What is Hadoop? And it?s Features

Posted by hussain on March 28th, 2019

The first is HDFS for capacity (Hadoop Distributed File System), that enables you to store information on different arrangements over a bunch. The second one is YARN, for an asset to the board in Hadoop. It permits parallel handling over the information, for example, put away crosswise over HDFS.


HDFS makes a deliberation, let me streamline it for you. Comparative as virtualization, you can see HDFS coherently as a solitary unit for putting away Big Data, however, you are putting away your information over numerous hubs in a dispersed manner.

In HDFS, Namenode is the ace hub and Data nodes are the slaves. Name node contains the metadata about the information put away in Data hubs, for example, which information square is put away in which information hub, where are the replications of the information square kept and so forth. The real information is put away in Data Nodes.


YARN plays out the entirety of your handling exercises by distributing assets and planning errands.

It has two noteworthy parts, for example, Resource Manager and Node Manager.

Resource Manager is again an ace hub. It gets the handling solicitations and after that passes the pieces of solicitations to comparing Node Managers likewise, where the genuine preparing happens. Node Managers are introduced on each Data Node. It is in charge of the execution of the errand on each and every Hadoop Training in Bangalore Data Node.

Features of Hadoop that Made It the Most Popular:


At the point when machines are working pair, on the off chance that one of the machines comes up short, another machine will assume control over the obligation and work in a solid and blame tolerant style. Hadoop framework has inbuilt adaptation to non-critical failure highlights and thus, Hadoop is exceedingly dependable.


Hadoop utilizes item equipment (like your PC). For instance, in a little Hadoop bunch, all your DataNodes can have ordinary arrangements like 8-16 GB RAM with 5-10 TB hard circle and Xeon processors, however in the event that I would have utilized equipment based RAID with Oracle for a similar reason, I would finish up burning through 5x times more at any rate. In this way, the expense of responsibility for Hadoop-based venture is entirely limited. It is simpler to keep up the Hadoop condition and is prudent also. Likewise, Hadoop is an open source programming and subsequently, there is no authorizing expense.

Open Source

Apache Hadoop is an open source venture. It implies its code can be changed by business prerequisites.


Hadoop has the inbuilt capacity of incorporating flawlessly with cloud-based administrations. In this way, in the event that you are introducing Hadoop on a cloud, you don't have to stress over the versatility factor since you can feel free to obtain more equipment and grow your setup inside minutes at whatever point required.

Fault Tolerance:

This is one of the essential highlights of Hadoop. As a matter of course, 3 reproductions of each square is put away over the group in Hadoop and it tends to be changed additionally according to the necessity. So if any hub goes down, information on that hub can be recuperated from different hubs effectively with the assistance of this trademark. Disappointments of hubs or errands are recuperated consequently by the system. This is the manner by which Hadoop is fault tolerant.


Hadoop is entirely adaptable as far as capacity to manage a wide range of information. We examined "Assortment" in our past blog on Big Data Tutorial, where information can be of any sort and Hadoop can store and process them all, regardless of whether it is organized, semi-organized or unstructured information.

High accessibility:

Information is very accessible and available in spite of equipment disappointment because of various duplicates of information. On the off chance that a machine or couple of equipment crashes, at that point information, will be gotten to from another way.

Simple to utilize:

No need of customer to manage dispersed processing, the system deals with every one of the things. So this component of Hadoop is anything but difficult to utilize.

These were the Characteristics of Hadoop that separates it from different Data Management frameworks.


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