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UID:pretalx-pydata-amsterdam2026-ADK8J8@pretalx.com
DTSTART;TZID=CET:20260911T115000
DTEND;TZID=CET:20260911T122000
DESCRIPTION:Delta Lake and Apache Iceberg solved data lake chaos but introd
 uced a new one: layers of JSON metadata scattered across object storage. D
 uckLake is an open table format that challenges the status quo by storing 
 all catalog metadata in a plain SQL database rather than a sprawling file 
 system.\nIn this talk we will explore DuckLake's architecture\, which coup
 les the low-cost storage of S3 with the transactional guarantees of a stan
 dard SQL database. Using only Python and DuckDB — no Spark cluster requi
 red — we will demonstrate how to build a fully ACID-compliant lakehouse\
 , complete with schema management\, time travel\, and concurrent writers.\
 nWe will also candidly discuss where DuckLake falls short: the single-writ
 er bottleneck\, the scale inflection point where a distributed catalog bec
 omes necessary\, and the ecosystem maturity trade-offs of adopting a newer
  format over established giants.\nAudience: data engineers\, analytics eng
 ineers\, and data scientists working with lakehouse architectures or curio
 us about lightweight alternatives to Iceberg/Delta.\nTakeaways: understand
  the "metadata-in-database" pattern\, learn to deploy a serverless multi-u
 ser lakehouse with the Python ecosystem\, and gain a pragmatic framework f
 or choosing between lightweight and enterprise-grade approaches.
DTSTAMP:20260710T140722Z
LOCATION:Unconference
SUMMARY:DuckLake: The Lakehouse That Finally Embraces the Database - Grazia
 no Montanaro
URL:https://pretalx.com/pydata-amsterdam2026/talk/ADK8J8/
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