Self-Healing Cloud Data Pipelines Using AI-Based Failure Detection, Root Cause Analysis, and Automated Recovery

Abstract

Enterprise cloud data pipelines fail far more often than organizations acknowledge, and the downstream cost of each failure extends well beyond the immediate job. Schema drift, delayed file arrivals, resource exhaustion, API instability, and orchestration defects each produce cascading effects across reporting layers, machine learning features, and regulatory deliverables. Current monitoring approaches detect failure states but stop short of diagnosis and remediation, leaving operational teams to manually cross-reference logs, lineage records, and infrastructure metrics before any recovery can begin. This article proposes a self-healing cloud pipeline framework that closes the gap between detection and resolution. The framework integrates observability telemetry aggregation, machine learning-based failure classification, retrieval-grounded large language model (LLM) root cause interpretation, and policy-driven automated recovery execution within a four-layer architecture. The layers, including observation, intelligence, decisioning, and recovery processes, manage incidents from the initial signal to the resolved state without requiring human intervention in approved scenarios. The approach targets measurable reductions in mean time to recovery (MTTR), lower manual intervention rates, and more consistent recovery behavior across large and diverse pipeline estates. The framework is designed for cloud-native orchestration environments and is assessed against rule-based and machine-learning-only baseline approaches across representative failure scenario categories.