Manufacturing IoT generates massive volumes of sensor data — temperature readings, vibration measurements, pressure values, production counts — every second of every day. The challenge isn't collecting this data. It's making it useful in real time.
In this walkthrough, we'll build a production-grade real-time data pipeline on AWS that ingests sensor data from factory floor devices, transforms it on the fly, and surfaces it for operational dashboards and ML-powered anomaly detection.
The architecture uses Amazon Kinesis Data Streams as the ingestion layer, AWS Lambda for real-time transformation and enrichment, Amazon Kinesis Data Firehose for delivery to S3 (our data lake), and Amazon Managed Grafana for operational dashboards.
Why Kinesis over Kafka? For mid-market manufacturers processing tens of thousands of events per second (not millions), Kinesis offers the right balance of capability, operational simplicity, and cost. You don't need a dedicated Kafka operations team.
The ingestion layer starts with your IoT devices publishing to AWS IoT Core via MQTT. IoT Core rules route messages to a Kinesis Data Stream partitioned by device ID. This ensures ordered processing per device while allowing parallel processing across devices.
The transformation layer uses Lambda consumers attached to the Kinesis stream. Each Lambda function enriches the raw sensor data with device metadata (location, type, calibration date), applies basic validation rules, and computes rolling aggregates (5-minute averages, standard deviations).
For the data lake landing zone, Kinesis Data Firehose delivers the transformed data to S3 in Parquet format, partitioned by date and device type. This gives you efficient query performance with Athena and a foundation for ML model training.
The operational dashboard layer uses Amazon Managed Grafana connected to Amazon Timestream for real-time metrics and Athena for historical analysis. Alerts are configured for threshold breaches and anomaly detection.
Total AWS cost for this architecture at 10,000 events per second: approximately $800-1,200/month. That's a fraction of what a traditional on-premises data historian would cost, with significantly more analytical capability.