AI-Augmented Data Pipeline Optimization for Scalable Cloud Systems

Abstract

Deterministic ETL architectures - scheduled, fixed, and failure-reactive - cannot sustain the operational requirements of modern enterprise data environments, where volume growth, schema instability, and SLA pressure compound continuously. This paper presents a five-layer AI-augmented pipeline operating model that replaces reactive recovery with proactive, adaptive operation. The model integrates intelligent scheduling, continuous anomaly detection, and an operational copilot capability within a coherent Azure-native reference architecture anchored by a persistent feedback store. A structured implementation pathway and a three-dimensional evaluation framework - covering operational reliability, data quality, and delivery performance - are provided alongside the architectural specification. The model is grounded in operational observability as a prerequisite for automation, with governance controls embedded as non-optional cross-cutting elements.