Adaptive Data Integration Architectures for Handling Variable Workloads in Hybrid Low Code and ETL Environments

Abstract

This study evaluates adaptive data integration architectures designed to manage unpredictable and highly variable workloads across hybrid low-code and ETL environments. By applying dynamic routing, interval compression, and resource-aware scheduling, the proposed framework demonstrated significant improvements in throughput, latency stability, and error-handling efficiency across simulated workload scenarios. The integration of lightweight low-code preprocessing with high-volume ETL transformations enabled smoother task distribution, reduced bottleneck formation, and more consistent execution behavior under stress conditions. Overall, the results confirm that adaptive execution models provide a resilient and scalable foundation for modern enterprise data pipelines facing continuous variability in ingestion patterns.