Measuring ETL Price-Performance On Cloud Data Platforms
Blog post from Onehouse
Cloud data platforms are essential for businesses, but evaluating their price-performance for ETL (Extract, Transform, Load) workloads remains a challenge due to standard benchmarking tools failing to capture the complexities of modern cloud data ecosystems. As companies increasingly rely on data lakehouses and open table formats, understanding real-world ETL costs becomes critical, especially given the rise of streaming data and mutable data patterns. Traditional benchmarks like TPC-DS and TPC-DI fall short in accurately modeling ETL workloads due to their outdated assumptions about data immutability and simplistic update/delete patterns. To address these gaps, Onehouse introduces Lake Loader, an open-source benchmarking tool designed to simulate realistic ETL workloads across DIM, FACT, and EVENT tables, offering a more accurate measurement of ETL performance and cost. This tool allows for consistent comparisons across platforms like AWS EMR, Databricks, and Snowflake, and aims to advance the standardization of ETL benchmarking by providing insights into incremental data changes, concurrency, and the impact of mutable operations.
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