Polars is best used when performance and memory efficiency are critical, especially with large datasets. For instance, in a scenario where you are processing a large parquet file for real-time analytics where speed is essential, Polars would be preferred over Dask due to its optimized performance.
You would use Dask over Polars when you need to handle very large datasets that exceed your local machine's memory or when you want to distribute the computation across a cluster of machines. For example, if you're analyzing a massive dataset stored in Parquet files across multiple servers and need to perform complex computations while leveraging parallel processing, Dask is a suitable choice.