DuckDB is particularly useful when you need to perform complex SQL queries on larger datasets that don't fit into memory. For instance, if you're working on a data analysis project with a sizable dataset stored in a CSV or Parquet format, and you want to run aggregate functions or joins seamlessly, DuckDB would be more suitable than Polars, which operates primarily in-memory.
Polars is ideal for scenarios where you need to perform large-scale data manipulations and analysis in-memory efficiently. For example, if you are working with a large dataset that requires complex transformations and you want to take full advantage of lazy evaluations to optimize performance, Polars would be a better choice. Additionally, if you are familiar with DataFrame-like APIs from Python or Pandas, Polars provides a similar interface for ease of use.