domain-iot:iot-cloud-integration

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

1. Decouple ingestion from processing — message broker or queue between device gateway and any processor; never direct device-to-database writes
2. Schema validation at the ingestion boundary — malformed payloads caught at the door, not discovered three hops later in a dashboard
3. 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
4. Idempotent consumers everywhere — IoT protocols guarantee at-least-once; your processors must handle duplicates gracefully
5. 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 pattern
• references/azure-iot-hub.md — D2C/C2D messaging, Device Twins, DPS allocation policies, IoT Edge modules, message routing config, Azure data pipeline pattern
• references/gcp-and-pipeline-design.md — GCP post-deprecation stack (Pub/Sub, Dataflow, BigQuery, Bigtable), generic platform-agnostic pipeline pattern, multi-cloud considerations