Cloud-Native Data Engineering: Improving Reliability Through AIOrchestrated Operations

Abstract

Cloud-native data engineering is rapidly evolving as organizations shift towards scalable, flexible, and highly reliable cloud infrastructures. In this context, AI-orchestrated operations are emerging as critical enablers for enhancing system reliability, automating error detection, and optimizing resource management. This paper explores the intersection of cloud-native architectures and artificial intelligence, focusing on how AI-driven orchestration mechanisms can address challenges in data processing, real-time decision-making, and system resilience. Key strategies include adaptive resource allocation, anomaly detection, and self-healing mechanisms, which significantly reduce downtime and improve operational efficiency. By leveraging AI, cloud-native environments can achieve unprecedented levels of automation and reliability, ensuring continuous availability of mission-critical data pipelines. This research provides insights into the design and implementation of AI-powered cloud-native data engineering solutions, offering a roadmap for improving performance and robustness in modern data-driven ecosystems.