<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Batch-Processing on Pi Stack</title><link>https://www.pistack.xyz/tags/batch-processing/</link><description>Recent content in Batch-Processing on Pi Stack</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 21 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.pistack.xyz/tags/batch-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Volcano vs YuniKorn vs Kueue: Best Kubernetes Batch Scheduler 2026</title><link>https://www.pistack.xyz/posts/volcano-vs-yunikorn-vs-kueue-kubernetes-batch-scheduler-guide-2026/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://www.pistack.xyz/posts/volcano-vs-yunikorn-vs-kueue-kubernetes-batch-scheduler-guide-2026/</guid><description>&lt;p>&lt;a href="https://kubernetes.io/">kubernetes&lt;/a> was designed primarily for long-running services, not batch workloads. The default scheduler makes no distinction between a web server that needs to stay up 24/7 and a data processing job that should run to completion and exit. For organizations running high-performance computing (HPC), machine learning training, ETL pipelines, or any job-queueing workload on self-hosted Kubernetes clusters, the default scheduler falls short.&lt;/p></description></item></channel></rss>