Building Robust Fintech Reporting Systems Using JPA with Embedded SQL for Real-Time Data Accuracy and Consistency
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Abstract
In the rapidly evolving fintech domain, the need for high-throughput, low-latency, real-time reporting systems has increased due to rising transaction volumes. This paper examines the architectural design and technical implementation of fintech reporting systems integrating Java Persistence API (JPA) with embedded SQL, ensuring data accuracy, efficient query execution, and transaction consistency. JPA, an Object-Relational Mapping (ORM) framework, abstracts interactions between Java objects and relational databases, simplifying development through entity mapping and reducing boilerplate code. Annotations like @Entity, @Table, and @Column define entity relationships and constraints, enabling automated schema synchronization. Embedded SQL complements JPA by allowing direct SQL query injection, optimizing complex, performance-critical queries while maintaining JPA’s portability. A multi-tier design ensures separation of concerns and scalability. The persistence layer, managed by JPA, facilitates database interaction, while connection pooling (e.g., HikariCP) and caching strategies (first-level and second-level) enhance transaction throughput. Transactional integrity is enforced via isolation levels, locking strategies, and batch processing, preventing issues like dirty reads and lost updates. Using native SQL queries via JPA’s create Native Query method, the system leverages advanced database optimization features. Profiling tools ensure minimal query latency, supporting real-time financial reporting with strict performance standards. By combining JPA’s abstraction with embedded SQL’s efficiency, this architecture provides a resilient, scalable reporting system, ensuring data integrity and optimized query execution for fintech operations.
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