IoT data pipeline: LavinMQ to Prometheus to Grafana

Description

We’ll walk through how to build a scalable IoT monitoring pipeline using LavinMQ, Prometheus, and Grafana. You’ll learn how sensor data flows from devices into a message broker, then into a time-series database, and finally to live dashboards. Why this pipeline? When your IoT system must support thousands of sensors and bursty traffic, you need: * A message broker to decouple devices from backend processing—LavinMQ plays that role. * A time-series database/monitoring system to store and query metrics—Prometheus handles this. * A visualization layer to make sense of the data in real time—Grafana brings the dashboards to life. We have three main layers: Devices (Office) – Sensors (e.g., temperature & humidity) publish JSON over MQTT to LavinMQ. Message broker (LavinMQ) – LavinMQ receives the MQTT messages and acts as the buffer. Monitoring stack (Docker Compose) – A tiny bridge service (mqtt2prometheus) subscribes to LavinMQ, converts JSON → Prometheus format, then Prometheus scrapes it, and Grafana queries Prometheus to show live dashboards. Blog guide can be found here: https://lavinmq.com/blog/iot-data-pipeline-lavinmq-to-prometheus-to-grafana Call to Action Want to build this yourself? Check out the GitHub repo (link in description) for the Docker Compose file, bridge code, and step-by-step instructions. If you have questions or want to discuss deployment scenarios — join our community (Slack link in blog) or drop us a message. Thanks for watching — if you found this useful, please like, subscribe, and share! Let’s make IoT monitoring simple, scalable and visual.