Edge Computing and Cloud Computing are two distinct data processing paradigms that, although they can complement each other, are better suited to different scenarios and requirements. Cloud Computing is based on centralising resources and services in large data centres, providing flexible and scalable access to vast processing and storage capacity. In contrast, Edge Computing decentralises data processing, bringing computation closer to the data source—that is, at the ‘edge’ of the network. This proximity reduces latency and enables real-time responses, a critical feature for applications that require instant decision-making. The choice between these two paradigms depends on technical specifications, latency requirements, network availability, and associated costs.

For applications that demand low latency and real-time responses, Edge Computing is generally the preferred option. Examples include autonomous vehicles, augmented reality, telemedicine, critical infrastructure management, and smart manufacturing. In these cases, the speed at which data is processed is crucial for system performance and safety. Edge Computing allows data to be processed locally, significantly reducing response times. It also ensures that critical operations can continue uninterrupted even when cloud connectivity is intermittent or unreliable.

On the other hand, Cloud Computing is more suitable for applications that require large storage and processing capacity but are not dependent on extremely low latency. Services such as long-term data storage, Big Data analytics, and enterprise applications like ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) benefit greatly from the cloud’s scalability and flexibility. The cloud enables organisations to handle large volumes of data and run complex analyses without worrying about the underlying physical infrastructure. Additionally, the cloud offers cost advantages, as organisations pay only for the resources they actually use. This can be more cost-effective for variable workloads that do not require real-time processing.

Another key difference between Edge Computing and Cloud Computing is data security and privacy management. In Edge Computing, data can be processed and stored locally, reducing the need to transmit sensitive information across the network and minimising the risk of interception and attacks. This is particularly important in sectors such as critical infrastructure management, healthcare, banking, and defence, where data privacy and security are top priorities. However, this decentralisation also means that security must be managed at multiple points, which can increase complexity. In comparison, Cloud Computing provides robust centralised security solutions, managed by providers that implement the latest technologies and best practices to protect data.

The choice between Edge Computing and Cloud Computing comes down to the specific needs of the application and the operating environment. Edge Computing is best suited for applications that require low latency, high availability, and local data processing, while Cloud Computing is ideal for those that demand large storage capacity, scalability, and are not

sensitive to latency. In many cases, a combination of both paradigms—known as a hybrid architecture—can provide an optimal solution by leveraging the advantages of edge computing for time-critical, real-time tasks and the cloud for large-scale processing and storage. This combination allows organisations to maximise efficiency, reduce costs, and improve both the security and performance of their systems.