Data Storage & Cloud:
Which Option is Right for Your Business
Written by Lynn Orlando
Published on March 25, 2020
Additionally, the cloud enables enormous scale that data-intensive, parallel workloads require, especially with regard to unstructured data. And, the cloud makes it reasonably affordable. A majority of AI and big data analytics applications are deployed in the cloud today primarily because legacy storage systems in on-premises storage environments are incapable of supporting them economically.
While cloud technology allows for easy storage and consistent accessibility, it does come with some challenges. Privacy, security, and lack of staff training are the top roadblocks to cloud adoption. Another issue for many businesses is backward compatibility with legacy storage systems. This backward compatibility requires a solution that can combine new and older technologies. Depending on your specific needs, there are three types of cloud deployments that might work best for your environment: private, public, and hybrid clouds. Of course, each comes with its advantages and disadvantages, which we’ll discuss below.
Cloud by the Numbers
- $623.3 billion – global cloud computing market estimate by 2023 (Report Linker)
- 90% – percentage of businesses on the cloud (451 Group)
- 38% and 41% – percentage of workloads that average companies run in public clouds and private clouds, respectively (RightScale)
- 58% – hybrid cloud adoption rate (RightScale)
Private Cloud: Security and Control
Private clouds allow only certain businesses or groups to access resources. Private clouds may be located either onsite in an on-premises data center or through a third-party cloud service provider with its own dedicated servers.
One of the key advantages of a private cloud model is that it can be more secure than a public cloud since only a limited number of designated users have access to it. Additionally, private clouds are considerably more customizable than public cloud deployments, which enables them to meet unique IT requirements more efficiently.
On the other hand, private cloud technology can be more costly. Additionally, businesses are limited in the resources available depending on where the cloud is deployed. Furthermore, depending on the security rules in place, it may be more challenging to allow for remote access if designated devices or applications are required.
Despite the increased costs and limitations on remote accessibility, private cloud models can give developers the ability to integrate AI with sufficient privacy. These solutions are particularly important when dealing with large quantities of sensitive data, as is often the case with large data sets used in training and deploying sophisticated AI.
Public Cloud: Affordable, Flexible Scalability
When people refer to the cloud, most people think of the public cloud. Many public clouds are hosted by third-party providers such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure. They offer bundled access to certain storage solutions, bandwidth, and hardware.
One of the advantages of using a public cloud is the ability to customize services based on only on what a company needs, with the ability to pay only for what they use. Public clouds also eliminate the hassle of trying to set up and regularly update the infrastructure because the service provider handles everything as part of the contract.
Security is one of the biggest concerns around public cloud. However, security is becoming less of a concern as many security vendors produce cloud-native solutions, and the public cloud providers themselves have baked in better security. Regardless of the security precautions in place, a public cloud still falls short on security compared to a private cloud environment simply because there are fewer risk factors with the private cloud. Additionally, regulatory compliance requirements can be an issue with public cloud networks as servers often are set up in multiple locations and can span multiple geographies.
When it comes to AI, the public cloud is a game-changer because of the computing power that these resources offer. If your primary need is computing power to run an AI application at a lower overall cost, public cloud solutions may be a great option. However, some businesses find that they pay a premium for transferring large quantities of data, even if the scalable pricing model seems appealing at first.
Hybrid Cloud: Best of Both Worlds
Hybrid cloud models bring together a combination of multiple clouds and/or on-premises servers into a well-orchestrated single environment. Many organizations use this approach as they are transitioning to cloud. They may start by moving a single workload like an AI application to the cloud to take advantage of compute power and scale. At the same time, they might leave other workloads like finance on-premises to maintain more control over security and access.
The beauty of a hybrid cloud setup is that it enables users to benefit from both private and public cloud technologies and capabilities. Simply put, hybrid models offer the flexibility that many organizations require.
Managing a hybrid cloud environment can be a challenge since there are so many different components. Admins are tasked with keeping up with multiple update schedules, security patches, compatibility issues, and so forth. There are tools available to help manage hybrid environments.
Choose the Cloud That’s Right for You
Depending on your company’s individual needs when it comes to developing and running applications, you might benefit from using either a public cloud, private cloud, or a hybrid of both. When it comes to data storage and access, it’s essential to look for a flexible data storage solution that can work in cloud-native and on-premises environments, which is what the Stellus Data Platform was designed to do. If you’re planning to move all or part of your workloads and applications to the cloud, we’d love to show you how Stellus can make that process easier.