Centralized infrastructure, offering on-demand access to shared computing resources, contrasts with a decentralized approach that brings computation and data storage closer to the source of data generation. One relies on remote servers and networks, while the other processes information locally, reducing latency and bandwidth consumption. Consider, for instance, a video surveillance system. With the former, all video streams are transmitted to a data center for analysis. The latter, conversely, analyzes the footage directly at the camera or a nearby server, only transmitting relevant events or alerts.
These paradigms are reshaping industries by providing scalable resources and optimized performance. The former enables cost-effective storage and processing of massive datasets, facilitating data analytics and machine learning. The latter allows for real-time decision-making in environments where connectivity is limited or unreliable, such as autonomous vehicles and remote industrial sites. Initially, the focus was on centralized processing, but growing demands for speed, security, and resilience are driving the adoption of distributed solutions.
