Unified Data Access in Multi-Model Databases: Querying Relational, Graph, and Document Data Seamlessly

Abstract

The proliferation of diverse data types in modern applications has catalyzed the development of multi-model databases—systems capable of managing and querying disparate models such as relational, graph, and document data within a unified framework. This paper systematically examines state-of-the-art architectures, query languages, and optimization strategies that enable seamless unified data access across these models. We address recent advances concerning distributed and edge computing environments, real-time streaming analytics, and tackle security, consistency, and reliability risks associated with cross-model operations. Leveraging a comparative approach, we synthesize findings from empirical and theoretical investigations, including GPU-accelerated querying and hybrid execution models. The study concludes by identifying persisting challenges and proposing directions for research on efficient, consistent, and secure multi-model query processing with growing data velocity and scale.