<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scala on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/scala/</link><description>Recent content in Scala 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/scala/index.xml" rel="self" type="application/rss+xml"/><item><title>Introduction to Apache Spark on Databricks</title><link>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/introduction-apache-spark/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/introduction-apache-spark/</guid><description>&lt;h2 id="introduction-to-apache-spark-on-databricks"&gt;Introduction to Apache Spark on Databricks&lt;/h2&gt;
&lt;p&gt;Welcome back, aspiring data wizard! In our previous chapters, you&amp;rsquo;ve taken your first steps into the Databricks Lakehouse Platform, getting comfortable with its environment and setting up your workspace. Now, it&amp;rsquo;s time to dive into the heart of what makes Databricks so powerful for big data: &lt;strong&gt;Apache Spark&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;This chapter will introduce you to the fundamental concepts of Apache Spark, explaining why it&amp;rsquo;s the go-to engine for large-scale data processing and how Databricks supercharges it. We&amp;rsquo;ll explore Spark&amp;rsquo;s core abstractions, understand its architecture, and, most importantly, get our hands dirty writing our first Spark code in a Databricks notebook. Get ready to unlock the true potential of distributed computing!&lt;/p&gt;</description></item></channel></rss>