BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//pycones-2026//talk//9JZRKJ
BEGIN:VTIMEZONE
TZID:Europe/Madrid
BEGIN:STANDARD
DTSTART:20251107T000000
TZNAME:CET
TZOFFSETFROM:+0100
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20260329T030000
RDATE:20270328T030000
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20261025T030000
RDATE:20271031T030000
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:Datanomy: Understanding the Anatomy of Arrow and Parquet - Raúl C
 umplido Domínguez
DTSTART;TZID=Europe/Madrid:20261107T123000
DTEND;TZID=Europe/Madrid:20261107T131000
DTSTAMP:20260726T231356Z
UID:pretalx-pycones-2026-9JZRKJ@pretalx.com
DESCRIPTION:Apache Arrow and Apache Parquet are the de-facto standards for
  in-memory and file formats for columnar data. We use them on our data wor
 kflows daily\, sometimes even without noticing. This talk will demystify t
 hese formats by going over the specifications and showing some real world 
 examples of how the data actually looks on our systems.\n\nFor Arrow\, we 
 will briefly mention some of the batteries included for exchanging data li
 ke Arrow IPC\, Arrow Flight or Arrow ADBC and how they relate to the core 
 In-memory format.\n\nThe main libraries shown will be PyArrow with its Arr
 ow and Parquet implementation and datanomy a new tool created for visualiz
 ing data formats.
LOCATION:Track 05
URL:https://pretalx.com/pycones-2026/talk/9JZRKJ/
END:VEVENT
END:VCALENDAR