<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Spark on Portraits</title><link>https://jiangxt2.github.io/en/tags/spark/</link><description>Recent content in Spark on Portraits</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Thu, 17 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jiangxt2.github.io/en/tags/spark/index.xml" rel="self" type="application/rss+xml"/><item><title>Spark</title><link>https://jiangxt2.github.io/en/docs/spark/</link><pubDate>Thu, 17 Sep 2026 00:00:00 +0000</pubDate><guid>https://jiangxt2.github.io/en/docs/spark/</guid><description>A learning path through execution, shuffle, Spark SQL, and streaming.</description></item><item><title>Spark vs Daft: different data-processing models</title><link>https://jiangxt2.github.io/en/comparisons/spark-vs-daft/</link><pubDate>Thu, 17 Sep 2026 00:00:00 +0000</pubDate><guid>https://jiangxt2.github.io/en/comparisons/spark-vs-daft/</guid><description>Compare Spark and Daft across data abstractions, physical plans, ecosystem, and fit.</description></item><item><title>Spark vs Ray: batch processing and distributed applications</title><link>https://jiangxt2.github.io/en/comparisons/spark-vs-ray/</link><pubDate>Thu, 17 Sep 2026 00:00:00 +0000</pubDate><guid>https://jiangxt2.github.io/en/comparisons/spark-vs-ray/</guid><description>Understand Spark and Ray through computation models, scheduling, data work, and ecosystem boundaries.</description></item></channel></rss>