The rise of polyglot data management and AI-ready database architectures has created a complex design space across diverse database paradigms. However, architecture selection in modern enterprise environments continues to rely heavily on ad-hoc engineering intuition, with limited systematic frameworks to guide decision-making across heterogeneous database systems. This paper introduces a unified cross-paradigm evaluation and selection framework for database architecture design in AI-ready data platforms. The framework is based on nine architectural dimensions and incorporates a structured multi-stage selection process involving workload characterization, constraint filtering, and compatibility scoring to enable systematic comparison and decision-making. To ground the framework, we conduct a structured comparative analysis across thirteen major database paradigms spanning transactional, analytical, and AI-oriented systems. This analysis reveals three recurring patterns in database evolution: decoupling of storage and compute, workload-driven specialization, and convergence toward integrated AI-ready platforms. The proposed framework is demonstrated through a representative enterprise case study in financial fraud detection, illustrating how hybrid, polyglot architectures emerge as optimal solutions for multidimensional workload requirements. The cross-paradigm analysis culminates in an AI-ready reference architecture that integrates lakehouse storage, feature processing, and semantic retrieval layers as the unified substrate for modern analytics, machine learning, and Retrieval-Augmented Generation applications.

翻译：多语言数据管理与人工智能就绪数据库架构的兴起，在多样化的数据库范式中构建了复杂的设计空间。然而，在现代企业环境中，架构选择仍严重依赖临时性的工程直觉，缺乏系统性的框架来指导异构数据库系统间的决策。本文提出了一种用于人工智能就绪数据平台中数据库架构设计的统一跨范式评估与选择框架。该框架基于九个架构维度，并引入结构化的多阶段选择流程，包括工作负载表征、约束过滤和兼容性评分，以实现系统化的比较与决策。为奠定基础，我们对涵盖事务型、分析型及面向AI系统的十三种主流数据库范式进行了结构化比较分析。分析揭示了数据库演化的三种反复出现的模式：存储与计算的解耦、工作负载驱动的专业化，以及向集成化人工智能就绪平台的趋同。通过一个典型的金融欺诈检测企业案例研究，展示了该框架的应用，说明混合多语言架构如何成为满足多维工作负载需求的最优方案。跨范式分析最终形成了一种面向AI的参考架构，该架构集成了湖仓存储、特征处理与语义检索层，作为现代分析、机器学习及检索增强生成应用的统一基础。