Building a digital twin with Fabric
Like most Fabric applications, the digital twin builder needs to have all its data stored in a lakehouse, with Fabric tools defining the ontology that maps data to the real-world systems and the processes your digital twin will model. Having experts on hand is important as you build the vocabulary that Fabric uses to pull together many different data sources and streams. This mapping process is key to defining the essential entities your model uses, bringing them together with queries to define semantic relationships.
Entities are specific machines, processes, inputs, and outputs, as well as the people and systems that sit around them. These are linked to the data in Fabric associated with them in a semantic hierarchy that lets you wrap different entities together.
Once you have built this map of your data, you’re able to use other Fabric tools to explore and analyze the data, linking it to the systems and the processes you’re modeling. This can be extended further, out into AI tools for more insights, for example, finding outliers in your data or providing visualizations. APIs let you use that data with code, linking it to digital twins of your control systems and models of physical hardware. Visualizations can be built in familiar tools, such as Power BI, or data can be delivered.
