Cost-Performance Trade-offs in Large-Scale ETL Workloads: Evidence from Cloud-Native Data Platforms

The paper is a cost-performance analysis of scale in large scale ETL (Extract, Transform, Load) workloads on cloudnative data platforms, especially how the cost efficiency of these workloads grows with data volume. When business organizations are struggling with more data, the necessity to streamline ETL processes in terms of performance and expenditure is paramount. The study compares the main cloud-native ETL systems including AWS Glue, Google BigQuery, Data bricks, and Snowflake and looks at the consequences of architecture options, such as scalar etching versus cluster-centered ones, batch computing versus micro-batch computing. The analysis indicates that cost efficiency follows a non-linear trend as data volumes grow, with serverless services providing greater benefits at smaller scales and cluster-based systems proving more efficient for large-scale applications. The results also indicate material cost savings achieved through architectural changes in the strategies and these have been in the 30-60% range. This research offers practical information to organizations that want to optimize their ETL pipelines in selecting the appropriate platform depending on their needs, data volumes, and transformation advancements. It ends with a pragmatic advice on choice of platform, cost optimization plans, and future research investigations, such as real-time streaming ETL analysis and multi-cloud cost arbitrage.