PERFORMANCE ANALYSIS OF EXTRACT, TRANSFORM, AND LOAD METHODS FOR BUSINESS INTELLIGENCE IN E-LEARNING SYSTEMS USING PENTAHO DATA INTEGRATION

The rapid adoption of Learning Management Systems (LMS) in higher education has resulted in the generation of large and complex datasets, posing significant challenges for efficient data integration and analysis. The urgency to address these challenges is driven by the growing demand for real-time analytics and data-driven decision-making in educational institutions. This study advances the field of computer science by evaluating and comparing the performance of three Extract, Transform, and Load (ETL) methods—Table Output, Sync After Merge, and Switch Case—using Pentaho Data Integration (PDI). The study introduces novel insights into ETL optimization techniques, focusing on execution time as the primary metric, critical for ensuring timely and reliable insights in Business Intelligence (BI) systems. Performance testing was conducted with synthetic datasets ranging from 150 to 1,000,000 records across five scenarios: data addition, synchronization, insertion, deletion, and combined operations. Results reveal that Sync After Merge outperformed other methods, achieving up to 35% faster execution times, particularly with large datasets. These findings contribute significantly to the advancement of data integration techniques in computer science, enabling institutions to optimize their BI systems, enhance system responsiveness, and support data-driven decision-making processes effectively. The research provides valuable insights for developing scalable ETL solutions in educational technology systems.