<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Schema Migration on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/schema-migration/</link><description>Recent content in Schema Migration on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 06 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/schema-migration/index.xml" rel="self" type="application/rss+xml"/><item><title>Evolving Your Schema: Versioned Migrations</title><link>https://ai-blog.noorshomelab.dev/mastering-dolt-guide/schema-evolution/</link><pubDate>Sat, 06 Jun 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/mastering-dolt-guide/schema-evolution/</guid><description>&lt;p&gt;Databases are rarely static. As applications evolve, so too must their underlying data structures. This process of changing a database&amp;rsquo;s schema – adding columns, creating new tables, modifying constraints – is known as &lt;strong&gt;schema evolution&lt;/strong&gt;. In traditional relational databases, this can be a perilous journey, often involving complex migration scripts, downtime, and a high risk of errors.&lt;/p&gt;
&lt;p&gt;This chapter dives into how Dolt transforms schema evolution from a high-stakes operation into a controlled, versioned, and collaborative process, much like managing code changes with Git. You&amp;rsquo;ll learn the core concepts of Dolt&amp;rsquo;s Git-for-Data approach applied to schemas, how to perform versioned migrations, and how to handle schema changes with confidence.&lt;/p&gt;</description></item></channel></rss>