10 Features Of Hadoop That Made It The Most Popular

Have you ever thought why companies adopt Hadoop as a solution to Big Data Problems?

In this article, we are going to study the essential features of Hadoop that make Hadoop so popular. The article enlists various Hadoop features like open source, scalability, fault tolerance, high availability, etc. that make Hadoop the most popular big data tool.

Let us first begin with a short introduction to Hadoop.

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What is Hadoop?

Hadoop is a software framework developed by the Apache Software Foundation for distributed storage and processing of huge amounts of datasets. Hadoop consists of 3 core components :

1. HDFS (High Distributed File System)

It is the storage layer of Hadoop. Files in HDFS are broken into block-sized chunks. HDFS consists of two types of nodes that is, NameNode and DataNodes.

  • NameNode stores metadata about blocks location.
  • DataNodes stores the block and sends block reports to NameNode in a definite time interval.

2. MapReduce

MapReduce is the processing layer in Hadoop. It is a software framework for writing an application that performs distributed processing.

3. YARN

It is the resource management layer. YARN is responsible for resource allocation and job scheduling.

To study in detail Hadoop and its component, go through the Hadoop architecture article.

Let us now begin with the Features of Hadoop.

Features of Hadoop

Apache Hadoop is the most popular and powerful big data tool, Hadoop provides the world’s most reliable storage layer. In this section of the features of Hadoop, let us discuss various key features of Hadoop.

features of hadoop

1. Hadoop is Open Source

Hadoop is an open-source project, which means its source code is available free of cost for inspection, modification, and analyses that allows enterprises to modify the code as per their requirements.

2. Hadoop cluster is Highly Scalable

Hadoop cluster is scalable means we can add any number of nodes (horizontal scalable) or increase the hardware capacity of nodes (vertical scalable) to achieve high computation power. This provides horizontal as well as vertical scalability to the Hadoop framework.

3. Hadoop provides Fault Tolerance

Fault tolerance is the most important feature of Hadoop. HDFS in Hadoop 2 uses a replication mechanism to provide fault tolerance.

It creates a replica of each block on the different machines depending on the replication factor (by default, it is 3). So if any machine in a cluster goes down, data can be accessed from the other machines containing a replica of the same data.

Hadoop 3 has replaced this replication mechanism by erasure coding. Erasure coding provides the same level of fault tolerance with less space. With Erasure coding, the storage overhead is not more than 50%.

Read Erasure coding article to learn the erasure coding algorithm.

4. Hadoop provides High Availability

This feature of Hadoop ensures the high availability of the data, even in unfavorable conditions.

Due to the fault tolerance feature of Hadoop, if any of the DataNodes goes down, the data is available to the user from different DataNodes containing a copy of the same data.

Also, the high availability Hadoop cluster consists of 2 or more running NameNodes (active and passive) in a hot standby configuration. The active node is the NameNode, which is active. Passive node is the standby node that reads edit logs modification of active NameNode and applies them to its own namespace.

If an active node fails, the passive node takes over the responsibility of the active node. Thus even if the NameNode goes down, files are available and accessible to users.

5. Hadoop is very Cost-Effective

Since the Hadoop cluster consists of nodes of commodity hardware that are inexpensive, thus provides a cost-effective solution for storing and processing big data. Being an open-source product, Hadoop doesn’t need any license.

6. Hadoop is Faster in Data Processing

Hadoop stores data in a distributed fashion, which allows data to be processed distributedly on a cluster of nodes. Thus it provides lightning-fast processing capability to the Hadoop framework.

7. Hadoop is based on Data Locality concept

Hadoop is popularly known for its data locality feature means moving computation logic to the data, rather than moving data to the computation logic. This features of Hadoop reduces the bandwidth utilization in a system.

To install and configure Hadoop follow this installation guide.

8. Hadoop provides Feasibility

Unlike the traditional system, Hadoop can process unstructured data. Thus provide feasibility to the users to analyze data of any formats and size.

9. Hadoop is Easy to use

Hadoop is easy to use as the clients don’t have to worry about distributing computing. The processing is handled by the framework itself.

10. Hadoop ensures Data Reliability

In Hadoop due to the replication of data in the cluster, data is stored reliably on the cluster machines despite machine failures.

The framework itself provides a mechanism to ensure data reliability by Block Scanner, Volume Scanner, Disk Checker, and Directory Scanner. If your machine goes down or data gets corrupted, then also your data is stored reliably in the cluster and is accessible from the other machine containing a copy of data.

Also, explore 10 changes in Hadoop 3 that makes it unique and fast.

Summary

In short, we can say that Hadoop is an open-source framework. Hadoop is best known for its fault tolerance and high availability feature. Hadoop clusters are scalable. The Hadoop framework is easy to use.

It ensures fast data processing due to distributed processing. Hadoop is cost-effective. Hadoop data locality feature reduces the bandwidth utilization of the system.

See Also-

Got any queries or feedback about these features of Hadoop and design principle tutorial?

Just leave your message in the comment section and we will get back to you.

Keep Learning!!

12 Responses

  1. Ellie says:

    I quite like looking through a post that will make people think.
    Thanks for writing this article on features of Hadoop that make it so unique.

    • DataFlair Team says:

      Hello Ellie,
      We are glad, our reader like our efforts. Our team is continuously working for a reader to make their best experience of learning.
      We recommend you to read more Hadoop tutorial. Surely, it will help you more!
      Regards
      DataFlair

  2. Flora says:

    Nice writeup on design principles of Big Data Hadoop.

    • DataFlair Team says:

      Hi Flora,
      Thanks for the nice words on Hadoop Features. We are trying to collect all the important and latest information to the reader.
      Keep visiting and keep appreciating DataFlair

  3. Rambabu says:

    Awesome write on design principle and assumptions on which hadoop works

  4. Ravi Kumar says:

    Thanks for sharing a blog on big data hadoop. These features will help you to make your working with software better

  5. Shubham says:

    Fantastic job guys! Keep up the good work in providing simple yet informative tutorials to complex topics

    • DataFlair Team says:

      Hey Shubham,

      Glad that you liked our article. It would be great if you give your feedback on Google by rating us.

  6. rainbowr says:

    thankls sir good blogg

  7. casota272 says:

    3h courses at school can be replaced by 1h of reading your articles. Very concise but still complete and easy to understand. Thanks a lot.

  8. Sandeep Kataria says:

    Hello,

    I am looking for help to spec out a Hadoop cluster so these articles have greatly helped. But i still need the people! Anyone interested can write to me amukataria at gmail

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