Uladzislau Bayouski

Here's how it usually goes wrong. You build an IoT prototype. Fifty sensors, one reading a minute. You throw the data into PostgreSQL because that's what you know — and it works beautifully. Queries are instant. Everyone's happy. Then you go to production. Five thousand devices. One reading per second. Suddenly you're writing 5,000 rows every second — 432 million rows a day — and things start breaking in ways that feel personal. Dashboard queries that took 50ms now take 40 seconds. Your storage bill triples in a month. Adding an index makes writes worse. Removing it makes reads worse. Nothing is technically broken. You just picked the wrong tool, and IoT scale found the flaw. 📊 The Short Version IoT data has four properties that break traditional databases: Writes are relentless and append-only — high-frequency, continuous, never-ending; traditional B-tree indexes choke at sustained write rates Cardinality explodes at device scale — 10,000 devices × 5 se...

At some point in every IoT project, you have to pick a cloud platform. And once you're in, switching is painful. AWS, Azure, and Google are the three names that come up every time. They all connect devices. They all handle telemetry at scale. They all have dashboards, SDKs, and documentation that stretches to the horizon. So how do you actually choose? The Short Version Each platform has a distinct personality — and the right choice depends almost entirely on what you're already running and what you care about most. AWS IoT Core — the most flexible, the most powerful, and the most complex. A "bring your own architecture" experience with the largest portfolio of IoT services: Core, Greengrass, SiteWise, TwinMaker, FleetWise, Device Defender. If you want to compose exactly the system you need from low-level primitives, AWS lets you. The trade-off: no cohesive out-of-the-box workflow. You glue it together yourself. Azure IoT Hub — the enterprise integration champio...

Most IoT architectures look like this: devices collect data, the cloud processes it. Simple. Clean. And for a lot of use cases — completely fine. But as deployments scale and real-time decisions matter, that round trip to the cloud starts to hurt. That's where fog computing comes in. The Short Version Fog computing adds a processing layer between your devices and the cloud. Not instead of the cloud — between it and the edge. Think of it as a local coordinator: close enough to the devices to act fast, powerful enough to filter, aggregate, and pre-process before anything goes upstream. Why does it matter? Latency : decisions that need to happen in milliseconds can't wait for a cloud round trip Bandwidth : sending raw sensor data from thousands of devices is expensive — fog filters it first Reliability : local processing keeps working when the internet goes down Privacy : sensitive data can be handled locally, never leaving the site A factory floor, a smart hospital, a...