Design of Fault Tolerant ETL Workflows for Heterogeneous Data Sources in Enterprise Ecosystems

Abstract

Fault-tolerant ETL architectures have become essential for ensuring continuous, reliable data movement in enterprise ecosystems characterized by highly heterogeneous and unstable data sources. This article presents a comprehensive evaluation of an adaptive ETL framework built on resilient ingestion gateways, schema-evolution handling, fine-grained fault isolation, intelligent routing, and checkpoint-based recovery. Results from multi-source load simulations show notable improvements in ingestion stability, transformation consistency, and recovery speed, even under highly variable conditions. By preventing localized failures from escalating into systemic disruptions, the proposed architecture strengthens end-to-end data reliability and supports mission-critical analytical workflows that depend on uninterrupted, high-quality data. The findings underscore the value of designing ETL systems with resilience-first principles to meet the demands of modern, distributed, and continuously evolving enterprise data environments.