(untitled) (UKLO Proceedings, 2026)
Introduction: This study explores the use of SnapLogic automation to modernize Extract, Transform, and Load (ETL) processes and support scalable machine learning (ML) workflows. Traditional ETL pipelines are slowed by manual intervention, inefficiency, and limited scalability. Methods: Our methodology followed an applied research design, focusing on the practical implementation of SnapLogic automation for ETL and machine learning workflows. Automated pipelines were built to handle data preparation, model deployment, and performance monitoring, and were tested with both synthetic and real-world datasets. Results: SnapLogic automation significantly improved efficiency, reduced development time, minimized manual errors, and enabled real-time processing of large, dynamic workloads. Continuous monitoring and automated lifecycle management enhanced system reliability and resilience. Discussion: These findings demonstrate that SnapLogic directly addresses the limitations of traditional ETL by streamlining workflows and bridging the gap between data engineering and ML. While the platform may involve a higher upfront investment, it optimizes long-term costs for large organizations.