Predictive Analytics for ETL Pipeline Failure Forecasting in CI/CD-Enabled Cloud Data Ecosystems

Pramod Raja Konda

Abstract


As enterprise data ecosystems grow in complexity, the reliability of Extract–Transform–Load (ETL) pipelines becomes critical for ensuring continuous data availability, real-time analytics, and operational efficiency. In CI/CD-enabled cloud environments, where pipelines are frequently updated and deployed, failures can lead to disrupted workflows, delayed insights, and increased operational costs. This paper presents a predictive analytics framework for forecasting ETL pipeline failures using machine learning–driven anomaly detection, metadata modeling, historical run logs, and CI/CD telemetry. The proposed approach integrates feature engineering from performance metrics, resource utilization patterns, schema drift indicators, and version-control triggers to predict failure risks with high precision. Through experimental evaluation on large-scale cloud data platforms, the framework demonstrates significant improvements in failure prediction accuracy, deployment stability, and fault resolution times. The study highlights how predictive analytics can transform ETL observability from reactive monitoring to proactive prevention, ultimately enhancing reliability, automation, and resilience in modern cloud-native data ecosystems

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