Case Study: Glowbl unifies members' social networks and delivers real-time, scalable recommendations with Neo4j

To bring together all the social networks of its members, Glowbl transforms their data into graphs using Neo4j

Glowbl is a Lyon-based start-up (offices in Paris and San Francisco) that provides a connected video-collaboration platform allowing users to join LiveStages via social networks or a shared URL. Facing rapid growth (≈60,000 users) it needed to aggregate contacts from multiple social networks, represent complex relationships as graphs and manage those connections and interactions in real time — something its existing SQL-based model struggled to do.

Glowbl adopted Neo4j as its graph database, implementing a real-time spatial graph (5–10 weeks) and a social/behavioral graph (3 weeks) to enable live targeting, messaging and user recommendations. The switch delivered fast, scalable real-time updates, simplified deployment and long-term stability (no downtime reported over three years), allowing Glowbl to handle massive, fluctuating data volumes and accelerate product development and growth.

Glowbl

Mathieu Labey

CEO and Founder