I spent way too many nights last year trying to push 500MB CSVs through various tools. Honestly, the native data analysis feature in the OpenAI ChatGPT Plus Subscription is surprisingly robust because it spins up a local Python instance, but it does hit a wall with memory limits if you're doing complex joins. Last month I was working on a massive retail dataset and the internal sandbox just kept timing out during a heavy pivot table operation. If you're hitting those limits, I've had much better luck using external environments that integrate with LLMs. The Notable Jupyter Notebook Plugin was my go-to for a long time because it actually lets you work in a persistent notebook where the data doesn't just vanish after the session. For strictly mathematical or structured data queries, the Wolfram Alpha Data Analysis Tool is unbeatable for precision, though it can be picky about formatting. Tbh, if your Q3 report is massive, you might want to look at the Julius AI Data Analysis Platform instead. It's built specifically for this and handles larger file uploads better than GPT's standard interface. Check out the pandas documentation for chunking if you decide to stick with the native tool—it's a lifesaver for memory management. I usually just tell the GPT to use chunksize when reading the CSV to stay under that 1GB RAM limit.