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UID:pretalx-pydata-amsterdam2026-MKAK8X@pretalx.com
DTSTART;TZID=CET:20260911T110500
DTEND;TZID=CET:20260911T113500
DESCRIPTION:For data engineers and data scientists building Python pipeline
 s workloads can hit the awkward middle: too large to handle comfortably wi
 th pandas on a single machine\, too small for a Spark cluster to justify t
 he operational cost. The usual choice is *overprovisioned single machines*
  on one end\, or accept *cluster maintenance and a second DataFrame API*.\
 n\nThis talk argues that **you don't need to choose**.  Polars' new stream
 ing engine is the common core behind high performance single node processi
 ng with spill-to-disk capabilities\, and distributed execution. The same A
 PI scales with your data. \n\nAttendees will leave with actionable insight
 s that help decide: **when vertical scaling and spill-to-disk are enough**
  (which gets you further than most assume)\, **when distributed computing 
 is actually justified**\, and technical knowledge of how Polars accomplish
 es both.\n\nWe'll follow one query across different dataset sizes\, go thr
 ough the technical challenges we overcame\, and teach you to decide your o
 ptimal choice for each scenario.
DTSTAMP:20260710T141320Z
LOCATION:Room 1 (170)
SUMMARY:The New Polars Engine That Tackles Megabyte to Terabyte Workloads -
  Thijs Nieuwdorp
URL:https://pretalx.com/pydata-amsterdam2026/talk/MKAK8X/
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