ADVANCED ETL OPTIMIZATION: A FRAMEWORK FOR NEXT-GENERATION DATA INTEGRATION

Abstract

This article presents a comprehensive framework for next-generation data integration, focusing on advanced ETL optimization strategies in modern enterprise environments. Through analysis of 47 enterprise implementations across manufacturing, healthcare, and financial services sectors from 2020-2023, the article evaluates the evolution from batch processing to real-time integration solutions. Our methodology combines quantitative performance benchmarking with qualitative assessments of implementation success factors across organizations ranging from 500 to 50,000 employees, analyzing over 1,200 ETL workflows across diverse technology stacks including Informatica PowerCenter, DBT, and Apache Airflow.Key findings demonstrate a 65% reduction in processing time through parallel execution optimization, 40% improvement in resource utilization through dynamic allocation, and 83% decrease in failed jobs through enhanced error handling protocols. Implementation of the proposed framework resulted in average performance improvements of 47% while reducing operational costs by 32%. Financial services organizations achieved 59.8% reduction in processing time, healthcare providers improved patient data integration efficiency by 54.3%, and retail operations optimized catalog updates by 68.7%. This article primary contribution is a novel, empirically-validated framework that synthesizes best practices from large-scale implementations while providing practical guidelines for scalable data integration. The framework introduces new methodologies for automated bottleneck detection and resolution, alongside a mathematical model for optimizing resource allocation in hybrid cloud-on-premise environments, demonstrating consistent performance improvements across varying workloads from 500GB to 30TB.