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Hadoop and Big Data

Overview of Hadoop

Hadoop can be easily understood and explained as an open sourced and Java based framework which is based on a system called HDFS, Hadoop is widely used for data storage of any type, it incorporates extensive processing power and is effective in handling many simultaneous tasks. Hadoop has managed to attract many big organizations towards itself because of it is affordability, high storage options and the unsurpassable processing power. Hadoop is perfect for data analysis especially when it comes to handling data in bulk along with large-scale indexing of the data and its reporting.

Reasons to go for Hadoop

For Handling What We Call “Big Data”: If you have huge volumes of data and such volumes are multitudinous, Hadoop can be an option, what we need to keep in mind is that by talking about Big Data we are basically dealing with data of at least terabytes and petabytes. In this era of digital world where data from social media platforms and the internet is immeasurable, there has been a proliferated and exponential growth both in terms of volume and variety of the data so to cope up with this dynamic and rapidly increasing data with varied categories, Hadoop serves as a right choice.

For Concurrent Data Processing:

The MapReduce algorithm ideally befits any application were simultaneous data processing is required.

Protection against Hardware Failure: This is clearly understood that there is a huge bulk of data we are dealing with but what can be a matter of concern is its protection measures. Hadoop incorporates fault tolerance which basically means that even in a case of hardware failure all the data is completely protected. Another interesting fact is that when there are situations that lead to node failure all the tasks assigned to particular nodes gets transferred to other nodes and thus computing never fails.


Security is of prime concern as we deal with data and is what Hadoop takes active care of. The security layers are Authentication, Authorization, and Audit & Data Protection; they can be called as layers of protection in which authentication is the most common form of protective measure which can extend from user to services and this layer is followed by authorization where access list can be accessed, controlled and modified. Encryptions of data are also done both at the Operating System and hardware level which makes the data highly secure.


In all the traditional databases we have been using for ages there was one trouble which was at the time of storing different types of data like images, audios or videos they were pre-processed without which they could not be dealt with. To elucidate further – you cannot save images, audios and videos in SQL or MYSQL in their raw format so before saving they are processed and converted to binary format whereas in Hadoop there are no such restrictions.

IIHT serves as a perfect guide for every aspirant who is looking forward to building their career in Hadoop, speaking of the employment scope, there are innumerable reasons for why Hadoop is the first choice for organizations dealing with Big Data and at IIHT we train candidates in this revolutionary framework and make them skilled to work relentlessly and productively on Hadoop which is now the first choice for every organization and with increase in data every day there is a huge demand for professionals in this field who can effectively work on and manage Hadoop and “Big Data”.