A Hybrid Strategy Sustainable for Combining Relational and Graph Databases to Improve the Effectiveness of Complex Relationship Analysis

Abstract

The purpose of this study is to develop and implement a hybrid approach that addresses the limitations of relational databases (such as MySQL) and graph databases (like Neo4j) in analyzing complex relationships. The study used an experimental methodology that replicated a setting with network data (recommendations, social ties) stored in a graph and structured data (customers, products) saved in a relational database. The findings showed that, while preserving data integrity, the suggested approach significantly reduced the execution time of complicated queries involving multi-level traversal of relationships (on average, 70% faster) when compared to depending only on the relational database. The study also showed that some data duplication resulted in a 15% increase in storage costs, which is acceptable given the notable improvements in speed. The study concludes that the hybrid model offers a workable option for applications that need dependable data traversal operations in addition to sophisticated relationship inquiries. The development of such resource-efficient data architectures is a critical step towards creating sustainable digital infrastructures, which are fundamental for managing the complex, interconnected systems that underpin modern economies and societies and directly supports SDG 9 targets 9.4 (improve infrastructure for sustainability) and 9.c (universal ICT access) by enabling effective digital infrastructure for developing economies through a 70–83% reduction in computational resource consumption for relationship queries.