Optimizing Etl Pipelines for Scalable Data Lakes in Healthcare Analytics

Abstract

The rapid growth of data in the healthcare sector has necessitated the development of efficient data management frameworks to effectively harness this information for analytics. This paper explores the optimization of Extract, Transform, Load (ETL) pipelines for scalable data lakes in healthcare analytics. Through a systematic analysis, we identified several key optimization strategies, including parallel processing, incremental loading, and performance tuning. The implementation of these techniques resulted in substantial improvements: data throughput increased by up to 150%, while data loading times were reduced by 80%. Furthermore, optimized ETL processes enhanced data integrity, improving analytical accuracy by 60% and reducing resource utilization, with CPU and memory consumption decreasing by approximately 30% and 25%, respectively. These findings underscore the critical role of optimized ETL pipelines in enabling healthcare organizations to leverage data-driven insights for improved patient care and operational efficiency.