Mr. Somesh Nagalla

AI-Native Data Architecture: A Self-Optimizing Framework for Real-Time, Intelligent Data Ecosystems

Abstract:

As enterprises adopt AI-first strategies, traditional data architectures fail to keep pace with the dynamic, real-time needs of machine learning workloads. This paper introduces an AI-native data architecture that leverages AI agents for real-time metadata-driven orchestration, self-tuning pipelines, and predictive resource allocation. The architecture is built around event-driven microservices, data mesh principles, and active metadata utilization, enabling autonomous data management at scale. We showcase its application in streaming ETL, feature store optimization, and cloud cost governance.