Leveraging Eclipse Sparkplug to Accelerate DataOps and Machine Modeling into AWS Services
summary
Demonstrates using MQTT plus Eclipse Sparkplug templates to auto-create machine models and assets at the edge, then populate AWS time-series via AWS IoT SiteWise; uses protobuf payloads, store-and-forward and a middleware called IoT Bridge that auto-instantiates models in SiteWise and streams process variables into the cloud. Claims reduce manual tag-mapping, enables low/no-code brownfield onboarding, and cites industry gains (Volkswagen ~30% efficiency). Notes AWS IoT Core lacks MQTT retain/death features so options are using an EC2 broker or relying on Sparkplug client store-and-forward.
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chapters (21)
- 0:00Introduction
- 1:10Welcome
- 3:15Digital Transformation
- 5:35Leveraging Technology
- 6:55IoT vs OT
- 8:23DataOps
- 10:29MQTT
- 13:29Otcentric Topic Name Space
- 15:03What does this mean
- 18:00Sitewise
- 20:37AWS IoT Core
- 23:12DataDriven Insights
- 31:15How to reconcile models in Sparkplug with digital twins
- 31:37How to build a model in Sparkplug
- 33:25Why construct a digital twin
- 35:24How to extend a model
- 37:18IOT Core
- 39:45Sparkplug B
- 42:11MQTT vs OPC UA
- 43:55OPC UA and Sparkplug
- 46:17Sparkplug in Automotive
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