<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cap-Theorem on Distributed Data Insights</title><link>http://ddinsights.net/tags/cap-theorem/</link><description>Recent content in Cap-Theorem on Distributed Data Insights</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 07 Jun 2026 10:00:00 -0700</lastBuildDate><atom:link href="http://ddinsights.net/tags/cap-theorem/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Distributed Data Is Hard (and Worth It)</title><link>http://ddinsights.net/posts/why-distributed-data-is-hard/</link><pubDate>Sun, 07 Jun 2026 10:00:00 -0700</pubDate><guid>http://ddinsights.net/posts/why-distributed-data-is-hard/</guid><description>&lt;p>Every distributed data system is an answer to the same uncomfortable question:
&lt;em>what do you do when part of your system fails but the rest keeps running?&lt;/em> On a
single machine, a crash takes everything down together — clean, if catastrophic.
Across a network, failure is &lt;strong>partial&lt;/strong>, &lt;strong>ambiguous&lt;/strong>, and &lt;strong>constant&lt;/strong>.&lt;/p>
&lt;h2 id="the-three-things-the-network-takes-away">The three things the network takes away&lt;/h2>
&lt;p>When you split state across machines, you lose three guarantees you took for
granted on a single box:&lt;/p></description></item></channel></rss>