This article explores the transformative potential of self-healing data pipelines enhanced by generative artificial intelligence in next-generation data engineering environments. The integration of machine learning models capable of predicting, detecting, and autonomously resolving anomalies represents a paradigm shift in how organizations manage their data infrastructure. By examining both the technical architecture and organizational implications of these systems, the article demonstrates how self-healing pipelines can significantly reduce operational overhead while improving data quality and processing reliability. The article investigates implementation strategies across various industry contexts, addressing technical challenges and governance considerations that emerge when deploying such systems. The article suggests that organizations adopting self-healing pipelines experience substantial improvements in operational efficiency and data integrity, ultimately enabling more sophisticated data-driven decision making. This article contributes to the evolving discourse on autonomous data systems and provides a framework for future research and implementation in the field of advanced data engineering.
Keywords: Predictive Maintenance, autonomous data systems, data engineering automation, generative AI, self-healing pipelines
