nabuskey commented on code in PR #193:
URL: https://github.com/apache/datafusion-site/pull/193#discussion_r3350393453
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content/blog/2026-06-10-comet-eks.md:
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@@ -0,0 +1,111 @@
+---
+layout: post
+title: What two months with the Comet community got our Spark workload on
Amazon EKS
+date: 2026-06-10
+author: Manabu McCloskey (AWS), Vara Bonthu (AWS), Andy Grove
+categories: [performance]
+summary: Apache DataFusion Comet now runs a 3TB TPC-DS workload significantly
faster than vanilla Spark 3.5.8 on Amazon EKS. This post covers what changed in
Comet over the past two releases, and how collaboration between EKS users and
maintainers shaped that work. <br></br> <img
src="/blog/images/comet-eks/version-arc-slope.png" width="60%"
class="img-fluid" alt="TPC-DS performance arc across Comet versions"/>
+---
+
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
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+{% endcomment %}
+-->
+
+[TOC]
+
+
+## Introduction
+
+Apache Spark workloads are some of the most common and cost-intensive jobs
running on Kubernetes. Our customers at AWS are always on the lookout for
meaningful speedups, since any speedup translates directly into lower bills.
That's what got us interested in Apache DataFusion Comet. The local TPC-DS and
TPC-H benchmarks looked very good, and we wanted to see how Comet would hold up
on a more realistic setup.
Review Comment:
done
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