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Neo4j’s Causal Clustering architecture helped the Telia team to horizontally scale their operations to help consumers simplify their lives and help consumers better entertain within their homes.



The Telia team recognized their roughly 1 million installed Wi-Fi routers as underutilised assets. Their goal was twofold: help consumers simplify their lives and help consumers better entertain within their homes.

The solution: The Telia Zone. After tracking all Wi-Fi connected devices within their customers’ homes, the Telia team provides this connected dataset and a number of APIs to a partner-driven ecosystem of apps and services.

The result is a smart home platform that allows consumers to pick and choose apps that meet the two goals of simplification and home entertainment.Telia Zone apps do everything from reminding consumers when they forget to lock the door after they leave for the day to let them know when their kids arrive home after school (and turning on the lights when they get there).

Powering it all on the backend is a graph database, which can be deployed on-premise or in the cloud. The Telia team chose graph technology because their smart home dataset was already a graph of connected data.

Also, with Neo4j’s Causal Clustering architecture, the Telia team was able to horizontally scale their operations without always knowing what given traffic loads might be. Furthermore, the graph data model allows them the flexibility to keep evolving the Telia Zone platform without breaking existing components.

A connected world strewn with brilliant smart cities and networks is on the horizon — and we have found the right Neo4j data engine to manage it at last.


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