1. The DataFrame scale gap When I started working on Polars, I was surprised how much DataFrame implementations differed from SQL and databases. SQL could run anywhere 1. It could run embedded, on a client server model, or on full OLAP data warehouses. Whereas for dataframes, the API was different per use case and performance was drastically lacking behind SQL solutions. Locally, pandas was dominant, and remotely/distributed, it was PySpark. For end-users, pandas was very easy to get up and runn...