Dual-Path IoT Processing Patterns

Amazon Web Services published a reference architecture that combines NB-IoT and LoRaWAN ingestion with a dual-path telemetry pipeline for real-time streaming and batch analytics. The design routes device data through AWS IoT Core into Kinesis for immediate processing and Firehose and Amazon S3 for historical analysis, with Apache Flink supporting stateful anomaly detection.

NB-IoT traffic arrives through cellular partners using lightweight protocols, while LoRaWAN devices forward base64-encoded payloads that Lambda functions convert into JSON. The architecture also uses AWS Glue for ETL and cataloguing, Amazon Timestream for InfluxDB 3 for low-latency time-series queries, and Amazon Bedrock AgentCore with Titan embeddings to translate natural-language requests into Athena SQL.

For operators, the model provides a shared ingestion layer for immediate alerts and long-term maintenance insights. However, device-specific decoders, the lack of a native Flink sink for InfluxDB and dependence on accurate Glue metadata highlight the integration work required for production deployments.

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