One of the critical aspects of Digital Twin is to flow data continuously between real and virtual models. DarkStax D2D subcomponents provide enterprise features to build real-time data processing pipelines with DataOps features. It also creates digital twins from streaming data at the edge.
D2D pipelines have extensible and flexible interfaces to High Performance Computing, On-Premise, and Cloud computing infrastructures. The Data pipelines are deployed on distributed computation software to reduce and analyze voluminous instrumentation data collected in structured, semi-structured, or unstructured formats. It includes a visualization web server that can interface with any given real-time data feeds, persistent databases, and flat file formats. D2D data pipelines are more versatile and can be employed for more use cases because they can continuously consume and emit data.
D2D’s visualization web server includes an interactive browser-based notebook. The notebooks enable data engineers, analysts, and scientists to be more productive by developing, organizing, executing, and sharing data code and visualizing results without referring to the command line or needing the cluster details. The notebook allows these users to run visual queries and work with long workflows interactively. Such web-based notebooks bring data exploration, visualization, sharing, and collaboration features to multiple sources of database/storage technologies. The advanced visualization library supports the multi-variant Digital Twin Simulation dataset analysis. It can display real-time data feeds as well as persistent data storage. The Dashboard Development Kit (DDK) allows analysts to develop dynamic dashboards with various visualization options.
The dashboards are updated by continuous queries on data-in-motion and/or data-at-rest. It includes a geospatial visualization package that supports time-dynamic 3D scenes such as satellites and aircraft. The visualization library’s core purpose is rapidly developing a dashboard for the analysts and operators. The dashboards provide data discovery, exploration, reporting, and visualization of the analysis or evaluation workflow.
If the system is complex and has multiple variables and multiple streams of data, then our solution recommends using traditional AI/ML tools and processes to develop models. We also leverage the MLOps process, enabling data scientists to deploy models representing behaviors for targeted digital twin representation quickly. In the process, data scientists train, reuse, and deploy models with any library and package them into reproducible steps. It is designed to work with any machine learning library and requires minimal changes to integrate into the existing codebase.