ACCELERATING ETL/ELT PROCESSES OF VOICE SIGNALS WITH GPU CLUSTERS: OVERVIEW

Abstract

The implementation of GPU clusters in ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes addresses significant challenges in data-intensive tasks, including high-speed data processing, efficient resource utilization, and scalability. This paper overviews several approaches—RAPIDS Accelerator for Apache Spark, RAPIDS and Dask integration, Cloudera Data Platform with NVIDIA GPUs, and Cylon framework—highlighting their effectiveness in accelerating ETL/ELT operations for voice signals. The analysis provides insights into the advantages and limitations of each method, offering guidance for optimizing data transformation and analytics on GPU clusters.