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Cloud Computing in Big Data

Role of Cloud Computing in Big Data

A human brain consumes less than 20W power on average and has more than 2 petabytes of storage. If we could build computers that big, it would need a power source as massive as the sun. Even the supercomputers dissipate massive and massive amounts of heat and consume an equally high amount of electricity and the standard 220V power delivered to your home isn’t at all enough to run a high-end computer. The electric meters in our household, that measure how much energy we’ve used in one bill cycle, may burn out due to excess heat if anything as large and powerful as the supercomputer is connected.

Developers and data analysts working from home, especially on big data, depend on remote desktops or cloud computing because the power needed to run a compatible computer isn’t available everywhere.

To put things in short, cloud computing has a role just as large as big data has in machine learning and cloud computing. Let’s see how

It provides robust platform to derive meaningful conclusions

Our brains are trained to understand and comprehend things happening around us without much efforts. This is because humans have highly developed and sophisticated brain. Computers, on the other hand, are nowhere near as close and won’t be for at least the next fifty years or so. We have the innate ability of learning lessons for life from one or two events, while computers need to be trained with a thousand possibilities even for the most mundane of tasks.

Big data involves deriving conclusions from what looks like a random set of the dataset. This data is usually unordered, cluttered and needs filtering before you could infer anything. Given the amount of data that you’d typically work with, applications are oftentimes shifted to a more robust computing platform, which is usually the cloud. Having your apps moved to the cloud comes with several added advantages as well. First, the information can be worked upon from any computer, irrespective of whether it meets compatibility requirements. Second, your dataset can be remotely monitored and worked with i.e. you no longer need to stick to one system to do your work.

It places zero load on your current system

Since the entire big data is hosted onto the cloud, your system per se has little part to play. Though having an advanced, high-end system always pays off with better app performance, moderate to mid-range computers work equally fine because the workload is entirely on the cloud.

This means that you can work on your big data with more affordable, mid-range laptops and computers, without the need to rig massively expensive configuration.

Cloud computing facilitates sharing & collaboration

Big data analysts seldom work alone. In fact, in bigger organizations, there could be over 20-30 people working together on the same dataset. In a conventional setup, where individuals work on their LOCAL instead of the cloud, queries will have to be pooled together before any meaningful conclusion can be drawn. This is because every member will have his/he own data to work with and changes made onto one database isn’t going to reflect on any other data, until the same is manually done by someone on all data instances.

On the contrary, with the cloud, every member can work on the parent data itself (or a cloud backup of it), with changes almost instantly reflecting on the original database.


Big data is a crucial condiment of AI and ML and without the means to work on big data, artificial intelligence would only be science on papers. Big data in itself could be a tricky art to master but what’s trickier is setting up the right platform and ecosystem needed to work on regressions, matrix algebra and other sorting techniques that help derive insights from the data.

At a glance

  • Human brain consumes too little power for the calculations that we undertake in our everyday lives
  • Big data techniques are fundamental to AI & ML and help derive meaningful conclusions
  • Hosting on the cloud can offload workload to remote systems and alleviate onsite loads
  • Clouds facilitate sharing and collaboration when working with bigger databases

As data scientists, you would appreciate the ability to quickly launch compatible environments and test whether your codes work the way you want. Go4hosting cloud enables you to launch virtual machines in an instant so you can test and debug your codes before deploying them on a live database. We’re also offering a $50 signup credit every time you sign up with us. Drop your contact details in the chat menu below and we’ll help you set up the right cloud environment.

About Nishant Nath (36 Posts)

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