Accelerate Deployment of Azure Digital Twin Instances
Realize Governance and Maintenance of Models
Leverage and Reuse Existing Azure Digital Twin Models
1
Partner connectivity applications Ignition and KepServerEX running on AWS Certified industrial PC ingests real-time sensor data and asset model data sent from PLC and industrial historians.
2
PI Interfaces/Connectors collect data sent from sensors, PLCs, SCADA, etc. into PI Server running at plants or regional data centers. Element Unify AF Management Client retrieves, deploys and manages PI Asset Framework asset models.
3
Element Unify ingests asset models and tag lists exported from edge applications (i.e. Ignition, Kepware, etc.)
4
Element Unify P&ID Productivity Client automates harvesting of asset information from graphical engineering designs. EAM and ERP systems (residing either on-premises or in an Amazon VPC), including SAP FLOC and work orders, connect to Element Unify through iPaas layer.
5
Element Unify is the centralized IT/OT data management platform that integrates, contextualizes, and governs asset data models from OSIsoft PI Asset Framework (bi-directional), Ignition, Kepware running at the edge, and IT data sources like SAP and Maximo, and performs bidirectional model sync with AWS IoT SiteWise.
6
Element Unify reads existing asset model data from AWS IoT SiteWise and then provisions the new, enriched asset model data back into AWS IoT SiteWise.
7
Time series data flows into AWS IoT SiteWise from partner connectivity applications running at the edge. AWS IoT SiteWise stores the asset model data and ingests, filters, transforms and processes all incoming data before storing both raw and processed data in a managed time-series data store. AWS IoT SiteWise publishes an MQTT message to AWS IoT Core each time the asset property value updates.
8
An AWS IoT Core rule publishes these asset propery update messages it receives from AWS IoT SiteWise in near real-time into Amazon Kinesis Data Firehose.
9
Amazon Kinesis Data Firehose captures data from AWS IoT Core, transforms it, and delivers the data in near real-time to Amazon S3 industrial data lake.
10
Once real-time and historical data is available in Amazon S3 industrial data lake, Amazon Lookout for Equipment can use the data to detect abnormal equipment behavior, so that machine failures can be detected before failure occur and avoid unplanned downtime. Computed metrics can be written back into Amazon S3 for storage and consumption.
11
Amazon QuickSight can be used with Amazon Athena to create and publish interactive Bi dashboards.
Integrate and contextualize data from disparate IT/OT/ET sources using no-code pipelines eliminating manual effort and greatly speeding time to deployment.
Capture data relationships in a semantic data model realizing flexibility and ability to keep model in sync with underlying assets.
Unify has a range of pre-built connectors (e.g for Azure Blob Storage, Azure Data Lake Storage, JDBC Connector for Azure, etc.) that make it simple to ingest data or publish models within an Azure cloud context.
Publish your models directly to Azure Digital Twins and consume exported models across other Microsoft apps and services.
An Open & Flexible Industrial DataOps Solution
Bring all of your industrial operations data together in one place
Pre-built, no-code connectors automate connectivity and data flows into Unify
Pick the right package
for your industrial transformation journey
Request a demo of Element Unify from one of our experts
We’d love to show you how KaYan works and answer any questions you may have.
Unify is an Open & Flexible Industrial DataOps Solution
Quickly build and deploy Digital Twins on AWS
Realize more value from your operational data on AWS
Accelerate the deployment of Azure Digital Twin instances
Accelerating your team’s project success with Unify
Digital transformation for Chemical plant operators
Power industrial transformation across upstream, midstream, and downstream
Use data to cut costs and achieve growth towards sustainability
We’d love to show you how KaYan works and answer any questions you may have.
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We’d love to show you how KaYan works and answer any questions you may have.
The integration between Microsoft Azure and Element Unify speeds up the process of building digital twins and enhances their effectiveness by simplifying the integration of data from both enterprise applications and operational systems (OT). As a result, customers can gain insights and value much faster, and the integration enables greater scalability.
Accelerate Deployment of Azure Digital Twin Instances
Realize Governance and Maintenance of Models
Leverage and Reuse Existing Azure Digital Twin Models
1
Partner connectivity applications Ignition and KepServerEX running on AWS Certified industrial PC ingests real-time sensor data and asset model data sent from PLC and industrial historians.
2
PI Interfaces/Connectors collect data sent from sensors, PLCs, SCADA, etc. into PI Server running at plants or regional data centers. Element Unify AF Management Client retrieves, deploys and manages PI Asset Framework asset models.
3
Element Unify ingests asset models and tag lists exported from edge applications (i.e. Ignition, Kepware, etc.)
4
Element Unify P&ID Productivity Client automates harvesting of asset information from graphical engineering designs. EAM and ERP systems (residing either on-premises or in an Amazon VPC), including SAP FLOC and work orders, connect to Element Unify through iPaas layer.
5
Element Unify is the centralized IT/OT data management platform that integrates, contextualizes, and governs asset data models from OSIsoft PI Asset Framework (bi-directional), Ignition, Kepware running at the edge, and IT data sources like SAP and Maximo, and performs bidirectional model sync with AWS IoT SiteWise.
6
Element Unify reads existing asset model data from AWS IoT SiteWise and then provisions the new, enriched asset model data back into AWS IoT SiteWise.
7
Time series data flows into AWS IoT SiteWise from partner connectivity applications running at the edge. AWS IoT SiteWise stores the asset model data and ingests, filters, transforms and processes all incoming data before storing both raw and processed data in a managed time-series data store. AWS IoT SiteWise publishes an MQTT message to AWS IoT Core each time the asset property value updates.
8
An AWS IoT Core rule publishes these asset propery update messages it receives from AWS IoT SiteWise in near real-time into Amazon Kinesis Data Firehose.
9
Amazon Kinesis Data Firehose captures data from AWS IoT Core, transforms it, and delivers the data in near real-time to Amazon S3 industrial data lake.
10
Once real-time and historical data is available in Amazon S3 industrial data lake, Amazon Lookout for Equipment can use the data to detect abnormal equipment behavior, so that machine failures can be detected before failure occur and avoid unplanned downtime. Computed metrics can be written back into Amazon S3 for storage and consumption.
11
Amazon QuickSight can be used with Amazon Athena to create and publish interactive Bi dashboards.
Integrate and contextualize data from disparate IT/OT/ET sources using no-code pipelines eliminating manual effort and greatly speeding time to deployment.
Capture data relationships in a semantic data model realizing flexibility and ability to keep model in sync with underlying assets.
Unify has a range of pre-built connectors (e.g for Azure Blob Storage, Azure Data Lake Storage, JDBC Connector for Azure, etc.) that make it simple to ingest data or publish models within an Azure cloud context.
Publish your models directly to Azure Digital Twins and consume exported models across other Microsoft apps and services.