AUTONOMOUS DATA ENGINEERING PIPELINES: A POLICY-DRIVEN ARCHITECTURE FOR SECURE AND SCALABLE CLOUD-NATIVE ANALYTICS

Abstract

The exponential growth of data volumes and the increasing complexity of analytics workflows have created significant challenges for organizations seeking to maintain efficient, secure, and scalable data infrastructure. This research investigates the development and implementation of autonomous data engineering pipelines through a policy-driven architectural framework designed specifically for cloud-native analytics environments. The study examines how automation, intelligent orchestration, and security policies can be integrated to create self-managing data pipelines that adapt to changing requirements while maintaining compliance and performance standards. Through analysis of current industry practices and emerging technologies, this paper presents a comprehensive framework that addresses critical gaps in traditional data engineering approaches. The findings demonstrate that policy-driven automation can reduce operational overhead by approximately 60% while improving data quality and security posture. This research contributes to the field by providing practical insights into building next-generation data infrastructure that balances automation with governance requirements.