Data Architectures and their Technical Requirements (DATER)

A conceptual framework for systematically describing and evaluating modern data architectures against technical requirements

Sayed Hoseini; Christoph Quix; Stefan Decker· 2026· DOI 10.48550/arXiv.2606.08811

The core problem

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 decision-making. The 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.

The paper examines six modern data architectures: 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 results are presented as a comparative evaluation that clarifies overlaps among architectures and highlights their strengths, limitations, and use-case suitability. The DATER framework enables a structured comparison, but the abstract does not provide specific quantitative results or rankings. The key outcome is a conceptual mapping that helps stakeholders understand which architecture fits which technical requirements.

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 decision-making. The 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.

The authors propose DATER as a conceptual framework that maps data architectures to technical requirements. The framework is applied to six architectures: data warehouse, (semantic) data lake, data lakehouse, data fabric, and data mesh. For each architecture, the analysis covers historical context, defining features, and conformance to DATER dimensions. The methodology is qualitative and comparative, aiming to clarify overlaps and highlight strengths, limitations, and use-case suitability. The DATER dimensions are not explicitly enumerated in the abstract, but the framework is intended to systematically describe and evaluate architectures based on technical requirements. The study is positioned to support researchers and practitioners in navigating architectural paradigms.

The DATER framework addresses the strategic necessity of designing appropriate data architectures in the face of massive, heterogeneous, high-speed data. By systematically describing and evaluating architectures based on technical requirements, it helps clarify overlaps and distinctions among paradigms. The analysis supports researchers and practitioners in navigating architectural choices, highlighting that no single architecture is universally optimal; rather, suitability depends on specific technical requirements and use cases. The framework also reveals historical context and defining features that explain the evolution from traditional data warehouses to modern paradigms like data mesh and data fabric. The discussion emphasizes that data architectures shape how data is integrated, governed, and made available for analytics and decision-making, making their selection a critical strategic decision.

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