<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reliability on Company Engineering</title><link>https://company-engineering.pages.dev/tags/reliability/</link><description>Recent content in Reliability on Company Engineering</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 01 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://company-engineering.pages.dev/tags/reliability/index.xml" rel="self" type="application/rss+xml"/><item><title>Designing Reliable Data Synchronization at Scale</title><link>https://company-engineering.pages.dev/posts/designing-reliable-data-synchronization-at-scale/</link><pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate><guid>https://company-engineering.pages.dev/posts/designing-reliable-data-synchronization-at-scale/</guid><description>&lt;p&gt;Keeping data consistent across systems we don&amp;rsquo;t control is one of the hardest
problems we work on. Every external integration is a small distributed system:
networks fail, third-party APIs rate-limit us, and the &amp;ldquo;source of truth&amp;rdquo; is
often whichever side wrote last. This post covers the patterns we lean on to
make synchronization predictable rather than heroic.&lt;/p&gt;
&lt;h2 id="the-shape-of-the-problem"&gt;The shape of the problem&lt;/h2&gt;
&lt;p&gt;A sync flow looks deceptively simple: read from a source, transform, write to a
destination. In practice each step can fail independently, and the same change
can arrive more than once. We design every flow around three assumptions:&lt;/p&gt;</description></item></channel></rss>