domain-iot:iot-cloud-integration
Cloud IoT platform integration covering AWS IoT Core, Azure IoT Hub, and Google Cloud IoT. Includes device shadow/twin patterns, rules engines, edge runtimes, data pipelines from ingestion to analytics, and multi-cloud considerations.
SKILL.md
Full skill instructions
IoT Cloud Integration
When to use
- Integrating devices with AWS IoT Core, Azure IoT Hub, or GCP after the Cloud IoT Core deprecation
- Configuring rules engines to route device messages to downstream services
- Designing hot/warm/cold data pipeline architectures for telemetry ingestion
- Setting up Device Provisioning Service (DPS) for zero-touch at scale
- Evaluating multi-cloud trade-offs or avoiding vendor lock-in
- Choosing between Timestream, Azure Data Explorer, BigQuery, and Bigtable for IoT storage
Core principles
- Decouple ingestion from processing — message broker or queue between device gateway and any processor; never direct device-to-database writes
- Schema validation at the ingestion boundary — malformed payloads caught at the door, not discovered three hops later in a dashboard
- Branch early for latency tiers — hot path (real-time alerts), warm path (dashboards), cold path (archive) forked at the rules engine, not bolted on later
- Idempotent consumers everywhere — IoT protocols guarantee at-least-once; your processors must handle duplicates gracefully
- Open formats in storage — Parquet/Avro in object storage makes the next cloud migration a query problem, not a rewrite
Reference Files
references/aws-iot-core.md— Device Gateway, Message Broker, Device Shadow, Rules Engine SQL, IoT Greengrass edge runtime, AWS data pipeline patternreferences/azure-iot-hub.md— D2C/C2D messaging, Device Twins, DPS allocation policies, IoT Edge modules, message routing config, Azure data pipeline patternreferences/gcp-and-pipeline-design.md— GCP post-deprecation stack (Pub/Sub, Dataflow, BigQuery, Bigtable), generic platform-agnostic pipeline pattern, multi-cloud considerations