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UID:pretalx-adass2026-GNKDSF@pretalx.adass.org
DTSTART;TZID=AWST:20261104T141500
DTEND;TZID=AWST:20261104T143000
DESCRIPTION:We archive massive datasets to map the stars in ESA’s Gaia mi
 ssion\, unlock the secrets of dark matter and dark energy with Euclid\, an
 d discover new worlds with PLATO. Over the past few years\, these workload
 s ran on Greenplum\, but when Greenplum stopped being open source\, we had
  to evaluate all available options—open source or not—exploring new di
 rections.\nIn this talk\, we’ll share how we’re rethinking architectur
 e\, tools\, and operations to handle petabyte-scale astronomy workloads. W
 e’ll cover the practical pros and cons of the candidate solutions we eva
 luated\, the administrative shifts required\, and the hurdles we face movi
 ng such critical operations between engines.\nWe have selected a solution 
 that offers both Open Source and Enterprise support options. We'll discuss
  how this option emerged as the best fit for our petabyte-scale workloads\
 , including its ability to handle complex cross-match queries essential fo
 r astronomical research.\nAttendees will leave with real-world lessons fro
 m our migration journey\, including performance benchmarks from Gaia catal
 og queries\, operational considerations for distributed PostgreSQL at scal
 e\, and key questions to ask before starting a migration of their own.
DTSTAMP:20261001T111508Z
LOCATION:Banquet Hall
SUMMARY:From Stars to Storage Engines: Migrating Big Science Workloads Beyo
 nd Greenplum - Joaquim Oliveira
URL:https://pretalx.adass.org/adass2026/talk/GNKDSF/
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