop is developed using the distributed file system.

Why Distributed File System?

In Hadoop software, HDFS is used to store a large amount of data. It also provides an easier access to the stored data’s. To avoid the data loss and to improve the easier accessibility of the data, it is stored on multiple machines in a redundant fashion.

Because of using HDFS you can easily access an application in a parallel way.

Map Reduce:

Map Reduce is one of the important components of Hadoop which is used to write applications in order to process a large amount of stored data. In simple term, Map Reduce is a processing technique for distributed computing which basically runs using Java. Hence it is suggested to learn Java programming before planning your career into Hadoop.

Why Map Reduce?

Map Reduce handle two different tasks which are Map & Reduce, the basic functionality of Map is to take a set of data and converts or breaks into another set of data in a pair format. Now reduce task is to take the broken pair as an input and make it a smaller set which makes the job easier and reduces the processing time.

Reduce can only perform secondly once Map completes its task of converting.

YARN:

Yet Another Resource Negotiator (YARN) – It is originally used for redesigning the resource manager, in Hadoop we use it as a part of large-scale distributed operating the system to access big data applications.

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