Data Architectures and their Technical Requirements (DATER)
For researchers and practitioners, this framework provides a systematic way to navigate and compare data architectures, but the contribution is incremental as it synthesizes existing concepts without empirical validation.
The paper introduces DATER, a conceptual framework for describing and evaluating data architectures based on technical requirements, and analyzes six modern architectures (data warehouse, data lake, data lakehouse, data fabric, data mesh) to clarify their strengths, limitations, and use-case suitability.
Modern organizations generate and consume massive volumes of heterogeneous data at high speed. This requires a continuous development of new techniques for more efficient and reliable data management. Designing appropriate data architectures has therefore become a strategic necessity, as they shape how data is integrated, governed, and made available for analytics and decisionmaking. This paper introduces a conceptual framework - Data Architectures and their Technical Requirements (DATER) - to systematically describe and evaluate data architectures based on technical requirements. Six modern architectures are examined: data warehouse, (semantic) data lake, data lakehouse, data fabric, and data mesh. Each is analyzed by historical context, defining features, and conformance to DATER dimensions. The study supports researchers and practitioners in navigating architectural paradigms, clarifying overlaps, and highlighting strengths, limitations, and use-case suitability.