VR-ETL: Leveraging virtual reality for pre-transformation ETL data validation

Abstract

Data validation in ETL workflows is most often performed after transformation, even though many data quality issues originate earlier in the pipeline. In practice, this means that errors are detected late, when tracing their source and applying corrections already requires additional effort. In this work, we explore whether virtual reality (VR) could be used as an alternative interface for addressing this problem earlier, at the stage of raw data inspection. We present VR-ETL, a proof-of-concept system that allows users to inspect and correct tabular data before transformation by interacting with it inside an immersive 3D environment. The system imports CSV datasets and applies a set of rule-based validation checks targeting common data quality problems, such as missing values, format inconsistencies, and basic referential integrity violations. Detected issues are visualized directly within the 3D representation of the data, using color-coded cells and simple spatial summaries that reflect error distribution across columns and records. A functional prototype was implemented using Unity and OpenXR and tested on structured datasets containing up to 10,000 records. During controlled technical testing, the system supported automated validation, real-time visual feedback, and immediate re-validation after user corrections, while maintaining performance suitable for immersive use on consumer-grade hardware. All intentionally seeded rule-based errors were detected. Although usability and workflow efficiency were not evaluated in this study, the prototype demonstrates that pre-transformation data validation in VR is technically feasible and provides a concrete starting point for future empirical investigations.