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Components of Hadoop

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The view of Hadoop architecture gives prominence to Hadoop Common, Hadoop YARN, Hadoop MapReduce of the Hadoop Ecosystem. It provides all Java libraries, utilities, OS level abstraction, and While Hadoop YARN is a framework for job scheduling and cluster resource management. HDFS in Hadoop architecture provides high throughput the access to application data and Hadoop MapReduce provides the YARN based parallel processing of large sets of data.

Hadoop Core Components

There are three Core Hadoop Components. They are:

  1. Hadoop Common

The Apache Foundation has pre-defined the set of utilities and libraries that can be used by the other modules within the Hadoop Ecosystem. For HBase and Hive want to access HDFS they need to make of Java archives which are stored in Hadoop Common.

  1. Hadoop Distributed File System (HDFS)

The big data storage layer for Apache Hadoop is HDFS. HDFS is the “Secret Sauce” of Apache Hadoop components as users can dump large quantities of datasets into HDFS and data will leverage until the user want and analyse. The big data storage layer is used to Apache Hadoop is HDFS. Hadoop Course in Chennai is the best place to learn the Hadoop Course. It creates the several replicas of the data block to be distributed across different clusters for reliable and quick data access. HDFS comprises of three important components. They are: NameNode, DataNode and Secondary NameNode.HDFS operates on the Master-Slave architecture model where the NameNode acts as the master node for keeping track of the storage cluster. The DataNode acts as slave node by summing up to various systems within a Hadoop Cluster.

  1. MapReduce Distributed Data processing Framework

MapReduce is a Java based system created by Google where the actual data from the HDFS store gets processed efficiently. It breaks down a big data processing job into smaller tasks. Big Data Training in Chennai at FITA is the best training institute in Chennai. It is responsible for the analyzing large datasets in parallel before reducing the results. Hadoop MapReduce is a framework based on YARN architecture. It is used for storing the large datasets.

The principle of operation behind MapReduce is that the “Map” job sends a query for processing the various nodes in the Hadoop Cluster. This reduces job collects the results to output into a single value.

I hope you learned few things about the components of Hadoop in my article, if you want to get a quite knowledge in Hadoop, you should learn Hadoop Training in Chennai. our advanced big data course teaches you what the Hadoop is? & How its beneficial for your career growth in IT market.

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