Maximizing ETL efficiency: Patterns for high-volume data

The increasing demands of big data environments have placed a renewed emphasis on the efficiency of Extract, Transform, and Load (ETL) processes. Traditional batch-oriented ETL approaches struggle to cope with the scale, velocity, and variety of modern datasets. This review explores emerging patterns and architectures for maximizing ETL efficiency in high-volume data contexts, focusing on serverless frameworks, real-time processing, distributed computation models, and cost optimization strategies. Experimental evaluations demonstrate that serverless and stream-based ETL frameworks achieve superior performance compared to traditional batch designs. The study further outlines future research directions, emphasizing AI-driven orchestration, hybrid ETL models, and energy-efficient transformations. These advancements are crucial for building robust, adaptive, and cost-effective ETL systems capable of supporting the evolving requirements of data-driven enterprises.