When it comes to edge computing, self-driving cars, autonomous robots, and automated retail come to mind. My favorite edge computing example comes from a fast-food restaurant. Every restaurant location uses analytics to make judgments such as when to put the fries in the fryer for the ideal crispiness. Edge computing is used to hyper-personalize these processes for each retailer. When leveraging transactional sales data, the organization can develop a cloud forecast to anticipate how many waffle fries should be prepared each minute over the course of a day.

However, it’s at the cutting edge that each store micro-adjusts the original estimate based on unique on-site, real-time data from their kitchen and point-of-sale systems. Quickly delivering services with a personal touch. Edge computing is capable of accomplishing this. Is edge computing the death knell for cloud computing? Certainly not! Edge computing will not only be a vital component in controlling the edge, but it will also fuel the next generation of cloud computing.

Edge computing is a novel feature that puts computation to the network’s edge, where it’s closest to people and devices — and, most importantly, as close to data sources as feasible. In cloud computing, on the other hand, data is generated or collected in multiple locations before being sent to the cloud, where computation is centralized. Data processing at scale is easier and less expensive with centralized cloud computing. When highly interactive — and fast — experiences are required, edge computing reduces the risk of network disruptions or cloud delays. By embedding intelligence and automation into the physical environment, Edge allows these experiences. Consider streamlining industrial operations, performing robotic surgery on a patient, or automating mine production. This is where we define how information is saved, disguised, summarized, and routed. It’s also where we can add controls to handle data dependability, privacy, and compliance.