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      <title>Spotify Builds External Index to Enable Low Latency Point Queries on its Data Lake</title>
      <link>https://www.infoq.com/news/2026/08/spotify-data-lake-point-queries/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Spotify introduced  external indexing architecture for Apache Parquet data lakes that enables low-latency point queries without replicating datasets into operational databases. The approach maps lookup keys to Parquet files and row locations, allowing targeted reads from cloud object storage while supporting analytics, machine learning, AI applications, and online services from the same datasets.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Low Latency</category>
      <category>Data</category>
      <category>Distributed Systems</category>
      <category>Data Lake</category>
      <category>Software Engineering</category>
      <category>Google Cloud</category>
      <category>AI Architecture</category>
      <category>Big Data</category>
      <category>IndexedDB</category>
      <category>Apache</category>
      <category>Apache Iceberg</category>
      <category>Query</category>
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      <pubDate>Wed, 12 Aug 2026 14:26:00 GMT</pubDate>
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      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-08-12T14:26:00Z</dc:date>
      <dc:identifier>/news/2026/08/spotify-data-lake-point-queries/en</dc:identifier>
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    <item>
      <title>JioHotstar Explains the Distributed Engineering behind Personalized Ad Requests at Streaming Scale</title>
      <link>https://www.infoq.com/news/2026/08/jiohotstar-ad-decisioning-flow/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/jiohotstar-ad-decisioning-flow/en/headerimage/generatedHeaderImage-1785711356085.jpg"/&gt;&lt;p&gt;JioHotstar explains the distributed architecture behind its real-time ad request workflow, covering ad decisioning, waterfall tiering, pacing algorithms, latency optimization, and service coordination required to select and deliver personalized advertisements during streaming playback at scale.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Low Latency</category>
      <category>Streaming Video</category>
      <category>Distributed Systems</category>
      <category>Video Codec</category>
      <category>Technology</category>
      <category>Advertising</category>
      <category>Architecture</category>
      <category>Algorithms</category>
      <category>Microservices</category>
      <category>Data-Driven Decision Making Series</category>
      <category>Decision Trees</category>
      <category>Streaming</category>
      <category>Scalability</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Wed, 05 Aug 2026 14:09:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/jiohotstar-ad-decisioning-flow/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Low+Latency</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-08-05T14:09:00Z</dc:date>
      <dc:identifier>/news/2026/08/jiohotstar-ad-decisioning-flow/en</dc:identifier>
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