Home » Big Data in the Cloud: What It Is, Advantages, and Disadvantages

Big Data in the Cloud: What It Is, Advantages, and Disadvantages

Veronica May 10, 2023

Big Data in the Cloud – In today’s world, we can’t understate the value of data. Organizations, regardless of size, know this well. They use it not just to identify challenges but also to measure performance and search for new opportunities for growth.

While the benefits of big data are tremendous, they require vast amounts of computing resources. This can strain the intellectual and financial capital of businesses, including even the large ones. This is where the cloud helps.

Clouds provide organizations with almost unlimited computing resources and services. In this post, we explain big data in the cloud, from the definitions of big data and cloud, the advantages and disadvantages, to cloud deployment models.

What’s Big Data in the Cloud?

Before we delve further into what big data in the cloud is, let’s get to know what big data and cloud are first. These two concepts are often interwoven to the point they are hardly separable. Knowing what each concept is will help you get a clearer picture.

Big data


The term big data refers to massive data, whether unstructured, structured, or semi-structured, derived from various sources that isn’t easily stored, managed, or analyzed by traditional business intelligence tools.

Big data is characterized by 3Vs, namely

1. Volume

Here, volume refers to the size of data. The size of data in big data is extremely large, often in petabytes (soon enough, zettabytes). Now you know what the “big” in big data refers to.

2. Variety

This refers to the types of data. Data in big data can be structured like data contained in databases, unstructured like images, videos, emails, tweets, and more, or semi-structured.

3. Velocity

Velocity in big data refers to data processing speed. In other words, how fast information can be processed.



Cloud computing, or simply cloud for short, provides computing services and resources on demand. In the cloud, a user can

  • arrange the infrastructure of cloud-based storage resources and compute instances
  • upload data sets
  • perform analyses
  • connect cloud services

Cloud users can utilize almost unlimited resources across the public cloud, use them as long as required, and then dismiss the environment. And they pay only for the services and resources that were actually used, which is a huge plus for organizations of any size.

As for cloud deployment models, there are 4 options to choose from. More on this later.

How big data and cloud are related


1. Cloud provides access to big data in a scalable and cost-effective manner

Back in the day, saving and processing data was expensive as technology was not as advanced as today. To handle more data, bigger machines were needed. This, in turn, caused the cost to scale exponentially.

Things are different with big data. As it is based on parallelized architectures, the cost doesn’t scale exponentially. Rather, it scales elastically and linearly. Moreover, clouds offer on-demand access and pay-per-use mechanisms, which organizations can take advantage of.

2. Cloud makes it easy to handle big data

Handling big data is not easy, especially if you don’t use the right tools for it. The thing is, big data is only useful if it is analyzed. If it is idle, it is useless. The good news is that the cloud is the perfect tool for it.

Using the cloud, users can build, manage, and secure big data with ease. Moreover, an organization needs not have its own cloud infrastructure. It can work with a cloud provider who handles the infrastructure.

3. Cloud helps us manage data

Managing a vast amount of data is a difficult task. When it comes to big data, we are talking about petabytes of data. The good news is plenty of emerging cloud tools provide users with metadata systems and pre-defined industry data models.

4. These systems catalog data and give you a unified view of it.

Cloud offers tools using which we can experiment with data easily

Analyzing big data gives us insights. But you will need tools for it. Thankfully, clouds offer various tools for data pipelines and model management. These tools enable data scientists and engineers to not just create, but also experiment and publish models and connect them in a pipeline.

With cloud handling the data “plumbing” for you, you can put your focus on the meaningful and actionable insights that can help your business.

The Advantages of Big Data in the Cloud


Combining the two technologies certainly bring a lot of benefits for organizations. Some of the biggest advantages of big data in the cloud include

1. Accessibility

A lot of clouds provide a global footprint. This makes deploying resources and services in most major global regions possible. This, in turn, allows data and processing activity to take place near the region where the big data task is situated.

Let’s use an example. Let’s say that an organization stores data in a certain region of a cloud provider. It will be relatively easier and simpler for the organization to implement its resources and services for a big data project within that specific cloud region than moving said data to another region.

This accessibility benefits organization of all sizes.

2. Cost

The data center of an organization is a massive capital expense. How could it not? Not only does the organization have to pay for the hardware, but it must also pay for power, ongoing maintenance, facilities, and others.

Clouds can help make it more cost-effective. Using the cloud, an organization can subscribe to a pay-per-use model that provides them with on-demand resources and services.

3. Scalability

Big data in the cloud offers scalability. A data center has limits when it comes to physical space, power, and cooling. There is also a limitation in the budget to purchase and deploy the hardware necessary to build a big data infrastructure.

Using the cloud, these limitations are no longer issues. An organization can take advantage of a cloud provider’s already existing infrastructure and software services, using them for its big data projects.

4. Agility

The next advantage of big data in the cloud is agility. Big data projects vary. For example, a big data project may require no more than 100 servers, while another project requires 2,000 servers or more.

With the cloud, organizations can utilize as many resources as required to accomplish a task and then release them when the task is accomplished.

5. Resilience

The real value of big data projects is their data. And here’s one of the biggest benefits of big data in the cloud: data storage reliability. Clouds maintain high availability in storage resources by replicating data.

Note that this is standard practice. Need more durability? No worries. There are even more durable and resilient storage options in the cloud.

The Disadvantages of Big Data in the Cloud


As good as it is, big data in the cloud also has its disadvantages. Some of the major disadvantages are

1. Network dependence

One of the major big data in the cloud disadvantages is network dependence. Cloud use entirely depends on complete network connectivity. That is, from the organization’s LAN network, across the internet, and to the network of the cloud provider.

In case an outage occurs in any of the network paths, at best it can result in increased latency and at worst, complete cloud inaccessibility. Due to how impactful outages can be, network dependence should be a factor to consider if an organization plans to use big data in the cloud.

2. Storage costs

While using the cloud can be cost-effective, it can also be costly. The three main issues of storage costs are data storage, data retention, and data migration. Loading a vast amount of data into the cloud takes time, and the data storage incurs a monthly fee.

Data retention can be an issue as retaining data that have no value also costs money. Not to mention moving data may incur additional fees.

3. Security

Security is another disadvantage of big data in the cloud. The data used in big data projects may involve data that is subject to data protection and other government- or industry-driven regulations.

As such, organizations that use clouds must take necessary steps to maintain security in cloud storage and computing. This can be achieved through sufficient authentication and authorization, data encryption, and meticulous logging of how the organization accesses and uses data.

4. Lack of standardization

Lastly, lack of standardization in big data in the cloud. On one hand, the lack of standardization provides cloud users with some form of freedom. On the other, it may lead to poor performance or worse, exposing the organization to potential security risks.

Cloud users, especially businesses, should document their big data architecture with any procedures and policies regarding its use. Such documentation can become a foundation for improvements and optimizations in the future.

4 Cloud Deployment Models


Currently, there are four cloud deployment models that organizations can choose from for big data deployment. These models are private, public, hybrid, and multi-cloud.

1. Private

The private cloud operates in a company’s local data centers. While some companies prefer to use collocated data center facilities, the point is still the same: everything is behind a company’s walls.

This cloud deployment model gives organizations control over their cloud environment, which is often done to accommodate availability, security, or specific regulatory requirements.

The downside of private cloud is that the organization has to own and operate the whole infrastructure, which makes it a costly option. That said, a private cloud is a good option for sensitive, small-scale big data projects.

2. Public

The public cloud offers on-demand resources and scalability, which makes it an ideal option for big data deployment of almost any size. Moreover, with the public cloud, the responsibility is shared. The provider handles the security of the cloud while the user has to manage and configure security in the cloud.

3. Hybrid

A hybrid cloud is a mix between the private and public clouds. In a hybrid cloud, both private and public clouds are integrated. This type of cloud deployment is useful when sharing specific resources.

For instance, an organization that operates a hybrid cloud may use its local private cloud as big data storage and use the public cloud for big data analytics services and computing resources.

Hybrid clouds are more difficult to build and manage.

4. Multi-cloud

As the name suggests, a multi-cloud is when an organization uses a combination of private, public, or hybrid clouds. For example, an organization may use multiple private and public clouds, or multiple hybrid clouds. The clouds can be connected or not at all.

Since multi-cloud involves multiple cloud deployment models, it is more complex to manage.

Big data in the cloud offers many advantages, such as accessibility, cost-effectiveness, scalability, agility, and resilience. But, as good as it is, it also has disadvantages like network dependence, storage costs, security, as well as lack of standardization.

In any case, big data and cloud computing play a vital role in today’s digital society. Using them, individuals and small organizations alike who have great ideas but limited resources can thrive and be successful. It is an amazing combination for organizations of all sizes.

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