Hadoop is the in-demand Big Data platform. It is essential to know Big Data first to understand Hadoop better. Big Data is an enormous collection of data that is exponentially growing over time. Usually, we work on the MB (MegaByte) or GB (GigaByte) size of data, but in Big Data, you can reach upto PetaBytes which is 10^15 Byte size.

Big Data contains data produced by various applications and devices. It is said that “90% of the world’s data was generated in the last few years.” Big Data can’t be computed using traditional methods. It requires various tools, frameworks, and techniques. Hadoop is one such tool that is leading in Big Data platforms.  

Big Data includes:

• Search Engine Data

Search Engine retrieves data from a vast range of sources and gets data from different databases.

• Social Media Data

Through social media, you can get a large amount of data from Twitter, Facebook, and more.

• Black Box Data

Black Box can be found in helicopters, airplanes, jets, etc. Through these Black Boxes, you can retrieve data regarding the voices of the flight crew, recordings of the progressions in the flight, and get an idea of the performance status. 

• Stock Exchange Data

Stock exchange data usually holds information about the bought and sold shares of different companies.

• Transport Data

Transport data can provide you data regarding the distance covered by the vehicles and vehicles’ availability, model, and capacity.

Hence, you can expect a variety of data from Big Data. They are of three types:

• Structured Data - like Relational Data
• Semi-Structured Data - like XML Data
• Unstructured Data - like Text, PDF, etc. 

To process all these kinds of data, you can make use of Hadoop. Hadoop is an open-source tool that allows you to store and process data in a distributed environment across a group of computers that uses simple programming models. Hadoop is very efficient in helping you to scale up your server from single to many, each of them fulfilling local storage and computation requirements.

The traditional approach is suitable for applications with less data than extensive data in Big Data. But suppose you are dealing with a large amount of scalable data. In that case, the traditional method is not a suitable solution because processing massive data through a single database is a hectic task.

Google solved the above problem with the help of an algorithm called MapReduce. It divides the more significant tasks into smaller ones and assigns them to the computers. The result is collected from them, and then these results are integrated to form the final result dataset.

Inspired by Google’s method, Hadoop, an open-source project was created. Hadoop uses the MapReduce algorithm for its better performance. It helps you to process your data parallelly with others. Hadoop is used for developing applications that allow you to complete statistical analysis concerning a large amount of data.

Hadoop involves two primary layers at its core:

• Processing/Computational Layer (MapReduce)
• Storage Layer (Hadoop Distributed File System)

Hadoop framework also includes:

• Hadoop Common

It includes Java libraries and utilities that modules may require of Hadoop.

• Hadoop Yarn

This framework helps you to schedule the tasks and management of the cluster resources.

Hadoop is beneficial for the users to write and test distributed systems quickly. It is efficient and automatically distributes the data among machines, which helps to process data faster. It also supports a parallel work mechanism where all these machines work parallel to each other for processing these distributed data.

If you are curious to learn Hadoop online free, enroll in Great Learning’s Hadoop Free Courses and get hold of the Hadoop Certificate for Free.