Posted On: Nov 17, 2022

AWS IoT TwinMaker makes it easier to create digital twins of real-world systems such as buildings, factories, industrial equipment, and production lines. Now with the feature launch of TwinMaker Knowledge Graph, customers can query their digital twins, contextualize data from disparate data sources, and gain deeper insights into their real-world systems. As a result, customers can save time performing functions like root cause analysis and drive more informed business decisions. 

To build a TwinMaker Knowledge Graph, customers create entities which are virtual representations of real-world systems, and then define the physical or logical relationships between those entities. Customers can then query the TwinMaker Knowledge Graph with open source query language partiQL. For example, customers can query all entities with name containing “pump”, or find all entities connected to an entity of interest. The query capability enables customers to perform functions like root cause analysis and predict the impacted entities and systems when changes are introduced into their physical systems. This can improve operational efficiency and reduces time to resolve issues. With data contextualized from disparate data sources, customers can drive informed decisions and anticipate where issues are likely to occur in the future.  To learn more, visit our developer guide and API reference.

TwinMaker Knowledge Graph is generally available, and you can use it in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), and Europe (Ireland).

For pricing information, please visit AWS IoT TwinMaker pricing page. To learn more visit the AWS IoT TwinMaker product page. Use the AWS Management Console to get started, or visit our GitHub repository for a sample digital twin application.