A Unified Apache Database Upgrade Framework using Gray Relational Analysis and Azure DevOps Automation for Fraud Analytics, Healthcare Security, and Cloud-Enabled Generative AI
DOI:
https://doi.org/10.15680/IJCTECE.2024.0705005Keywords:
Apache database upgrade, Gray Relational Analysis (GRA), Azure DevOps automation, Cloud security, Fraud analytics, Healthcare data security, Generative AI, Continuous integration and deployment (CI/CD), Cloud modernization, Database reliability engineeringAbstract
The rapid evolution of data-intensive applications in financial and healthcare domains demands robust, automated, and secure database upgrade mechanisms. This paper presents a unified Apache Database Upgrade Framework that integrates Gray Relational Analysis (GRA) with Azure DevOps–driven automation to streamline upgrade decision-making, reduce operational risk, and enhance system resilience. GRA is employed to evaluate multi-criteria upgrade parameters—including performance metrics, compatibility attributes, latency behavior, and security compliance—yielding an optimized upgrade path. Azure DevOps pipelines automate version deployment, validation, rollback operations, and continuous monitoring across distributed cloud environments. The framework is further validated in two high-risk application scenarios: credit card fraud analytics and healthcare security workflows incorporating cloud-enabled Generative AI models. Experimental results demonstrate significant reductions in upgrade time, configuration drift, and error rates while improving security posture and data governance. The proposed solution offers a scalable, secure, and intelligent blueprint for modernizing Apache-based architectures in regulated industries.
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