<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Advanced Queries on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/advanced-queries/</link><description>Recent content in Advanced Queries on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 19 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/advanced-queries/index.xml" rel="self" type="application/rss+xml"/><item><title>Advanced Data Manipulation with Spark SQL</title><link>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/advanced-data-manipulation-spark-sql/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/advanced-data-manipulation-spark-sql/</guid><description>&lt;h2 id="introduction-unlocking-deeper-insights-with-spark-sql"&gt;Introduction: Unlocking Deeper Insights with Spark SQL&lt;/h2&gt;
&lt;p&gt;Welcome back, data explorer! In our previous chapters, you&amp;rsquo;ve mastered the fundamentals of setting up your Databricks environment, loading data, and performing basic queries with Spark SQL. You&amp;rsquo;ve seen how powerful SQL can be for interacting with your data lakehouse. But what if your data questions become more complex? What if you need to calculate moving averages, rank items within groups, or break down a massive query into more manageable parts?&lt;/p&gt;</description></item></channel></rss>