How edge computing delivers cloud capabilities near the user
The foundations of distributed system design are shifting.
For over a decade, the dominant model has centralized processing and storage in large regional data centers. This traditional cloud approach delivered strong scalability, but it struggles with latency, data locality, and bandwidth constraints as applications demand lower-latency responses across geographically distributed users.
At the same time, IoT growth, 5G rollout, and increased real-time requirements are exposing the limits of purely centralized cloud architectures. In response, many teams are adopting hybrid cloud–edge models that place compute closer to where data is produced and consumed.
What is edge computing?
Edge computing places compute resources physically near the devices that generate or consume data. This enables local processing, faster responses, and reduces the need to send all data to the central cloud.
Understanding the impact of this shift is easier when we look at how data flows differently in traditional cloud setups vs. edge-first architectures.
The impact is tangible. Workloads that previously required cross-region processing can now execute locally, with only aggregated or long-term data sent to the cloud. This shift raises a practical question: when latency, bandwidth, and locality define performance, how should system designers respond?
This newsletter analyzes the resulting evolution in distributed system architecture. It covers:
The core motivations driving the move to the edge.
The evolution from cloud-centric to edge-enhanced architectures.
Key design patterns and frameworks for building edge-aware systems.
The critical trade-offs you must navigate.
Data synchronization strategies for maintaining consistency.
Let’s begin!
