Big Data and Cloud Computing

The success of cloud computing has made it much easier to get the technology needed to efficiently handle the immense amounts of data that make up big data. The cloud not only reduces costs but has also enabled the emergence of a wide range of applications that are now available to all types of businesses.

At the same time, there is an explosion of information on the web. Social networks are a world where companies and ordinary users generate a large amount of data every day. Organizations and institutions are also creating data daily, which can be challenging to manage in the long run. Some statistical data on the generation of Big Data in the last five years gives us an idea of this growth:

2.5 trillion bytes (2.3 trillion gigabytes) of data are created daily.

40 zettabytes (43 trillion gigabytes) of data will be created by 2023.

Most businesses in the US have at least 100 Terabytes (100,000 Gigabytes) of data stored.

These high volumes of data present a challenge to the cloud environment. How to manage and secure the essence of this information instead of simply storing it?

Also Read: Cloud Computing And Its Deployment Methods

The cloud and big data are an ideal combination for this. Together, they provide a scalable and adaptable solution for big data and business analytics. Let’s look at these advantages in detail:

Agility: Traditional data management and storage infrastructure are slower and more challenging to manage when subjected to today’s volume requirements. It can take weeks to get a server up and running. Cloud computing can provide all the resources that the business needs. For example, a database in the cloud can have thousands of virtual servers, which get it up and running smoothly in a matter of minutes.

Cost reduction: Cloud computing means you don’t need a big budget to reap the benefits of the latest technology. Not only is it more affordable, but it allows you to pay for what you need and expand your resources as your business grows. Before the cloud, companies had to make a significant investment in creating IT departments and in the acquisition of customized solutions, to which the cost of updating the hardware had to be added. Now any organization can host its data on external servers or pay only for the storage space and energy it consumes.

Data processing: The explosion of unstructured and semi-structured data, especially from social networks, greatly complicates processing. Once again, the solution is in the cloud, where Big Data Analytics platforms such as Apache Hadoop make it easier and more accessible for small, medium and large companies.

Viability: While traditional solutions would require adding more physical servers to the cluster to increase processing power and storage space, the virtual nature of the cloud allows for the availability of seemingly limitless resources. With the cloud, businesses can easily and quickly scale up or down to the desired level of processing power and storage space.

Scope: Big Data analytics imposes new processing requirements for large data sets, and the cloud environment is the perfect platform to accomplish this task. Most solutions can be found in the cloud following a SaaS model, so there is no need for additional infrastructure.

Also Read: Big Data: The Challenge Of Managing Structured And Unstructured Data