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UID:pretalx-coscup-2026-KGA3TE@pretalx.coscup.org
DTSTART;TZID=CST:20260808T141000
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DESCRIPTION:In modern data science\, the "Data Lakehouse" is a powerful way
  to manage large-scale data. However\, for many teams\, the "last mile" of
  integration—connecting Machine Learning platforms like Kubeflow to big 
 data processing engines like Apache Spark—remains a major technical chal
 lenge. In a shared Kubernetes cluster\, managing security\, user permissio
 ns\, and connection strings across different user profiles often leads to 
 manual errors and security risks.\n\nIn this session\, we will explore an 
 open-source solution to this problem using Juju and Charmed Operators. We 
 will walk through a "Multi-Integrator" architecture where each Kubeflow pr
 ofile receives its own secure connection to the data platform. Instead of 
 manual configuration\, we use automated integrators to inject Spark Servic
 e Accounts\, configurations\, and security secrets directly into user name
 spaces.\n\nBy using Charmed Spark and Charmed Kubeflow\, we can create a m
 odular system where data scientists can move from a simple notebook to a d
 istributed Spark job without worrying about the underlying YAML files or c
 redentials. We will discuss the logic of this design\, how it handles mult
 i-tenancy\, and how it simplifies the path to a production-ready data lake
 house on Ubuntu.\n\nMore info: https://events.canonical.com/event/146/cont
 ributions/951/
DTSTAMP:20260713T142446Z
LOCATION:AU
SUMMARY:From Big Data to ML: Integrating Spark and Kubeflow with Juju - Bik
 alpa Dhakal
URL:https://pretalx.coscup.org/coscup-2026/talk/KGA3TE/
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