The Role of Artificial Intelligence in Predictive ETL and Failure Prevention
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-132Keywords:
Artificial Intelligence, Predictive ETL, Data Pipelines, Anomaly Detection, Failure Prevention, Machine Learning, Data Reliability, Explainable AI, Resource Optimization, Human-in-the-LoopAbstract
A modern data ecosystem relies on Extract, Transform, Load (ETL) processes, which are especially prone to failures that disrupt operations, impede decision-making, and hamper data quality. The most disruptive failures are caused by schema drift, data inconsistency, orchestration discrepancies, and infrastructural constraints; each can lead to costs in organizations that rely on real-time analytics and robust pipelines. Artificial Intelligence (AI) plays an important role by predicting and preventing failures through anomaly detection and monitoring, by predicting workloads and contextualizing monitoring away from the ETL processes as anomalies are reported. Additionally, AI models can independently receive history, logs, usage, and performance metrics and offer expectations of bottlenecking for downstream ETL processes, anomaly detection in real-time, and suggestions to rectify issues before failures arise. This research explores AI in predictive ETL systems to evaluate the efficiency, scalability and reliability of automated systems. Overall, it concludes that the best implementation involves a human-in-the-loop model, where human use of automation improves resilience, operational efficiency and ethical accountability.
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