Large Data Downloads from WRDS Using Python

[pinpss]

I’m currently working with WRDS and need to download a significant amount of data. While I understand that SAS is often the go-to for handling large data downloads efficiently, I prefer to manage everything within Python to streamline my workflow.
Could anyone share tips or best practices for optimizing data downloads from WRDS using Python? Any libraries, tools, or configurations that can help improve speed and performance when dealing with large datasets from WRDS?

[FlipperPA]

Hello @pinpss, I'm Tim, I work for WRDS and do a lot with Python. I would recommend connecting directly to PostgreSQL, and consider using polars instead of pandas. It is a similar library, with the concept of DataFrames, that isn't memory bound. It is written in Rust for speed. What data product(s) are you using?