CLOUD-BASED DATA ARCHITECTURE DESIGN FOR SCALABLE ENTERPRISE INFORMATION SYSTEMS

As organizations continue to expand their data volumes, and the need for real-time decision-making rises, the structural restrictions of monolithic and on-premises information systems have become increasingly apparent. This paper introduces a layered, cloud-based data architecture that could be used to enable elastic scalability, high availability and data governance across a large enterprise environment. The design stack consists of an API gateway tier, microservices running in containers, event-driven data integration tier and polyglot distributed storage tier, all of which are deployed on elastic cloud infrastructure. The evaluation is performed using a simulation based approach which is compared to a traditional monolithic deployment along four metrics: response time under concurrent load, system availability, data throughput and operational cost. According to the results, the proposed architecture is able to decrease the average response time by around 85% when the load reaches 10,000 concurrent users, increase the data throughput by over 280%, decrease the relative operational cost index by 48%, and enhance the system availability to 99.95%. The results clearly show the significant benefits of a well-designed, cloud-native data architecture in terms of scalability, resilience, and cost-effectiveness for enterprise information systems. The paper ends with guidelines for architecture design along with future research directions for adaptive, AI-assisted cloud data governance.