<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Batch Processing on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/batch-processing/</link><description>Recent content in Batch Processing on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 19 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/batch-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Real-time Data with Structured Streaming</title><link>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/structured-streaming/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/structured-streaming/</guid><description>&lt;h2 id="introduction-the-pulse-of-real-time-data"&gt;Introduction: The Pulse of Real-time Data&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 8! So far, we&amp;rsquo;ve mastered processing vast amounts of historical data using Spark DataFrames, transforming and analyzing it at scale. But what if your data isn&amp;rsquo;t static? What if new information arrives constantly, and you need to react to it &lt;em&gt;now&lt;/em&gt;? Think about monitoring sensor data, tracking website clicks, or processing financial transactions as they happen. This is where the magic of real-time data processing comes in!&lt;/p&gt;</description></item></channel></rss>