Data Architecture Models for Enterprise Applications and Their Implications for Data Integration and Analytics
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P110Keywords:
Data Architecture, Enterprise Applications, Data Integration, Analytics, Microservices, Service-Oriented Architecture, Data Mesh, Event-Driven ArchitectureAbstract
The digital transformation of modern enterprises has significantly increased the demand for scalable and flexible data architecture models that can seamlessly support data integration and analytics. This paper explores various data architecture models, such as monolithic, layered, service-oriented, microservices, and data mesh, analyzing their roles in enterprise application ecosystems. It further examines how these models influence data integration and analytics processes, impacting decision-making, business intelligence, and operational efficiency. Key challenges such as data silos, interoperability, and latency are discussed alongside emerging trends like event-driven architecture and cloud-native patterns. The paper also includes comparative analysis through case studies, architecture diagrams, and performance metrics to assess the suitability of each model. The findings emphasize that while no single architecture fits all use cases, hybrid models and best practices in governance, metadata management, and real-time data streaming are crucial to enabling robust enterprise-level integration and analytics capabilities
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