<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Apache on jason grey</title><link>https://jason-grey.com/tags/apache/</link><description>Recent content in Apache on jason grey</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 13 Mar 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://jason-grey.com/tags/apache/index.xml" rel="self" type="application/rss+xml"/><item><title>Scaling Airflow Dataset Scheduling: Lessons from Common Crawl</title><link>https://jason-grey.com/posts/2025/airflow-at-scale/</link><pubDate>Thu, 13 Mar 2025 00:00:00 +0000</pubDate><guid>https://jason-grey.com/posts/2025/airflow-at-scale/</guid><description>&lt;h1 id="scaling-airflow-dataset-scheduling-lessons-from-common-crawl"&gt;
 Scaling Airflow Dataset Scheduling: Lessons from Common Crawl
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&lt;p&gt;Since joining &lt;a href="https://www.commoncrawl.org/" class="external-link" target="_blank" rel="noopener"&gt;Common Crawl&lt;/a&gt;, I&amp;rsquo;ve been involved with a number of activities revolving around indexing and cataloging the integrity of our data. To coordinate these activites, and to be able to re-run them upon dataset changes, I decided to give Airflow a try. The introduction of dataset-based scheduling in Airflow 2.4 seemed attractive, but it also comes with interesting challenges when working at our scale.&lt;/p&gt;</description></item></channel></rss>