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      <title>Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads</title>
      <link>https://www.infoq.com/news/2026/09/dropbox-riviera-ai-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Data+Pipelines</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Asynchronous Architecture</category>
      <category>Tika</category>
      <category>plugins</category>
      <category>Apache</category>
      <category>Large language models</category>
      <category>Platform Engineering</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Distributed Systems</category>
      <category>Enterprise Content Management</category>
      <category>Architecture</category>
      <category>Data Pipelines</category>
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      <pubDate>Wed, 16 Sep 2026 14:42:00 GMT</pubDate>
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      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-16T14:42:00Z</dc:date>
      <dc:identifier>/news/2026/09/dropbox-riviera-ai-platform/en</dc:identifier>
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      <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=Data+Pipelines</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>Spring Boot</category>
      <category>Kubernetes Operator</category>
      <category>Optimization</category>
      <category>Data Pipelines</category>
      <category>Scalability</category>
      <category>Cloud Computing</category>
      <category>Kubernetes</category>
      <category>Event Stream Processing</category>
      <category>Distributed Systems</category>
      <category>Clusters</category>
      <category>Apache Kafka</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</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=Data+Pipelines</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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