<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Big Data Processing on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/big-data-processing/</link><description>Recent content in Big Data Processing 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/big-data-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Parallel Compression and Distributed Systems</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/parallel-compression-distributed-systems/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/parallel-compression-distributed-systems/</guid><description>&lt;h2 id="introduction-to-parallel-compression-and-distributed-systems-with-openzl"&gt;Introduction to Parallel Compression and Distributed Systems with OpenZL&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid data explorer! In our journey through the fascinating world of OpenZL, we&amp;rsquo;ve learned how to craft intelligent compression plans and apply them to various data formats. But what happens when your data isn&amp;rsquo;t just large, but &lt;em&gt;enormous&lt;/em&gt;? What if it resides across many machines in a vast data lake? That&amp;rsquo;s where the power of parallel compression and distributed systems comes into play.&lt;/p&gt;</description></item></channel></rss>