A Digital Twin can contribute to the product development process by merging data from the physical assets and the digital model, and generally assist in an orchestrating role. [1] For a Digital Twin framework to support any type of product development, an effective but generic data handling is a core task. Two essential data generation processes in product development are measurements and simulations. Typically, the simulations and measurements are not limited to a single physical domain and multiphysics simulations are a common necessity. In conclusion, a Digital Twin framework for product development needs to be able to handle big data collections, with measurement data and simulation data from different physical domains being an essential core part. This research is part of the open source framework OpenTwin [3], which is a platform for product related Digital Twins. Currently, this research focuses on the analysis of requirements associated with electromagnetic interference (EMI) studies, an essential part of modern product development of electronic components. However, the derived data model is designed to be generic, so that it can describe general measured and simulated data, even from different physical domains. The characteristics of the big data collections favour the use of a NoSQL database, and the typical data access pattern favours a document-based NoSQL database. Various generic data models and metadata models have been proposed [2], but a document-based model suitable for both simulated and measured data was still missing. In addition to proposing the generic conceptual document-based data model, performance studies were carried out on the physical data model. The studies aimed to find an optimal schema for storing potentially multidimensional data, including s-parameters and data tuples, such as complex numbers, in order to optimise memory usage and query performance. [1] F. Tao, et al., “Digital Twin in Industry: State-of-the-Art,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2019, [2] R. Eichler, et al., “HANDLE - A Generic Metadata Model for Data Lakes,” in Big Data Analytics and Knowledge Discovery, Springer International Publishing, 2020, pp. 73–88. [3] “Multi-Domain Simulations with Open Source”. Open Twin. https://opentwin.net/ (accessed Oct. 17, 2024)