The rapid growth of data-intensive applications has necessitated the adoption of diverse data storage technologies to meet evolving performance, scalability, and reliability requirements. Traditional single-database approaches often fail to address the heterogeneous data needs of modern systems, leading to inefficiencies in data management and processing. This research proposes a unified hybrid persistence framework that integrates in-memory, NoSQL, and relational databases—specifically Redis, MongoDB, and PostgreSQL—to optimize data storage and retrieval strategies in high-performance environments. The framework leverages Redis for low-latency caching and real-time data access, MongoDB for flexible schema design and efficient handling of semi-structured data, and PostgreSQL for strong transactional integrity and advanced querying capabilities. By combining these systems within a cohesive architecture, the proposed approach enables intelligent data tiering, workload distribution, and consistency management. Furthermore, the study introduces adaptive data routing and synchronization mechanisms to ensure seamless interoperability across multiple persistence layers. Experimental evaluation indicates that the proposed framework significantly improves system throughput, reduces query response time, and enhances scalability compared to traditional monolithic database solutions. Additionally, it strengthens fault tolerance and supports dynamic scaling in distributed environments, making it highly suitable for modern cloud-native and enterprise-scale applications.
Anderson et al. (Fri,) studied this question.