Breaking down Hadoop in layman terms

Hadoop! This strangely sounding term is bound to spring up every now and then if you are reading or doing some research on Big Data analytics. So, what is Hadoop? What does Hadoop do and why is it needed? Read on to delve into the fine details of Hadoop.

In simple terms, Hadoop is a collection of open source programs/procedures relating to Big Data analysis. Being open source, it is freely available for use, reuse and modification (with some restrictions) for anyone who is interested in it. Big Data scientists call Hadoop the ‘backbone’ of their operations. In fact, Hadoop certification has become the next stepping stone for experienced software professionals who are starting to feel stagnated in their current stream.

The birth of Hadoop

As it had always been with the IT industry, it was the innovation of a bunch of forward thinking software engineers at Apache Software Foundation that led to the introduction of Hadoop. These engineers realized that reading data from bulk storage devices took longer than reading it from small storage devices of multiple numbers working simultaneously. Moreover, eliminating one single large storage location also made data available to multiple users spread across a network.

The first version of Apache^TM Hadoop framework was released in 2005 and ever since it paved the way for better Big Data Analytics. Internationally, Hadoop courses are in hot demand since they offer the promise of a lucrative career in a domain, i.e. Big Data, which is going to grow with leaps and bounds.

Trivia: Hadoop is named after the toy elephant belonging to the son of one of the key developers.

The 4 ‘Modules’ of Hadoop and what they stand for

Hadoop comprises mainly of 4 modules namely, DFS, MapReduce, YARN and Hadoop Common. Each of these module has a specific task assigned to it for facilitating Big Data Analytics.

1. Hadoop Common

Hadoop Common provides Java-based user tools that are to be used for accessing and retrieving data stored in a Hadoop file system.

2. Distributed File System (DFS)

In Hadoop, Distributed File System is what enables data to be stored in a form that can be easily accessed and retrieved. The data will be stored across several interconnected devices which can be reached for using MapReduce.

3. YARN

The fourth and final module, YARN manages the system resources when the analytics are being conducted on the data stored in linked devices.

4. MapReduce

MapReduce basically does two primary functions: it reads data from databases and puts them into a format that is suitable for Big Data Analytics. Further, it breaks down the data into meaningful information that can be used for interpretation. For instance, how many male members above the age of 30 in a given data population.

Over the period of years a good number of other features have also come to form essential part of the Hadoop framework. However, the above-mentioned four modules continue to be the main elements that denote the Hadoop architecture. Hadoop training courses also assert more impetus on these four core elements as the framework is deemed to be updated revolving them.

How Hadoop favors the Fortune companies

Hadoop is meant for Big Data analytics. Hence, its primary users are corporations that have massive chunks of data awaiting analysis and interpretation across multiple geographical locations. Needless to say, at least 90% of the Fortune 500 Companies have integrated Hadoop tutorials and training program for their engineers to make better use of Big Data.

The International Data Corporation’s “Trends in Enterprise Hadoop Deployments” report states that at least 32% of the enterprises have actually deployed Hadoop and another 36% preparing to deploy it within the next one year. Another similar report by Gartner, Inc. also forecasted 30% of enterprises to have already invested heavily in Hadoop infrastructure.

Four reasons why corporations will continue sticking on to Hadoop:

• It is flexible. More data systems can be added, edited or deleted when required.

• It is cost-effective and practical. More storage units can be added by procuring readily-available storage from IT vendors.

• It is open source, providing ample flexibility for corporations to customize it the way they want for effective use. Unlike bespoke off-shelf software systems that are rigid and complex to customize.

• Commercial versions like Cloudera are available in the market which further simplify the process of installing and setting up the Hadoop framework. 

In a nutshell

Hadoop in a nutshell (a large one, that is) is an open-source, flexible and robust framework for Big Data Analytics. It is made up of 4 major modules and more are being added to it for diverse applications. Apache presented Hadoop to the world in 2005. There is a thriving community of Hadoop developers and users where anything and everything related to the framework can be asked for and discussed. Find it here.