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    <title>InfoQ - Cloud Computing</title>
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      <title>Presentation: Beyond Speed Limits: Exploring the Performance Power of Valkey</title>
      <link>https://www.infoq.com/presentations/valkey-datastore/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</link>
      <description>&lt;img src="https://res.infoq.com/presentations/valkey-datastore/en/mediumimage/viktor-vedmich-medium-1780058022702.jpeg"/&gt;&lt;p&gt;Senior Solution Architect Viktor Vedmich shares how engineering leaders can maximize application performance using Valkey. He discusses the open-source Redis fork's 100% API compatibility, explores advanced caching strategies like lazy loading, and explains how to implement powerful data structures for real-time analytics, rate limiting, and session stores to solve the thundering herd problem.&lt;/p&gt; &lt;i&gt;By Viktor Vedmich&lt;/i&gt;</description>
      <category>Key-Value Store</category>
      <category>InfoQ Dev Summit Munich 2025</category>
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      <category>AWS</category>
      <category>Data</category>
      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Mon, 08 Jun 2026 10:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/valkey-datastore/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</guid>
      <dc:creator>Viktor Vedmich</dc:creator>
      <dc:date>2026-06-08T10:15:00Z</dc:date>
      <dc:identifier>/presentations/valkey-datastore/en</dc:identifier>
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    <item>
      <title>Article: The Technology Adoption Curve, Twenty Years On</title>
      <link>https://www.infoq.com/articles/adoption-curve-twenty/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</link>
      <description>&lt;img src="https://res.infoq.com/articles/adoption-curve-twenty/en/headerimage/generatedHeaderImage-1780670505517.jpg"/&gt;&lt;p&gt;Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.&lt;/p&gt; &lt;i&gt;By Renato Losio, Dio Synodinos&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Machine Learning</category>
      <category>Generative AI</category>
      <category>Technology Trends</category>
      <category>Java</category>
      <category>Agile</category>
      <category>Kubernetes</category>
      <category>Software Engineering</category>
      <category>Microservices</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Mon, 08 Jun 2026 08:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/adoption-curve-twenty/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</guid>
      <dc:creator>Renato Losio, Dio Synodinos</dc:creator>
      <dc:date>2026-06-08T08:30:00Z</dc:date>
      <dc:identifier>/articles/adoption-curve-twenty/en</dc:identifier>
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    <item>
      <title>Article: Two Misconfigurations That Caused Spark OOM Failures on Kubernetes</title>
      <link>https://www.infoq.com/articles/spark-oom-kubernetes-misconfigurations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</link>
      <description>&lt;img src="https://res.infoq.com/articles/spark-oom-kubernetes-misconfigurations/en/headerimage/spark-oom-kubernetes-misconfigurations-header-1780044756757.jpg"/&gt;&lt;p&gt;After migrating Spark pipelines to Azure Kubernetes Service, two infrastructure settings interacted destructively: spark.kubernetes.local.dirs.tmpfs=true backed shuffle spill with RAM instead of disk, and a hard podAffinity rule forced all executors onto one node. Together, they caused repeated OOM kills invisible to standard diagnostics.&lt;/p&gt; &lt;i&gt;By Pranav Bhasker&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Apache Spark</category>
      <category>Kubernetes</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Wed, 03 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/spark-oom-kubernetes-misconfigurations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Cloud+Computing</guid>
      <dc:creator>Pranav Bhasker</dc:creator>
      <dc:date>2026-06-03T09:00:00Z</dc:date>
      <dc:identifier>/articles/spark-oom-kubernetes-misconfigurations/en</dc:identifier>
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