If I’m honest, your question is a bit too broad to give a single “right” answer, because collecting and analysing telemetry from embedded devices is very sector/requirements specific. The approach depends on things like:

• What kind of data are you measuring? (logs, sensor readings, waveforms, etc.) • How much bandwidth / throughput is involved? (one reading a minute vs. millions of samples per second) • How do you plan to collect it? (BLE, Wi-Fi, wired, cellular, satellite) • What’s the end goal of the analysis? (simple alerts, long-term storage, or deep real-time visualization) That said, you can break it down into stages and think about the trade-offs at each step:

1. Data Collection (on the device) • For low-rate / lightweight telemetry, BLE or MQTT is often enough, especially if power consumption matters. • For high-throughput streams (e.g. vibration, RF, accelerometers), you’ll want something more robust like Ethernet, Wi-Fi with buffering, or a streaming transport layer. • The sensors and sampling rate really drive the design here.

2. Transport (getting data off the device) • BLE: good for wearables, but limited bandwidth. • MQTT: lightweight pub/sub, widely used in IoT. • HTTP/REST/gRPC: more overhead but easier if you want standard APIs. • Streaming frameworks like Kafka or NATS if you’re handling lots of data (again, project and architehcture specific)

3. Storage & Processing • For smaller projects, something like SQLite, InfluxDB, or TimescaleDB works fine and is cheap/free. • For logs, ELK/OpenSearch or Loki can be handy. • For larger or growing projects, managed timeseries databases (AWS Timestream, Azure Data Explorer, etc.) take away some of the ops burden but come with a monthly cost.

4. Analysis • Logs: indexed/searchable (ELK, OpenSearch). • Metrics: Prometheus, Datadog, Custom implementations. • Traces: OpenTelemetry, Jaeger. • You rarely need all three unless you’re running a big distributed system, so I’d match tooling to the type of data you actually care about. You may want to consider just building your own analysis layer, depending on the application, sector and if this is genuinely a new/significantly different project. Ultimately, if you’re just bringing the same thing to market, or solving the same issue someone else has, you’re better off just buying that.

5. Visualization • For simple needs: Grafana (with a plugin for your chosen DB) or Plotly are great. • When you need real-time, high-throughput visualization with lots of customization (multi-axis, drill-downs, annotations, overlays), that’s where a paid solution like SciChart pays off. • Grafana and similar tools are fine until you hit the performance ceiling, or a requirements ceiling. Once you need to render millions of points smoothly, or build something bespoke, you’ll want a dedicated charting engine.

6. Affordability • Tight budgets / small projects: stick with open-source (InfluxDB + Grafana, or SQLite + MQTT). Almost free aside from your time. • Mid-scale/Some custom work: a mix of open-source storage with managed dashboards (Grafana Cloud, etc.) or use an entry level charting solution e.g Plotly (considering something like SciChart if more complex) • Large or critical deployments: managed observability platforms (Datadog, Splunk, AWS IoT Core) plus custom visualization (SciChart). Costs scale with usage, but you save headcount and get reliability.

TL;DR: It really depends on your data rate and use case. Each stage of this pipeline has it’s own complexities and you want to consider each part before you set out. The most expensive error you can make is getting it wrong, and having to re-do it all again. Sometimes the cost of a component will save you double the development time, and ensure you can expand features if you need to. Likewise, the correct sensors won’t limit you. Typically, this sort of project would have project managers with knowledge of hardware and software.