Case Study: Global Rail Transportation Technologies & Infrastructure Provider achieves scalable, preventive, real‑time monitoring of 2,500 railway crossings with Altoros

Scalable, Preventive, Real-Time Monitoring of Railway Crossings

A global provider of technologies, infrastructure, and vehicles for rail transportation turned to Altoros to replace a legacy monolithic monitoring system that could not scale, was hard to extend, and could not reliably deliver real‑time accident notifications while ingesting data from thousands of devices. The company sought flexibility to sustain petabytes of daily IoT data from multiple edge devices and to deliver critical alerts in real time.

Altoros split the monolith into microservices, containerized the system with Docker and Kubernetes, integrated HiveMQ (with MQTT) for messaging, used HDFS for historical storage and Apache Kafka for secure data transfer, and prototyped video analytics with TensorFlow; legacy sensors were also standardized via MQTT. As a result, Altoros delivered a scalable monitoring platform now running at ~2,500 railway crossings with nearly 5,000 edge devices, processing petabytes per day at megabytes‑per‑second throughput, enabling real‑time notifications and cutting operational deployment effort by about 10–15×.