Edge Computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, reducing latency, bandwidth usage, and dependencies on central cloud infrastructure by processing data at or near the source of its generation.

For enterprise architects, Edge Computing represents a significant architectural shift from centralized cloud models toward distributed processing. This approach is driven by several factors: the exponential growth of IoT devices generating massive data volumes; latency-sensitive applications requiring real-time responses; bandwidth constraints for data transmission; and resilience requirements for operations in environments with unreliable connectivity. Edge architectures typically implement a hierarchical model with multiple tiers—from device-level processing (sometimes called “far edge”) through local aggregation points to regional data centers and central cloud platforms. Key architectural considerations include workload placement strategies determining which functions run at which tier; data synchronization mechanisms between edge nodes and central systems; orchestration frameworks managing distributed application deployments; and security approaches addressing the expanded attack surface. Enterprise architects must design for heterogeneous environments, considering how containerization, lightweight virtualization, and specialized hardware accelerators can be leveraged at different edge tiers. The operational model for edge computing introduces unique challenges around remote management, software updates, monitoring, and support that must be addressed in the architecture. As 5G networks and specialized edge platforms mature, architects should develop frameworks for evaluating edge computing opportunities—particularly for use cases in industrial IoT, autonomous systems, augmented reality, and situations requiring local data processing for privacy, compliance, or bandwidth optimization reasons.