Why this combination keeps winning for IoT telemetry backends — not in a benchmark, but against flaky gateways, replayed data, and firmware that never quite agrees with itself.
If you’ve ever built the backend for a fleet of IoT devices — sensors, gateways, industrial equipment reporting temperature, humidity, GPS, battery, signal strength — you’ve faced the same fork in the road early on: what do you build the ingestion layer with, and what do you store the data in?
After building a telemetry backend from scratch for a real fleet of LoRa/BLE sensors and gateways — handling dual ingestion paths, binary and JSON payload formats, automatic recovery of lost data, and a growing set of operational dashboards — I keep coming back to the same combination: Python (FastAPI + asyncio) for the API, MQTT for device transport, and PostgreSQL for storage. Here’s why that combination holds up so well for this specific problem, not just “in general.”
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