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.

翻译：现代组织以高速生成并消费海量异构数据，这要求持续开发新技术以实现更高效、可靠的数据管理。因此，设计合适的数据架构已成为战略必需，因为它们决定了数据如何集成、治理，并用于分析与决策。本文提出一个概念框架——数据架构及其技术需求（DATER）——用以基于技术需求系统性地描述和评估数据架构。研究考察了六种现代架构：数据仓库、（语义）数据湖、数据湖仓、数据编织和数据网格。每种架构均从历史背景、定义特征及对DATER维度的符合性进行分析。本研究有助于研究人员与实践者理解架构范式的演变、厘清重叠之处，并突出优势、局限及适用场景。