Trim Memory Bills: Pandas Chunking, Dask, Polars Power ETL Without New Hardware
When RAM costs skyrocket, adding compute isn’t viable. The article shows how to process a 6‑million‑row, 200‑column social‑media dump using Pandas chunking, Dask scaling, and Polars lazy evaluation, avoiding out‑of‑memory errors and extra cloud spend. These techniques let data engineers squeeze more work out of existing clusters.