<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - Apache Beam - News</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Apache Beam News feed</description>
    <item>
      <title>Yelp Overhauls Its Streaming Architecture with Apache Beam and Apache Flink</title>
      <link>https://www.infoq.com/news/2024/04/yelp-streaming-apache-beam-flink/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Beam-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2024/04/yelp-streaming-apache-beam-flink/en/headerimage/generatedHeaderImage-1713604115642.jpg"/&gt;&lt;p&gt;Yelp reworked its data streaming architecture by employing Apache Beam and Apache Flink. The company replaced a fragmented set of data pipelines for streaming transactional data into its analytical systems, like Amazon Redshift and in-house data lake, using Apache data streaming projects to create a unified and flexible solution.&lt;/p&gt; &lt;i&gt;By Rafal Gancarz&lt;/i&gt;</description>
      <category>Data Pipelines</category>
      <category>Apache Flink</category>
      <category>Apache Kafka</category>
      <category>Streaming</category>
      <category>Event Stream Processing</category>
      <category>Apache Beam</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 22 Apr 2024 07:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2024/04/yelp-streaming-apache-beam-flink/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Beam-news</guid>
      <dc:creator>Rafal Gancarz</dc:creator>
      <dc:date>2024-04-22T07:00:00Z</dc:date>
      <dc:identifier>/news/2024/04/yelp-streaming-apache-beam-flink/en</dc:identifier>
    </item>
    <item>
      <title>QCon London: Lessons Learned from Building LinkedIn’s AI/ML Data Platform</title>
      <link>https://www.infoq.com/news/2024/04/linkedin-ai-platform-venicedb/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Beam-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2024/04/linkedin-ai-platform-venicedb/en/headerimage/generatedHeaderImage-1712832810337.jpg"/&gt;&lt;p&gt;At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. He specifically delved into Venice DB, the NoSQL data store used for feature persistence. The presenter shared the lessons learned from evolving and operating the platform, including cluster management and library versioning.&lt;/p&gt; &lt;i&gt;By Rafal Gancarz&lt;/i&gt;</description>
      <category>Apache Flink</category>
      <category>Low Latency</category>
      <category>MLOps</category>
      <category>Big Data</category>
      <category>Kubernetes</category>
      <category>Performance &amp; Scalability</category>
      <category>Apache Pinot</category>
      <category>Performance Tuning</category>
      <category>NoSQL</category>
      <category>QCon London 2024</category>
      <category>Apache Kafka</category>
      <category>Machine Learning</category>
      <category>Artificial Intelligence</category>
      <category>MySQL</category>
      <category>Apache Beam</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 15 Apr 2024 07:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2024/04/linkedin-ai-platform-venicedb/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Apache+Beam-news</guid>
      <dc:creator>Rafal Gancarz</dc:creator>
      <dc:date>2024-04-15T07:00:00Z</dc:date>
      <dc:identifier>/news/2024/04/linkedin-ai-platform-venicedb/en</dc:identifier>
    </item>
  </channel>
</rss>
