Self-Healing Data Quality Pipelines in Cloud-Native Architectures Using Event-Driven Learning

Abstract

Data quality failures impose significant financial burdens on cloud-native enterprises through downstream transaction failures, compromised customer experiences, and extensive manual remediation. Traditional rule-based validation and centralized cleansing pipelines cannot address distributed system characteristics: fragmented microservices ownership, asynchronous failure manifestation, context-dependent correctness, and informalized recurring remediation patterns. This article proposes a self-healing data quality framework treating quality outcomes as observable events within event-driven architectures, enabling continuous learning and adaptive remediation while preserving governance controls essential for regulated environments. The framework implements domain-aligned microservices with a layered architecture separating deterministic validation from learning augmentation. Event streams capture quality signals enabling pattern recognition, confidence-based decisioning, and automated correction. Feedback loops incorporating downstream outcomes and human overrides continuously refine learning models. Embedded governance mechanisms ensure explainability, auditability, and controlled automation through decision traceability, version logging, and role-based thresholds. Operational metrics prioritize reducing repeat failures and manual effort over isolated model accuracy, transforming data quality into adaptive system capability, maintaining regulatory compliance and stakeholder trust.