Predictive Maintenance · Real-time Analytics
Vaillant needed to handle exploding IoT telemetry from connected HVAC devices because its Azure CosmosDB environment required constant manual scaling, suffered repeated crashes and slow queries, and blocked predictive maintenance and API-driven service innovation.
Reported impact
After moving to MongoDB Atlas, Vaillant eliminated downtime, cut database size by more than 99%, increased API throughput by 250%, and created a platform that supports remote troubleshooting, real-time analysis, and more efficient field service for connected HVAC systems.
Vaillant migrated its connected-HVAC platform from Azure CosmosDB to MongoDB Atlas on Microsoft Azure after explosive device growth made manual scaling, slow queries, and nightly outages routine. IoT telemetry from tens of thousands of internet-connected devices now lands in Atlas as a single, auditable operational store, where the aggregation framework and secondary indexes support real-time analytics for monitoring and predictive-maintenance workflows. Atlas's managed scaling removed write-limit headaches, and the migration itself took about 90 minutes after the team redesigned the schema. Vaillant also keeps some analytics at the edge to reduce network traffic, exposes device data through APIs for partners and engineers, and uses Atlas security features such as VNet peering-only mode to protect sensitive operational data. The platform now underpins services such as serviceASSIST and future guided-repair experiences for field technicians.
Sensor state changes from connected HVAC devices stream into MongoDB Atlas on Azure, where aggregation and indexing support real-time monitoring and predictive-maintenance analysis, while selected analytics stay at the edge to reduce network traffic and service data is exposed through APIs and technician applications.
"MongoDB Atlas’ VNet peering-only mode ensures that data is kept secure and access is limited."
"Today, we have over 1.9 billion records and there has been no downtime at all, and our customers immediately noticed the performance improvements."
"We had 70,000 devices generating 600,000 transactions per second."
"With MongoDB Atlas, powerful secondary indexing, and the aggregation framework, the team has decreased its database size by over 99% all the while increasing the throughput of its API by 250 percent."
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Scoring methodology
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Traceability
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