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Empowering
Empowering
real-time
fitness
analytics
01
The challenge
The limitations of on-premise systems
As Orangetheory expanded to over 1,500 studios globally, legacy infrastructure created processing bottlenecks, forcing the company to rely on static approximations rather than true telemetry.
·Compute constraints — on-premise servers could not absorb wearable telemetry at peak global class times
·Data silos — fragmented databases blocked centralized reporting and data science workloads
·Scalability roadblocks — provisioning hardware for franchise expansion was slow and costly
02
The tech stack
A data-driven AWS architecture
Beyrsol engineered a seamless migration to Amazon Web Services, focused on high-throughput data processing, advanced analytics readiness and modernized user interfaces.
·Scalable data pipelines for high-volume wearable telemetry
·Centralized data lake optimized for Python-driven machine learning and predictive health analytics
·Decoupled monolith with Next.js dashboards for studio managers to view live metrics
03
The impact
Peak performance and precision
The platform now ingests and analyzes 100,000 telemetry data points per second with near-zero latency, while automated cloud management lowered maintenance costs and accelerated the rollout of digital features.
·Real-time processing at 100,000 data points per second
·Heart-rate profiles generated for members after just five sessions
·A secure foundation for the launch of Orangetheory Live
04
Conclusion
Redefining real-time analytics
By replacing fragmented on-premise servers with an elastic AWS foundation, Beyrsol equipped Orangetheory for continuous digital scaling.
·Eliminating silos — one data lake for global franchise reporting and BI
·Elastic scalability — compute scales with daily global class schedules
·Enhanced experience — real-time biometric feedback for members worldwide
100K/s
Data points per second
Zero
Processing latency
5
Sessions to profile
100%
Automated IaC