Extract, Transform, and Load (ETL)

Extract, transform, and load (ETL) is a research line in the Laboratory of Digital Models in Structures and Construction, aiming to understand better the integration of diverse data sources into a structured and centralized repository. Given the need to exchange information across different platforms, formats, and disciplines, ETL plays a crucial role in standardizing, cleansing, and organizing raw data to ensure consistency and interoperability.

In our context, ETL not only facilitates the preparation of data for storage, simulation, and analysis but also serves as a key enabler for further transformations. By structuring information from various sensors, digital models, and experimental results, ETL allows for seamless integration with computational tools, ontological frameworks, and digital twins. This ensures that data can be efficiently used for structural assessments, predictive analytics, and decision-support systems.

Furthermore, ETL is particularly interesting in the transition between different modeling paradigms. Whether transforming physical observations into parametric models, aligning heterogeneous datasets for multi-scale simulations, or enabling real-time monitoring through connected infrastructures, ETL ecosystems aree vast.