AWS IoT TwinMaker FAQs

A digital twin is a living digital representation of an individual physical system that is dynamically updated with data to mimic the true structure, state, and behavior of the physical system in order to drive business outcomes.

Digital twins are built to improve business outcomes beyond existing modeling approaches by accelerating the understanding of systems and processes to improve operational decision making and drive more effective actions. Digital twins can be applied to a wide variety of use cases spanning the entire lifecycle of a system or asset such as buildings, factories, industrial equipment, and production lines. For industrial operations, digital twins can be used to optimize factory operations, increase production output, and improve equipment performance by connecting and combining Internet of Things (IoT) data with data from enterprise cameras and databases and presenting that data within 3D visualizations for easier understanding.

AWS IoT TwinMaker is a service that makes it faster and easier to create digital twins of real-world systems and apply them to improve operations. Creating digital twins is a complex process requiring developer expertise that spans operational technology (OT) systems, graph databases, IoT technologies, user interfaces, and 3D web development. To build digital twins, you have to manually connect different types of data from diverse sources (for example, time-series sensor data from equipment, video feeds from cameras, and maintenance records from business applications) and model them into a graph that represents the relationships between the data sources in your physical environment. You then have to build a 3D virtual representation of your physical systems (for example, buildings, factories, equipment, and production lines) and overlay the real-world data onto the 3D visualization. After that, developers have to create applications for end users such as plant operators and maintenance engineers to use their digital twins for everyday operations. AWS IoT TwinMaker makes this process easier by: providing a service that connects to different types of data from multiple sources; automatically creating and managing a digital twin graph that combines and understands the relationships among the connected data sources; and providing a simple web-based tool to create 3D visualizations that display data and insights to create the digital twin.

To get started, log in to the AWS Management Console and navigate to AWS IoT TwinMaker. To learn more, refer to the AWS IoT TwinMaker documentation. To explore service capabilities and sample digital twin applications, visit the AWS IoT TwinMaker GitHub repository.

First, create a workspace that will hold all of the resources (such as models and visual assets) you will need to create the digital twin.

Inside the workspace, create entities that represent digital replicas of your equipment (for example, a mixer or pump). Then associate entities with connectors to data stores such as AWS IoT SiteWise to bring data together from diverse data stores and add equipment context to the stored data. AWS IoT TwinMaker automatically creates a digital twin graph of your entities as you specify the relationships between them.

Next, using the AWS IoT TwinMaker console-based scene composer, import 3D models (such as CAD files and point cloud scans) to compose scenes, and position the 3D assets to correctly match and represent your physical environment and systems. You can add anchors using the scene composer to add data overlays that connect a specific 3D location with data streams or user actions for that entity.

Finally, create web-based digital twin applications using the AWS IoT TwinMaker plug-in for Amazon Managed Grafana to build dashboards that embed the 3D scenes and display data and insights about the operational state of your physical systems from the digital twin. These applications use the AWS IoT TwinMaker unified data access APIs to populate the data in the dashboards.

AWS IoT TwinMaker provides built-in connectors for different data stores, including AWS IoT SiteWise for time-series sensor data, Amazon Kinesis Video Streams for video data, and Amazon Simple Storage Service (S3) for document data. AWS IoT TwinMaker connects to AWS IoT SiteWise for asset model data, and to Amazon S3 for resources such as visual asset files and equipment documents. AWS IoT TwinMaker provides a plug-in for Grafana and Amazon Managed Grafana so you can create web-based digital twin applications for end users to monitor and optimize their operations.

To build digital twins, you start with a workspace that represents a single work site. A workspace holds all the resources (such as models and visual assets) needed to create the digital twin. Inside the workspace, you can create entities that represent digital replicas of their real-world systems. You can also specify custom relationships between the entities that will form a digital twin graph. You then connect to data from various data stores and add equipment context to the stored data. AWS IoT TwinMaker makes it simple for you to bring this data together without creating another data store and without requiring you to reenter the schema information that already exists in your data stores. To provide context to the data present in the data stores, you can associate entities with built-in connectors to these diverse data stores, such as time-series sensor data in AWS IoT SiteWise, video data in Amazon Kinesis Video Streams, or document data in Amazon S3,.

A digital twin graph is a knowledge graph that structures and organizes information about digital twins for easier access and understanding. As you create entities that represent digital replicas of your real-world systems, specify relationships between the entities, and connect these entities to different data sources, AWS IoT TwinMaker automatically creates a digital twin graph that organizes relationship information in a graph database.

AWS IoT TwinMaker supports built-in connectors to time-series data stores in AWS IoT SiteWise, video data stores in Amazon Kinesis Video Streams, and document data stores in Amazon S3. You can also author your own custom data connector to connect to data in AWS or third-party data stores.

You can author custom connectors to data stores using the AWS Lambda service. Using AWS Lambda, you can run the custom code and logic needed to connect to a data store, incorporating security credentials, custom queries, and the filtering and processing of raw data. You will create your AWS Lambda function with a standard interface defined by AWS IoT TwinMaker, which allows you to read from and write to the different data stores using only the AWS IoT TwinMaker unified data access API—without needing to query each data source with their own individual API.

First, you create asset models and assets in AWS IoT SiteWise that represent the source of the video data—for example, a camera. Then you configure and deploy the AWS IoT Greengrass component, Greengrass Edge Connector for KVS, which is used to connect to cameras and upload their video data to Amazon Kinesis Video Streams. Finally, you then associate the cameras and the video data with the entities that represent digital replicas of your real-world systems using AWS IoT TwinMaker. You can then integrate this video data into the 3D visualization of your physical systems and environment to display live video or playback of specific video segments from the cameras.

You will import previously built 3D models such as CAD and Building Information Modeling (BIM) files or point cloud scans (optimized for the web and converted to glTF format) into your resource library in Amazon S3. Using the AWS IoT TwinMaker scene composer, you can bring these visual assets into a scene, and position the 3D assets correctly to match your physical environment. AWS IoT TwinMaker also makes it easy for you to bind the data modeled in entities (for example, a pump or mixer) with your 3D visualization. In the scene composer, you can then add visual components to connect a specific 3D location with data streams or user actions for that entity. For example, a tag can be added to a mixer in the 3D scene that you then connect to the underlying time-series data that is reporting the current state of the mixer. Once this binding has been added, you can also configure rules (for example, temperature greater than 50 degrees) to change the visual representation of the mixer as well as the interaction when a user clicks on it (for example, updating the time-series chart to focus only on the selected mixer).

AWS IoT TwinMaker supports 3D assets in the glTF (Graphics Language Transmission Format) format, which is a 3D file format that stores 3D model information in a JSON format and enables efficient transmission and loading of 3D models in applications. The glTF format minimizes the size of 3D assets and the runtime processing needed to unpack and use them. The 3D models from traditional CAD applications, as well as point cloud scans, can be easily converted to glTF using AWS Partner solutions, such as those from Pixyz.

AWS IoT TwinMaker provides an application plug-in for Grafana as a low-code option to create end-user applications. The plug-in provides custom visualization panels, including a 3D scene viewer and dashboard templates, as well as a data-source component to connect to your digital twin data and quickly create interactive applications for end users. Grafana is a popular open-source analytics platform that enables you to query, visualize, alert on, and understand your metrics, regardless of where they are stored. The AWS IoT TwinMaker plug-in supports both customer-managed deployments of Grafana as well as Amazon Managed Grafana, a fully managed AWS service for open-source Grafana developed in collaboration with Grafana Labs.

You can work with the AWS Partner Network (APN) to help you harness AWS IoT TwinMaker capabilities and realize the potential of digital twins for your business. The APN includes partners that provide digital twin software solutions that are either hosted on or integrated with the AWS platform, as well as partners that can help you design, architect, migrate, or build new digital twin applications on AWS. We also have partners that provide software tools and systems that deliver data, simulation, and visualization services used in creating digital twins with AWS IoT TwinMaker. For more information, visit the Partners page.