Industrial DataOps on AWS using Cognite Data Fusion
Publication date: November 2024 (Diagram history)
With this architecture, you can use AWS Cloud and Cognite
Data Fusion
Industrial DataOps architecture diagram
The following steps describe the architecture:
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Purpose-built extractors ingest industrial data sources such as IT, OT, and engineering data. You can also use AWS IoT Greengrass (an open source IoT edge runtime) to bring data from edge gateways or programmable logic controllers (PLCs) to AWS Cloud.
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Data from industrial historians and other devices flows through AWS IoT Greengrass into AWS IoT SiteWise. Cognite Native Extractors ingest pre-aggregated industrial data from a customer data lake in Amazon S3, Amazon Redshift, and Amazon DynamoDB.
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Use Lambda and Amazon API Gateway together to ingest data from CDF. Transform data by using AWS Glue and write data back into CDF through the Cognite SDK. Amazon Managed Service for Apache Flink and Amazon SageMaker AI provide additional analytics and machine learning (ML) capabilities.
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End users create custom applications depending on data type by using AWS IoT TwinMaker and Amazon Managed Grafana.
Further reading
For additional information, see the following resources:
Diagram history
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| Change | Description | Date |
|---|---|---|
Initial publication | Reference architecture diagram first published. | November 1, 2024 |
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