Data Architectures and their Technical Requirements (DATER)

2026-06-07

AI Summary

This study addresses the lack of systematic methodologies for selecting data architectures in modern organizations grappling with vast, heterogeneous data environments. To this end, it proposes the DATER conceptual framework, which establishes a unified taxonomy of technical requirements and systematically examines the historical evolution, core characteristics, and applicability boundaries of six prominent data architectures: data warehouses, data lakes, lakehouses, data fabrics, and data meshes. Through conceptual modeling and multidimensional comparative analysis, the framework clarifies overlaps and distinctions among these architectures, articulating their respective strengths and limitations. By offering a structured evaluation tool, DATER significantly enhances the strategic alignment and contextual appropriateness of data architecture design for both researchers and practitioners.

Abstract

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.

Problem

• data architecture
• heterogeneous data
• data management
• technical requirements
• data integration

Innovation

• Data Architecture
• DATER Framework
• Data Lakehouse
• Data Fabric
• Data Mesh