Enterprise Data Modernization using Cloud-Native Architectures and Automation
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Abstract
Enterprise data platforms are rapidly evolving as organizations transition from legacy, monolithic systems to cloud-native architectures. Traditional ETL-driven platforms struggle to meet modern enterprise requirements such as elastic scalability, automation, real-time availability, and regulatory compliance. This paper presents an end-to-end framework for enterprise data modernization using cloud-native architectures and automation. The proposed model integrates distributed data processing, workflow orchestration, infrastructure as code, monitoring, and compliance-by-design principles. Through architectural analysis, implementation patterns, and enterprise case scenarios, this study demonstrates how automation-driven data platforms improve operational efficiency, reduce compliance risk, and enable scalable analytics across regulated industries.
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