Self-Healing Data Pipeline Architecture Using Predictive Failure Detection and Adaptive Machine Learning

Abstract

Modern enterprises depend on reliable data pipelines for business intelligence, predictive analytics, artificial intelligence, and decision-making. However, distributed data platforms face challenges such as pipeline failures, data quality issues, schema changes, processing delays, and infrastructure instability. Traditional monitoring systems rely on reactive alerts and manual intervention. This paper proposes a conceptual self-healing data pipeline architecture integrating predictive failure detection, anomaly detection, automated recovery, continuous data quality assessment, root-cause analysis, and feedback-driven optimization. A literature-based methodology is used to evaluate the proposed framework conceptually against reactive monitoring, rule-based recovery, and predictive monitoring. Key evaluation metrics include recovery time, failure detection, data integrity, false alarms, processing latency, and operational overhead. As the architecture has not been experimentally implemented or tested, no empirical performance claims are made. The study also identifies challenges involving concept drift, recovery safety, model uncertainty, dependency management, and governance. The proposed framework provides a foundation for future research and practical implementation in enterprise data engineering, cloud analytics, and machine learning operations.