Polars, DuckDB, Pandas, Modin, Ponder, Fugue, Daft — which one is the best dataframe and SQL tool?

The blog post explores various open-source dataframe and SQL tools like Polars, DuckDB, Pandas, Modin, Ponder, Fugue, and Daft, highlighting their strengths and weaknesses in data engineering, machine learning, and analytics. It discusses the traditional separation and recent convergence of SQL and dataframe methodologies, emphasizing the blend of SQL's declarative querying power with the imperative, in-memory computational abilities of dataframes. While Pandas remains popular for many data science tasks, its limitations have led to the development of faster alternatives like Polars, which boasts a vectorized OLAP query engine optimized for performance and memory usage. DuckDB offers a versatile SQL dialect, integrating smoothly with dataframe libraries, while Modin and Ponder allow scaling of Pandas operations. Fugue provides a unified interface for distributed computing, and Daft is noted for supporting distributed computation akin to Spark and Dask. The article emphasizes considering organizational skills, data volume, and framework maturity when selecting a tool, recommending DuckDB and dbt for SQL-focused tasks and Polars for Python-oriented workflows, with the flexibility to transition between tools using the Apache Arrow format.