IoT · Connected devices · Analytics

Sensor hardware analytics platform

Sevendyne built a remote Node.js, Angular, and Spring Boot team for connected sensor programmes, then that team worked with client operators to design and ship temperature and device telemetry ingest, aggregates, and operator dashboards.

3-minute CTO brief

We staffed a multi-runtime IoT pod and placed them with client operators — standups on device health, PR reviews on ingest contracts, and pairing with client tech leads on threshold rules. Sensor hardware spits continuous temperature readings — the hard part is idempotent ingest, roll-up math, and dashboards that trust the data. The team kept Node for messy gateway payloads, Spring for calculations and persistence, and Angular for trends and threshold breaches.

Complexity: Telemetry volume · threshold rules · device health
Pattern: Edge ingest → JVM core → operator UI
Contrast: IoT pipeline vs German analytics BI stack

IoT telemetry · multi-runtime pipeline

Ingest → calculate → visualize

Layer	Role	Why separate
Node.js edge	Gateway REST/batch ingest	Fast iteration on device payload formats
Spring Boot	Roll-ups, thresholds, storage	Typed rules for temperature math and audit
Angular	Operator dashboards	Reusable charts per site and device

Reliability tactics

Idempotent ingest keyed by device_id + reading_ts
Gap detection when gateways miss heartbeats
Versioned alert rules — threshold changes don’t rewrite history

Production outcomes

End-to-end path from sensor hardware readings to operator dashboards.
Reusable pattern for IoT programmes mixing JVM services and JS front ends.
Complements deeper C++/Qt and Laravel portfolios.

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