Cloud Computing For Big Data
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Cloud Computing For Big Data: Magic Dual


Big Data, a quite self-explanatory term, is a name given to large collections of datasets. These datasets are stored, analyzed, and used to form company solutions. On the other hand, in this day and age, we are already very familiar with cloud computing and its functionalities which revolve around storing data and performing specific operations on that data. The question arises when we combine the two and talk about the two resources as a whole.

What is cloud computing for big data?

Cloud computing and big data, we now know what the two terms mean but how exactly are they linked together? The link occurs when we talk about using cloud computing to analyze Big Data. The results achieved from these processes are banked on by businesses. This is done by analyzing their data more effectively to improve business models. The strategies are also formed regardless of the data being structured or unstructured.

A study by Accenture reveals,

companies that do not embrace big data will lose their competitive position and may even face extinction

Why cloud computing for big data?

Having access to the cloud when analyzing Big Data is a huge plus. Organizational collaboration is improved along with simplifying connectivity as authorized employees can access data and analytic results at ease. Data storage, updating, deletion, and analyses become a piece of cake.

However, these basic actions can also be carried out from a normal datacenter then why is cloud computing such a key asset for Big Data? When we talk about data at large scales, there is a certain cost associated with storage, processing, and analyzing the data.

This may require infrastructure upgrades consisting of adding more storage capacity to physical data centers or even increasing the number of servers to cater to analytic requirements. But even after going through all the hassle, your constantly growing data will keep outweighing your existing resources. Your infrastructure just won’t be able to keep up with the rate of data growth. This is where cloud computing for Big Data plays its vital role.

Uploading your Big Data onto the cloud is the cheapest yet most efficient solution for storage and analysis. This is due to the fact that the large number of resources required which would have resulted in huge sums of capital expenditure is now no longer needed as all these facilities are already provided by the cloud. AWS provides a complete set of services to manage your Big Data.

[ Read also: Why We Can’t Imagine Digital Transformation Without The Cloud? ]

Cost of hardware

IaaS, also known as Infrastructure as a Service, is a cloud model which helps organizations completely eliminate capital expenditure. The expenditure is now shifted to operational expenditures which are flexible and adjust according to customer needs. This way you are only utilizing enough resources to meet your requirements. Not more not less. Cloud computing just eliminates the hassle of maintaining hardware to store your Big Data and helps you utilize resources according to need with the relaxation of scaling up and down whenever needed to handle data traffic spikes. Cloud’s flexibility makes it the perfect choice for Big Data analytics.

Maintenance and upgrade cost

Moreover, when we talk about analyzing Big Data. The process proves to be very costly due to maintaining the system and on-premise infrastructures along with the huge energy consumption associated with mining and the obvious frequent upgrade costs.

However, these factors are not on your list of worries anymore as the cloud cuts down your cost with its efficient resources proving to be extremely beneficial for any organization with Big Data performing analytics.

Real-time processing for big data

Big Data analytics is efficiently handled by the cloud’s Software as a Service or SaaS model which supports data processing. It is used for managing your Big Data along with processing it to generate results for future insights in context to the company. With this model real-time processing on Big Data is made possible.

A substantial amount of data can be stored at a time with processing happening simultaneously in real-time. This speeds up the process of Big Data analytics.

Backups for big data

Moving on to backing up Big Data to prevent data loss due to various reasons like a failure of infrastructure, data corruption, or even cyber-attacks, we are required to duplicate the whole data center storage, infrastructure containing servers and network equipment and storage mediums which are already so expensive to build at first. Backing it up would mean doubling your expenditure. Therefore, it is a better option to give cloud this responsibility of keeping your data secure and backed up to take the burden off your shoulders and wallets.


It is safe to conclude that cloud computing for Big Data is like water for fish. It’s completely essential for the two to be linked together to avoid massive investments upfront in the face of capital expenditure.

Furthermore, it has proven to be extremely beneficial for organizations with large sums of data but no means to analyze it. Obtaining future predictions from current data collected will give businesses an edge over their competitors.

Currently, popular cloud services like Google Drive and iCloud are used as a public storage medium for Big Data.

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