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SUMMARY:Optimize the geospatial data processing with Apache Sedona and Sed
 onaDB. - Paweł Tokaj
DTSTART;TZID=Poland:20260721T160000
DTEND;TZID=Poland:20260721T162000
DTSTAMP:20260911T013611Z
UID:pretalx-euroscipy-2026-U9TNWY@pretalx.com
DESCRIPTION:During this comprehensive talk\, we will discuss how to optimi
 ze your spatial data processing using Apache Sedona\, a distributed proces
 sing engine\, and SedonaDB\, a powerful data fusion-based database that tr
 eats spatial data as a first-class citizen. In this talk\, you will unders
 tand how to optimize:\n- Distributed and non-distributed spatial join\n- H
 ow to optimize spatial partitioning and reduce data skew\n- How to leverag
 e Spatial Apache Parquet and Geoparquet to efficiently store and retrieve 
 data \n- Optimizing Apache Sedona Python applications to be more performan
 t and consume less memory\, incorporating Apache Arrow and SedonaDB\n- Pow
 erful indexing techniques\n- Distributed K-nearest neighbor algorithm\n\nI
  will explain why the knowledge of optimization patterns is important and 
 how understanding Apache Sedona's Python limitations is crucial to making 
 your spatial data pipelines robust and efficient. The last part is to expl
 ain when use Apache Sedona and where SedonaDB fits.
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/U9TNWY/
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