<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ETL on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/etl/</link><description>Recent content in ETL on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 26 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/etl/index.xml" rel="self" type="application/rss+xml"/><item><title>Integrating OpenZL with Existing Data Workflows</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/integrating-openzl-existing-workflows/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/integrating-openzl-existing-workflows/</guid><description>&lt;h2 id="integrating-openzl-with-existing-data-workflows"&gt;Integrating OpenZL with Existing Data Workflows&lt;/h2&gt;
&lt;p&gt;Welcome back, aspiring data architect! In the previous chapters, we laid the groundwork by understanding what OpenZL is, how to set it up, and its core concepts like codecs, graphs, and compression plans. Now, it&amp;rsquo;s time to bridge the gap between theory and practice: how do you actually weave OpenZL into your existing data processing pipelines?&lt;/p&gt;
&lt;p&gt;This chapter will guide you through the practical aspects of integrating OpenZL. You&amp;rsquo;ll learn where OpenZL fits best within typical data workflows, how to define your data&amp;rsquo;s structure for OpenZL, and how to apply compression plans programmatically. By the end, you&amp;rsquo;ll have a solid understanding of how to leverage OpenZL to optimize storage and improve performance for your structured datasets. Get ready to transform your data pipelines!&lt;/p&gt;</description></item></channel></rss>