Self-Healing Data Pipelines: Leveraging AI to Detect and Correct Failures in Real-Time
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P117Keywords:
Self-Healing Data Pipelines, Real-Time Anomaly Detection, AI-Driven Pipeline Resilience, Automatic Rollback Mechanisms, Cloud-Native Data Engineering, Failure Prediction and Recovery, Intelligent Pipeline Monitoring, Mean Time to Repair (MTTR) Optimization, Autonomous Data Operations, Financial-Grade Data SystemsAbstract
In this paper, a practicum architecture and assessment of self-healing pipeline data applications with financial-grade constructs are given. We are focused on real-time anomaly deterrence, automatic rollbacks and rollback plans, and operation modes to reduce the duration of cloud-native pipeline downtimes. We quantify the possible decrease of the downtime and false interventions based on anomaly-detection standards (2024–2025) by using recent industry estimates of costs and applications in the business environment. The paper presents patterns of implementation, a reference architecture, and sample outcomes of the mean time to detect and repair improvement. This is why the given point is particularly pertinent in contemporary situations.
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