🤖 AI Summary
This study addresses the lack of systematic methodologies in modern enterprises for selecting databases across multiple paradigms, a process often driven by ad hoc experience. The authors propose a cross-paradigm database architecture evaluation and selection framework grounded in nine dimensions, employing a three-stage workflow—workload characterization, constraint filtering, and compatibility scoring—to enable scientifically informed decisions for AI-ready data platforms. The work identifies three emergent trends in database evolution: compute-storage disaggregation, workload-driven specialization, and convergence toward AI-ready platforms, and introduces the first unified reference architecture tailored for AI applications. Validated through a financial fraud detection case study, the approach demonstrates the advantages of a hybrid polyglot architecture, integrating lakehouse storage, feature engineering, and semantic retrieval layers to effectively support modern analytics, machine learning, and retrieval-augmented generation (RAG) applications.
📝 Abstract
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.