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UID:pretalx-europython-2024-9DSSHZ@programme.europython.eu
DTSTART;TZID=CET:20240711T153000
DTEND;TZID=CET:20240711T160000
DESCRIPTION:Caching data and calculation results in jupyter notebooks is a 
 great way to speed up development by making expensive cells easier to re-r
 un.\n\nData scientists and developers using notebooks on a daily basis\, c
 an improve their notebook workflow with low-effort changes in the notebook
  code\, cut the time spent waiting and reduce context switches.\n\nThis ta
 lk targets developers and data scientist of all experience levels and will
  cover:\n\nWhy caching in notebooks?\nSetting up the context in which deve
 lopers and data scientists use notebooks for exploratory work and how cach
 ing is relevant in it.\n\nWhat is caching\nQuick definition of caching\, i
 ntroducing the different types of persistence (in-memory\, on disk\, datab
 ase\, object storage …)\, cache invalidation strategies (parameters\, co
 de changes\, ttl\, …)\, with some cautionary comments about data securit
 y when caching protected data.\n\nCaching Techniques \nGoing through readi
 ly available options from the python standard library\, and how to use the
 m in notebooks. Introducing a few off-the-shelves options like ipython % m
 agics\, and cachetools.\nShowcasing how one would build their own mini-cac
 hing framework\, that fits for their specific use case\, using pandas and 
 spark for the example\nExplaining when to stop trying to cache\, and keepi
 ng the caching framework mini\, what are the signs that caching went overb
 oard.
DTSTAMP:20260617T072114Z
LOCATION:Terrace 2A
SUMMARY:Caching for Jupyter Notebooks - Lauris Jullien
URL:https://programme.europython.eu/europython-2024/talk/9DSSHZ/
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