<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Streaming on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/data-streaming/</link><description>Recent content in Data Streaming on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/data-streaming/index.xml" rel="self" type="application/rss+xml"/><item><title>Event-Driven Architectures: Reacting to Data in AI Systems</title><link>https://ai-blog.noorshomelab.dev/ai-system-design-2026-guide/event-driven-architectures-ai/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-system-design-2026-guide/event-driven-architectures-ai/</guid><description>&lt;h2 id="introduction-the-pulse-of-real-time-ai"&gt;Introduction: The Pulse of Real-time AI&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI architects! In our previous chapters, we explored the power of modularity with microservices and the art of coordinating complex tasks with orchestration. We learned how to break down monolithic AI systems into manageable, independent pieces and how to guide those pieces through their workflow.&lt;/p&gt;
&lt;p&gt;But what happens when your AI system needs to react &lt;em&gt;instantly&lt;/em&gt; to new information? What if you have a continuous stream of data, and your services need to process it without waiting for explicit requests or tightly coupled calls? How do you ensure that your recommendation engine updates in real-time as a user browses, or that your fraud detection system flags suspicious transactions as they happen?&lt;/p&gt;</description></item></channel></rss>