Zero‑ETL Analytics: Transforming operational data into actionable insights

The emergence of Zero‑ETL (Extract, Transform, Load) analytics promises to revolutionize operational decision-making by enabling real-time insights without the traditional ETL burden. This study explores how Zero‑ETL architectures can transform operational data into actionable intelligence, focusing on healthcare operations management. Using a simulated hospital operations dataset (adapted from the MIMIC-IV database), we implement and evaluate a Zero‑ETL analytics pipeline. The results indicate that Zero‑ETL not only reduces latency and operational costs but also improves decision-making efficacy compared to traditional ETL approaches. The study provides both theoretical foundations and practical implications for deploying Zero‑ETL analytics in data-intensive environments.