Pandas vs Polars vs DuckDB vs PySpark: The Data Engineer’s Guide to Choosing the Right Tool

source: OpenAI GPT Image 2 model You have been there. A 2GB CSV lands on your desk, you fire up Jupyter Notebook, run `pd.read_csv()`, and watch your laptop fan spin up like a jet engine. Five minutes later, your kernel crashes. You restart, try again with `low_memory=False`, and pray. This is the Pandas experience at scale, and if you have been in data science or engineering for any length of time, it is painfully familiar. But here is the thing most tutorials will not tell you: Pandas was never designed for big data...