Spark vs. DuckDB vs. Polars: Benchmarking Performance for Small to Medium Workloads
2024-12-15
This article benchmarks Spark, DuckDB, and Polars, comparing their performance, cost, and development ease on 10GB and 100GB datasets. Results show that for large datasets and ETL tasks, Spark remains dominant due to its distributed computing capabilities and mature ecosystem. DuckDB and Polars excel at interactive querying and data exploration on smaller datasets. The author recommends a strategic mix-and-match approach, using Spark for ETL, DuckDB for interactive queries, and Polars for niche scenarios, tailoring engine choice to specific needs.