BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//euroscipy-2026//talk//U9TNWY
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-euroscipy-2026-U9TNWY@pretalx.com
DTSTART;TZID=CET:20260721T160000
DTEND;TZID=CET:20260721T162000
DESCRIPTION:During this comprehensive talk\, we will discuss how to optimiz
 e your spatial data processing using Apache Sedona\, a distributed process
 ing engine\, and SedonaDB\, a powerful data fusion-based database that tre
 ats spatial data as a first-class citizen. In this talk\, you will underst
 and how to optimize:\n- Distributed and non-distributed spatial join\n- Ho
 w to optimize spatial partitioning and reduce data skew\n- How to leverage
  Spatial Apache Parquet and Geoparquet to efficiently store and retrieve d
 ata \n- Optimizing Apache Sedona Python applications to be more performant
  and consume less memory\, incorporating Apache Arrow and SedonaDB\n- Powe
 rful indexing techniques\n- Distributed K-nearest neighbor algorithm\n\nI 
 will explain why the knowledge of optimization patterns is important and h
 ow understanding Apache Sedona's Python limitations is crucial to making y
 our spatial data pipelines robust and efficient. The last part is to expla
 in when use Apache Sedona and where SedonaDB fits.
DTSTAMP:20260603T201737Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:Optimize the geospatial data processing with Apache Sedona and Sedo
 naDB. - Paweł Tokaj
URL:https://pretalx.com/euroscipy-2026/talk/U9TNWY/
END:VEVENT
END:VCALENDAR