Reviewed by Aditya Kumar · Last reviewed 2026-03-24
(1) Restart kernel to avoid stale state. (2) Limit data—sample, filter early, lazy eval. (3) Cache intermediate with .cache() or @lru_cache. (4) Profile with %%time, %%memit, Spark UI. (5) del/gc.collect() to free memory. (6) Spark/Polars for big data. (7) Parameterize...
(1) Restart kernel to avoid stale state. (2) Limit data—sample, filter early, lazy eval. (3) Cache intermediate with .cache() or @lru_cache. (4) Profile with %%time, %%memit, Spark UI. (5) del/gc.collect() to free memory. (6) Spark/Polars for big data. (7) Parameterize (Papermill, widgets) for automation. (8) Refactor to production scripts. WHY: Notebooks are for exploration; production needs reproducibility and scheduling. COST: Large retained objects increase cluster cost; cache wisely. TRADE-OFF: Notebooks vs. scripts—notebooks for iteration; promote to dbt/Python pipelines for prod.
Red Flag: Running notebooks in production as-is. Pro-Move: 'We have a promotion path—notebook → Python module → Airflow DAG; enforce no Pandas for jobs >1GB.'
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