The Roles and Relationships of Big Data and Cloud Computing

Big Data and Cloud Computing

Learn how big data and cloud computing go hand in hand.

What is the buzzword in today’s tech world?

It is the most important thing for both the consumers and service providers.

The buzzword is “data.”

There are tons of data online and everywhere. At least 8 out of 10 – or sometimes 10 out of 10 – things that we use every day produce data.

Our social media accounts, mobile phones, photos, communication, services, gadgets, credit cards, businesses, vehicles all produce data.

Still, it is not enough to understand how much data humans produce. So, let’s look at some numbers to put it into perspective and help understand how vast a repository of data we are leaving behind with every click and login.

According to Domo, every person-generated 1.5 MB of data every second in 2020. On average, 2.5 quintillion bytes of data per day were created every day by us.

That number is huge. It is 1, followed by 18 zeroes.

This is called ‘big data. But data without any insight is just raw information. This stack of numbers, figures, and decimals is of no use if it can’t be processed to extract meaningful information or use it to improve service delivery to people.

The task to turn this data into something useful comes down to computation, calculations, and analytics. To analyze the relationship between big data and cloud computing, let’s understand both terms in detail.

What are Big Data and Cloud Computing?

Big Data

Basically, big data is tons of data produced by various activities online and on other electronic devices.

For example, if you search for something on Google, it is data. The messages you send on WhatsApp, the emails you receive on Gmail, the food you order on DoorDash, the things you ask Alexa to do, and every other thing you do on an electronic device is data.

There are five main characters of big data, called the V5:

  1. Volume: It is the amount of data produced from different sources.
  2. Variety: It represents the variety of sources from which data is produced.
  3. Velocity: It is the measure of the speed with which the data is produced.
  4. Veracity: It represents the quality of data that is gathered and measures its accuracy.
  5. Value: It shows the value of data after it is analyzed.

In big data, we are not talking about MBs or even GBs of data. We are looking at several quintillion bytes and more.

So, here lies the problem: such a huge amount of data cannot be processed and analyzed on ordinary PCs.

To process this data, one needs vast storage, computers that can make complex calculations very fast, and resources to visualize it to be used for any meaningful purpose.

Centralized storage with limited computation power can work for traditional data, but not with big data. So, cloud computing presents the solution to this problem.

Cloud Computing

Cloud, as we know, refers to something that doesn’t have a physical presence, at least not with us. Cloud computing refers to on-demand computing resources and systems which provide many services to users without being bound by local resources.

Take the example of cloud storage. Our data is stored in decentralized servers somewhere far away in some other part of the world. We don’t have to maintain these servers to store data, it is done by someone else, and we only have to upload it.

Cloud computing is divided into three kinds of services:

Software as a Service (SaaS)

In Software as a Service, various application software are provided to the users, which they can use without installing on their systems. The user is not responsible for the software’s working, and they have to modify settings according to their usage.

Dropbox, Salesforce, and Google apps are an example of SaaS.

Infrastructure as a Service (IaaS)

Infrastructure as a Service provides various infrastructure support to the users, with the only difference being that the infrastructure doesn’t exist in physical form. In the IaaS model, all the services like hardware, software, servers, storage, and other infrastructure are provided by a third party through virtualization.

Examples of IaaS include Linode, Amazon Web Services, Microsoft Azure.

Platform as a Service (PaaS)

Platform as a Service delivers a framework for developers which they can use to build and create applications. All the platform’s work is managed by third-party service providers, while developers can manage their applications. 

Examples of PaaS include AWS Elastic Beanstalk, Heroku and Force.com.

Relationships of Big Data and Cloud Computing

The use of technology and electronic devices and their integration has led to the rapid development of an electronic information society. This society involves the massive generation and transfer of big data. Storing, sorting, and analyzing this data is a problem.

To solve this problem, big data and cloud computing go hand in hand. Here is how:

Storage

One of the biggest concerns with big data is its storage. Physical infrastructure is not enough to store this huge amount of data properly. Even if the capacity is not the issue, the scalability of physical storage cause issues for the users.

Cloud computing provides reliable, secure, and scalable storage facilities to store and access big data. These remote storages take away the maintenance responsibilities from users, and the decentralization means there is no need to have any physical infrastructure.

Since the cloud storage services are based on a pay-as-you-go model, scalability is not an issue as this storage can easily be increased or decreased as per users’ requirements.

Accessibility

The SaaS, IaaS, or PaaS models delivered by cloud services are all virtual services hosted by third parties. The users can modify them and access them from their browsers without installing and running the software.

The ease of accessibility is coupled with the swift transfer of data through many channels without an external source. Take the example of a Google Docs file. Unlike the documents that are stored on your computer, it is stored on the cloud. To send or transfer this file, you have to simply copy the URL and send it.

Security

Data security is a big issue in today’s world of information technology. According to Statista, the number of cases of data breaches in the US alone stood at 1001 in 2020. Cloud services are open-sourced and accessible. Thus secure storage is a challenge.

Cloud services provide various levels of security based on users’ needs. A customer might require protection of their data only through basic logical access, or they can request sophisticated security features like encryption, data hiding, logging in, etc.

It involves a Service Level Agreement, which is a contract between the users and the service providers. This agreement includes rules to protect data, ensure security, provide accessibility, modify capacity and provide scalability.

In this way, cloud computing eases the storage, handling, and accessibility of big data. With the increasing penetration of electronic devices, the need for cloud computing is only going to increase.

Final words

Cloud computing has made it easy and cheap to store data. With this decentralized storage, we are seeing a revolution in how data is managed and stored. Without data being accessible to use anywhere at any time, it has made it easier for remote teams to work on projects that involve collaborations.

The increasing importance of cloud computing is further accentuated by the disruptions caused by the coronavirus pandemic. With work-from-home being implemented all over the world, people are accessing data remotely and managing their work.

In the future, the use and demand for cloud computing are only going to increase. How it deals with this demand while also ensuring strict security protocols to store and transfer data safely will determine how widespread the adoption of cloud computing will be.

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Mehul Rajput

Mehul Rajput

Mehul Rajput is the CEO of MindInventory, a leading mobile app development company that provides web and mobile app development as well as UI/UX design services from startup to enterprise-level companies. His role involves heading the operations related to business and delivery with strategic planning and defining roadmap for the future.