Near Real-Time IoT Analytics with AWS and IBM CP4D
Publication date: May 12, 2023 (Diagram history)
This architecture demonstrates how to build near real-time IOT analytics with machine learning by using IBM Cloud Pak for DATA (CP4D) running on AWS.
Near Real-Time IoT Analytics with AWS and IBM CP4D Diagram

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Data from multiple sources across the manufacturing plant and edge devices is ingested from all the assets to AWS IoT Core.
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Amazon Data Firehose streams IoT data and loads it into Amazon Simple Storage Service (Amazon S3), a fully managed, highly available and scalable data lake storage service.
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Infrequently accessed data is moved to Amazon S3 Glacier for cost-effective archival.
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CP4D runs as a container workload running on OpenShift. Red Hat OpenShift Service on AWS (ROSA) is a fully managed OpenShift implementation on AWS.
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With IBM DataStage, you can create, edit, load, and run data transformation jobs to generate enriched and tailored information.
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You can use IBM Watson Knowledge Catalog to create a data governance framework, enrich data, and train machine learning (ML) models. You can create data protection rules for data access and mask sensitive information.
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Use Watson Studio to analyze data, and build and train machine learning models.
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Trained models are deployed to IBM Watson Machine Learning and are exposed as endpoints.
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Model outputs are stored in Amazon S3 output data buckets.
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The Edge device custom developed applications on Amazon Elastic Compute Cloud (Amazon EC2) with dashboards used by factory operators and supervisors to monitor the model predictions.
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Enterprise resource planning (ERP) applications get the data and put it into data stores using AWS Transfer Family.
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Contributors
Contributors to this reference architecture diagram include:
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Sankar Cherukuri, Partner Solution Architect
Diagram history
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Change | Description | Date |
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Initial publication | Reference architecture diagram first published. | May 12, 2023 |
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