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      <title>Uber Moves beyond CPU Cores with Performance-Aware Scheduling for Stateful Workloads</title>
      <link>https://www.infoq.com/news/2026/10/uber-cpu-stateful-workloads/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/uber-cpu-stateful-workloads/en/headerimage/generatedHeaderImage-1792576315045.jpg"/&gt;&lt;p&gt;Uber has introduced Normalized Compute Units (NCUs) to make CPU allocation more consistent across its highly heterogeneous server fleet.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
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      <category>Kubernetes</category>
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      <pubDate>Tue, 27 Oct 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/uber-cpu-stateful-workloads/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka-news</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-10-27T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/10/uber-cpu-stateful-workloads/en</dc:identifier>
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      <title>Pinterest Uses Event Time Watermarks to Finalize Petabyte Scale Data Partitions</title>
      <link>https://www.infoq.com/news/2026/10/pinterest-event-time-partition/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/pinterest-event-time-partition/en/headerimage/generatedHeaderImage-1791063877867.jpg"/&gt;&lt;p&gt;Pinterest has introduced event time-based partition finalization for its database ingestion framework. The mechanism uses Apache Flink to track event time and Apache Iceberg metadata to publish a non-regressing watermark, helping downstream jobs determine when continuously ingested CDC data is sufficiently complete for processing.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Change Data Capture</category>
      <category>Apache Kafka</category>
      <category>Data Partitioning</category>
      <category>Partitioning</category>
      <category>Data Lake</category>
      <category>Event Driven Architecture</category>
      <category>Apache Spark</category>
      <category>Streaming</category>
      <category>Apache Iceberg</category>
      <category>Apache Flink</category>
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      <pubDate>Mon, 19 Oct 2026 14:37:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/pinterest-event-time-partition/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Kafka-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-10-19T14:37:00Z</dc:date>
      <dc:identifier>/news/2026/10/pinterest-event-time-partition/en</dc:identifier>
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