<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Patterns - Tag - alpinegizmo.com</title><link>https://alpinegizmo.com/tags/patterns/</link><description>Patterns - Tag - alpinegizmo.com</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Fri, 10 Apr 2026 11:33:35 -0700</lastBuildDate><atom:link href="https://alpinegizmo.com/tags/patterns/" rel="self" type="application/rss+xml"/><item><title>Patterns in Stream Processing: Deduplication</title><link>https://alpinegizmo.com/posts/deduplication/</link><pubDate>Fri, 10 Apr 2026 11:33:35 -0700</pubDate><author>David Anderson</author><guid>https://alpinegizmo.com/posts/deduplication/</guid><description><![CDATA[<p>I generally recommend Flink SQL to developers starting a new stream processing project because it includes a powerful collection of built-in operators. These built-in operators can be composed together to satisfy the requirements of most use cases — provided you are able to see how to decompose your use case into a combination of these building blocks.</p>
<p>These built-in operators can do things like transforming, enriching, or correlating events, aggregating them in windows, and looking for patterns. It&rsquo;s common to perform several of these operations together in one application.</p>]]></description></item></channel></rss>