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      <title>Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows</title>
      <link>https://www.infoq.com/news/2026/09/netflix-conductor-4-workflow/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/netflix-conductor-4-workflow/en/headerimage/generatedHeaderImage-1787938102526.jpg"/&gt;&lt;p&gt;Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Asynchronous Architecture</category>
      <category>Windows Workflow Foundation</category>
      <category>Orchestration</category>
      <category>Microservices</category>
      <category>Concurrency</category>
      <category>Cloud Architecture</category>
      <category>Java Operator SDK</category>
      <category>Scalability</category>
      <category>Netflix</category>
      <category>Apache Kafka</category>
      <category>Cassandra</category>
      <category>S3</category>
      <category>Distributed Systems</category>
      <category>ElasticSearch</category>
      <category>Workflow Foundation</category>
      <category>Apache Iceberg</category>
      <category>Workflow / BPM</category>
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      <pubDate>Fri, 11 Sep 2026 14:17:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/netflix-conductor-4-workflow/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-11T14:17:00Z</dc:date>
      <dc:identifier>/news/2026/09/netflix-conductor-4-workflow/en</dc:identifier>
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    <item>
      <title>Netflix Moves toward Open Source Flink Autoscaler for 30,000+ Streaming Jobs</title>
      <link>https://www.infoq.com/news/2026/09/netflix-flink-autoscaler/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/netflix-flink-autoscaler/en/headerimage/generatedHeaderImage-1787937137575.jpg"/&gt;&lt;p&gt;Netflix is moving toward the open-source Apache Flink Autoscaler for more than 30,000 streaming jobs across multiple AWS regions. The operator-level approach addresses limitations of Netflix’s cluster level autoscaler for complex, stateful pipelines. Netflix reports a 58% reduction in annualized Flink compute expenditure for one team, saving approximately $1.1 million annually.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Temporal Patterns</category>
      <category>Apache</category>
      <category>Apache Flink</category>
      <category>Data Pipelines</category>
      <category>Optimization</category>
      <category>Kubernetes Operator</category>
      <category>Spring Boot</category>
      <category>Event Stream Processing</category>
      <category>Scalability</category>
      <category>Apache Kafka</category>
      <category>Cloud Computing</category>
      <category>Kubernetes</category>
      <category>Clusters</category>
      <category>Distributed Systems</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 07 Sep 2026 14:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/netflix-flink-autoscaler/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-07T14:06:00Z</dc:date>
      <dc:identifier>/news/2026/09/netflix-flink-autoscaler/en</dc:identifier>
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