Improving Performance of Data Extracts Using Window-Based Refresh Strategies
Swethasri Kavuri, Suman Narne
Abstract
This research paper investigates the application of window-based refresh strategies to enhance the performance of data extracts in large-scale data management systems. Traditional extract, transform, load (ETL) processes often struggle with the increasing volume and velocity of data in modern environments. Window-based refresh strategies offer a promising solution by focusing on specific subsets of data during each refresh cycle. This study examines various window-based techniques, including time-based, size-based, and hybrid approaches, and evaluates their effectiveness in improving extract performance. Through extensive analysis and empirical testing, we demonstrate that window-based strategies can significantly reduce processing time and resource utilization while maintaining data consistency and integrity. The paper also explores optimization techniques, challenges, and future research directions in this field.