Data Observability and Self-Healing Pipeline Architecture in Regulated Enterprises: Proactive Anomaly Detection, Schema Drift Management, and Automated Recovery Strategies
DOI:
https://doi.org/10.22399/ijcesen.5165Keywords:
Data Observability, Self-Healing, Data Pipelines, Schema Drift Detection, HIPAA/SOX, DataOps Pipeline ResilienceAbstract
Production data pipelines increasingly serve as critical infrastructure in regulated industries, yet they frequently fail silently—without triggering traditional monitoring alerts. These failures, including schema drift, delayed data delivery, and statistical anomalies, propagate across downstream systems and compromise compliance, analytics, and decision-making. This paper presents a practitioner-oriented research contribution to DataOps and data reliability engineering, focusing on the integration of data observability with self-healing pipeline architectures. We define data observability as a continuous, production-runtime discipline distinct from monitoring and testing, and introduce a structured framework based on five pillars: freshness, volume consistency, schema stability, data distribution, and lineage traceability. The paper further develops a confidence-based escalation framework for automated remediation, examines anomaly detection techniques applied to pipeline telemetry, and proposes self-healing mechanisms including dynamic retries, rollback strategies, and failure containment. Regulatory implications are analyzed in HIPAA-governed healthcare systems and SOX-audited financial environments. Finally, a comparative review of observability tooling is provided, along with architectural integration guidance
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