Integrating Polyglot Persistence with Large Language Models for Scalable Social Network Applications

Modern cloud applications, particularly those resembling professional social networks, demand data management systems capable of handling heterogeneous, highly interconnected data. Traditional relational databases are often inadequate for such dynamic environments. This paper proposes a polyglot persistence architecture that combines document, graph, and key–value data stores to address diverse data storage and query requirements. Moreover, by integrating Large Language Models (LLMs) as an intelligent query and analytics interface, the system can interpret natural language requests, generate structured queries across multiple data stores, and provide personalized insights. We discuss the architectural rationale, outline the integration of LLMs with multi-database systems, and propose future research directions.