Top Advantages and Disadvantages of Hadoop

 The objective of this tutorial is to discuss the advantages and disadvantages of Hadoop 3.0

Hadoop is designed to store and manage a large amount of data. There are many advantages of Hadoop like it is free and open source, easy to use, its performance etc. but on the other hand, it has some weaknesses which we called as disadvantages. know more hadoop training

So, let’s start exploring the top advantages and disadvantages of Hadoop.

Advantages of Hadoop

Hadoop is easy to use, scalable and cost-effective. Along with this, Hadoop has many advantages. Here we are discussing the top 12 advantages of Hadoop. So, following are the pros of Hadoop that makes it so popular –

6. Low Network Traffic

In Hadoop, each job submitted by the user is split into a number of independent sub-tasks and these sub-tasks are assigned to the data nodes thereby moving a small amount of code to data rather than moving huge data to code which leads to low network traffic.

7. High Throughput

Throughput means job done per unit time. Hadoop stores data in a distributed fashion which allows using distributed processing with ease. A given job gets divided into small jobs which work on chunks of data in parallel thereby giving high throughput.know more hadoop training

8. Open Source

Hadoop is an open source technology i.e. its source code is freely available. We can modify the source code to suit a specific requirement.

10. Ease of use

The Hadoop framework takes care of parallel processing, MapReduce programmers does not need to care for achieving distributed processing, it is done at the backend automatically.

11. Compatibility

Most of the emerging technology of Big Data is compatible with Hadoop like Spark, Flink etc. They have got processing engines which work over Hadoop as a backend i.e. We use Hadoop as data storage platforms for them.know more hadoop training

1. Issue With Small Files

Hadoop is suitable for a small number of large files but when it comes to the application which deals with a large number of small files, Hadoop fails here. A small file is nothing but a file which is significantly smaller than Hadoop’s block size which can be either 128MB or 256MB by default. These large number of small files overload the Namenode as it stores namespace for the system and makes it difficult for Hadoop to function.

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