Authors: Richard Taylor, Mark Allen, Joshua Baker, Chaitanya Srinivas, Niharika
Abstract: Modern enterprises increasingly depend on complex data pipelines to support real-time analytics, business intelligence, machine learning, and mission-critical decision-making. However, conventional data pipeline monitoring approaches are largely reactive, requiring manual intervention after failures, performance degradation, data quality issues, or infrastructure anomalies have already occurred. These limitations can result in increased downtime, data loss, processing delays, and operational costs. This research proposes a Proactive AI-Driven Framework for Intelligent Data Pipeline Monitoring and Self-Healing that integrates artificial intelligence, predictive analytics, anomaly detection, automated diagnostics, and intelligent remediation mechanisms to improve the reliability and resilience of enterprise data pipelines. The proposed framework continuously analyzes pipeline telemetry, including execution metrics, processing latency, failure patterns, resource utilization, logs, data-quality indicators, and dependency information. Machine learning models are employed to identify anomalous behavior and predict potential pipeline failures before they occur. Based on detected conditions, an intelligent decision engine determines appropriate corrective actions, such as task retry, resource optimization, dependency recovery, workload redistribution, configuration adjustment, or pipeline restart. A feedback mechanism continuously evaluates remediation outcomes and enhances future predictions and recovery decisions. The framework aims to reduce pipeline downtime, mean time to recovery (MTTR), false alerts, manual operational effort, and data-processing failures while improving pipeline availability, reliability, and operational efficiency. The proposed approach provides a scalable foundation for autonomous data operations and demonstrates how AI-driven predictive monitoring can transform traditional reactive pipeline management into a proactive and self-healing data engineering model.
DOI: http://doi.org/10.5281/zenodo.23076070
