AWS IIoT Manufacturing Cloud Build

I want to stand up a full-stack IIoT environment on AWS tailored for a manufacturing plant. The platform must ingest machine performance data from shop-floor sensors, stream it securely into AWS, and expose three core capabilities: real-time monitoring, predictive maintenance, and rich analytics with clear reporting dashboards. The flow I have in mind uses AWS IoT Core or Greengrass at the edge to collect OPC-UA/MQTT payloads, routes them through Kinesis or Kafka (open to your suggestion), lands the raw data in S3, and writes curated time-series to DynamoDB or Timestream for fast queries. From there I need: • Live dashboards (Grafana, QuickSight, or a similar tool) that show cycle times, throughput, and alert me the moment a metric drifts outside tolerance. • A predictive maintenance pipeline—SageMaker or an equivalent managed service is fine—training on historical failures, then pushing inference back to the edge so operators get early warnings. • An analytics layer with pre-built reports I can slice by line, shift, and machine so managers don’t have to export raw CSVs. Security (IAM, VPC design, TLS) and cost-efficient architecture are non-negotiable. Everything must be deployed via Infrastructure as Code—CloudFormation or Terraform—so we can recreate environments in staging and production. Please outline: 1. Your proposed AWS services and data flow. 2. How you will implement real-time monitoring, predictive models, and reporting. 3. The deliverables, including IaC templates, source code, and a brief hand-off document. If you have completed similar manufacturing IIoT builds on AWS, I would love to see a short demo or repo link with any confidential info stripped out.

Project ID: 40442461