<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>数据仓库 on PlumePHP</title><link>https://plumephp.com/tags/%E6%95%B0%E6%8D%AE%E4%BB%93%E5%BA%93/</link><description>Recent content in 数据仓库 on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://plumephp.com/tags/%E6%95%B0%E6%8D%AE%E4%BB%93%E5%BA%93/index.xml" rel="self" type="application/rss+xml"/><item><title>数据仓库建模深度指南：从 Kimball 到 Data Mesh</title><link>https://plumephp.com/data-warehouse-modeling/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0800</pubDate><guid>https://plumephp.com/data-warehouse-modeling/</guid><description>&lt;p&gt;数据仓库建模是分析体系的基石。恰当的建模方法直接决定数仓的查询性能、扩展能力与维护成本。本文系统梳理五种主流方法论：Kimball 维度建模、Data Vault 2.0、Data Mesh、Anchor Modeling 与 OBT，从理论到 SQL 实现，辅以对比分析与 FAQ，帮助为不同场景选择合适的建模策略。&lt;/p&gt;</description></item></channel></rss>