Aim: This study aims to design and analyze a scalable and resilient service-oriented data architecture tailored for enterprise systems by integrating event-driven microservices with distributed storage solutions. The research focuses on addressing limitations of monolithic architectures, including scalability bottlenecks, tight coupling, and inefficiencies in handling large-scale data processing. It also seeks to enhance system responsiveness and flexibility in dynamic enterprise environments. Method: A qualitative and architectural design-based methodology is employed, incorporating comparative analysis of existing enterprise architectures and simulation-based validation. The proposed model integrates event-driven communication mechanisms such as message brokers and asynchronous processing with distributed storage technologies including NoSQL databases and data lakes. Architectural patterns like CQRS and event sourcing are also evaluated. Results: The findings demonstrate improved scalability, fault tolerance, and data consistency across distributed systems. The integration of event-driven microservices significantly reduces system latency and enhances modularity. Distributed storage contributes to improved data availability and horizontal scalability. Experimental simulations indicate better performance compared to traditional service-oriented and monolithic architectures. Conclusion: The proposed architecture provides a robust framework for modern enterprise systems requiring high scalability and real-time data processing. It enables organizations to transition toward cloud-native and resilient infrastructures. Future research may focus on optimizing orchestration strategies and enhancing security mechanisms in distributed environments.
Rohit Wadhwa (Wed,) studied this question.