<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Engineering on PlumePHP</title><link>https://plumephp.com/tags/data-engineering/</link><description>Recent content in Data-Engineering on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://plumephp.com/tags/data-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Kafka Connect CDC 实战：Debezium 数据同步与变更捕获</title><link>https://plumephp.com/kafka-connect-cdc/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://plumephp.com/kafka-connect-cdc/</guid><description>&lt;p&gt;在现代数据架构中，系统通常由数十个异构数据源组成。传统全量 ETL 在实时性和资源开销上越来越难满足需求。变更数据捕获（CDC）提供了一种在源数据发生变更时实时捕获并传播这些变更的机制，而 Kafka Connect 与 Debezium 的组合已成为业界最受欢迎的 CDC 解决方案之一。&lt;/p&gt;</description></item></channel></rss>