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      <title>Netflix Serves 84% of Query Results from Cache with Interval-Aware Caching in Apache Druid</title>
      <link>https://www.infoq.com/news/2026/05/netflix-druid-interval-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Data+Analytics</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/netflix-druid-interval-cache/en/headerimage/generatedHeaderImage-1777092326529.jpg"/&gt;&lt;p&gt;Netflix improves Apache Druid performance with interval aware caching, serving 84% of analytics results from cache and reducing query load by 33%. The system decomposes rolling window queries into reusable time segments, enabling partial cache reuse and recomputation only for recent data. At scale, it reduces scan volume, improves P90 latency, and optimizes real time analytics workloads.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Distributed Systems</category>
      <category>Data Analytics</category>
      <category>Observability</category>
      <category>Optimization</category>
      <category>Apache</category>
      <category>Time Series Data</category>
      <category>Caching</category>
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      <pubDate>Mon, 11 May 2026 14:36:00 GMT</pubDate>
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      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-11T14:36:00Z</dc:date>
      <dc:identifier>/news/2026/05/netflix-druid-interval-cache/en</dc:identifier>
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    <item>
      <title>LinkedIn Consolidates Hiring Data Pipelines to Power AI Driven Talent Systems</title>
      <link>https://www.infoq.com/news/2026/05/linkedin-unified-hiring-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Data+Analytics</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/linkedin-unified-hiring-platform/en/headerimage/generatedHeaderImage-1776925266106.jpg"/&gt;&lt;p&gt;LinkedIn introduced a unified integrations platform to standardize and reconcile hiring data across systems. The platform reduces onboarding time by 72%, improves data consistency and completeness, and enables scalable AI-driven hiring features through standardized schemas, orchestration workflows, and centralized data processing.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Integration</category>
      <category>Unification</category>
      <category>Data Pipelines</category>
      <category>Data Analytics</category>
      <category>Platforms</category>
      <category>Evolutionary Architecture</category>
      <category>Hiring</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 06 May 2026 14:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/linkedin-unified-hiring-platform/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Data+Analytics</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-06T14:15:00Z</dc:date>
      <dc:identifier>/news/2026/05/linkedin-unified-hiring-platform/en</dc:identifier>
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