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Shifting compute and storage closer to data sources to reduce latency and conserve bandwidth.
Edge computing moves processing and storage closer to data sources to reduce latency, conserve bandwidth, and enable local decision-making. It comprises distributed edge nodes, lightweight platform services and hybrid integration with centralized cloud backends. Common use cases include IoT telemetry, real-time control loops and constrained-network applications.
Measurement of time from event to local/central response.
Amount of data sent per time unit to central backends.
Percentage of time edge instances operate correctly.
Manufacturing uses local edge controllers for closed control loops and sends only aggregated KPIs to central systems.
Video analytics runs on-camera or on a local node; only relevant events are archived or uploaded to the cloud.
Branches keep personal data locally and synchronize anonymized metrics centrally.
Define requirements and latency targets and identify suitable edge sites.
Evaluate hardware and platform options and deploy pilot nodes.
Design network and security architecture for edge-to-cloud connections.
Adapt applications for local execution and define data flows.
Set up automated monitoring, updates and operational processes.