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PRODID:-//pretalx//global2024.pydata.org//YEDBAB
BEGIN:VEVENT
UID:pretalx-cfp-YEDBAB@global2024.pydata.org
DTSTART:20241203T143000Z
DTEND:20241203T150000Z
DESCRIPTION:Streamlining clinical trial output workflows is a key challenge
  in clinical studies. To deliver reports to health authorities\, clinical 
 trial statisticians need to create several scripts to produce deliverables
  such as output datasets\, tables\, figures\, and listings. Statisticians 
 must also handle specific execution orders to respect dependencies between
  the generated datasets.\n\nOur project leverages Python programming to au
 tomatically generate orchestration workflows from clinical trial project m
 etadata using the Snakemake framework. Snakemake supports the execution of
  multiple jobs using Docker containers\, facilitating multilingual orchest
 ration. This enables our users to run end-to-end (E2E) data engineering wo
 rkflows using their preferred programming languages\, primarily SAS and R.
  Moreover\, Snakemake allows parallel runs for efficient workflow manageme
 nt.
DTSTAMP:20250709T220233Z
LOCATION:AI/ML Track
SUMMARY:Enabling Multi-Language Programming in Data Engineering Workflows w
 ith the Snakemake Framework - Daphné Grasselly
URL:https://global2024.pydata.org/cfp/talk/YEDBAB/
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