Announcing AWS IoT TwinMaker (Preview), a service that makes it easier to build digital twins
Today, we are announcing AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create and use digital twins of real-world systems to monitor and optimize operations. Digital twins are virtual representations of physical systems such as buildings, factories, production lines, and equipment that are regularly updated with real-world data to mimic the structure, state, and behavior of the systems they represent. Although digital twin use cases are many and diverse, most customers want to get started by easily using their existing data to get a deeper understanding of their operations.
Today, we are announcing AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create and use digital twins of real-world systems to monitor and optimize operations. Digital twins are virtual representations of physical systems such as buildings, factories, production lines, and equipment that are regularly updated with real-world data to mimic the structure, state, and behavior of the systems they represent. Although digital twin use cases are many and diverse, most customers want to get started by easily using their existing data to get a deeper understanding of their operations.
With AWS IoT TwinMaker, you can quickly get started with creating digital twins of equipment, processes, and facilities by connecting data from different data sources like equipment sensors, video feeds, and business applications, without having to move the data into a single repository. You can use built-in data connectors for the following AWS services: AWS IoT SiteWise for equipment and time-series sensor data; Amazon Kinesis Video Streams for video data; and Amazon Simple Storage Service (S3) for storage of visual resources (for example, CAD files) and data from business applications. AWS IoT TwinMaker also provides a framework for you to create your own data connectors to use with other data sources (such as Snowflake and Siemens MindSphere). AWS IoT TwinMaker forms a digital twin graph that combines and understands the relationships between virtual representations of your physical systems and connected data sources, so you can accurately model your real-world environment.
Once the digital twin graph is built, customers want to visualize the data in context of the physical environment. Using AWS IoT TwinMaker, you can import existing 3D models (such as CAD files, and point cloud scans) to compose and arrange 3D scenes of a physical space and its contents (e.g. a factory and its equipment) using simple 3D tools. To create a spatially aware visualization of your operations, you can then add interactive video and sensor data overlays from the connected data sources, insights from connected machine learning (ML) and simulation services, and equipment maintenance records and manuals.
To help developers create a web-based application for end users, AWS IoT TwinMaker comes with a plugin for Amazon Managed Grafana. End users, such as plant operators and maintenance engineers use Grafana applications to observe and interact with the digital twin to help them optimize factory operations, increase production output, and improve equipment performance. Amazon Managed Grafana is a fully managed service for the open source dashboard and visualization platform from Grafana Labs.
AWS IoT TwinMaker is available today in preview in US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Singapore), with availability in additional AWS Regions to come.
To learn more and get started, visit the AWS IoT TwinMaker product page. To find an AWS Partner that can help you harness the AWS IoT TwinMaker capabilities for your business visit the partner page.