Why Scala Dominates Data Engineering
<p>Ever found yourself browsing through a Data Engineering job posting and wondered, “Why on earth do they want me to know Scala?” Or perhaps you’ve mused, “What on earth is Scala, anyway?”</p>
<p>Allow me to explain…</p>
<h2>Birth of Scala</h2>
<p>Scala is a high-level, statically typed, general-purpose programming language. Developed in 2003 as a comeback to Java criticism, Scala can be compiled to Java bytecode and run on Java Virtual Machine (JVM). Because of this, Scala allows for interoperability with Java libraries, which can be directly referenced in code. Scala’s like that cool bilingual friend who effortlessly switches between languages.</p>
<p>Scala’s mission? To eliminate boilerplate code. Java likes to babble on and on but Scala believes in keeping it concise. And fewer lines of code mean quicker development and speedier deliveries. Scala even spices things up by blending some functional programming concepts with Java’s object-oriented nature. It’s like the peanut butter and jelly of programming paradigms.</p>
<p>While Scala might seem deceptively simple, it introduces some complex features that can leave even seasoned programmers scratching their heads. With fewer lines of code, you need to be better at detecting what the code does and not just blindly follow it. It’s all about predicting what’s happening under the hood.</p>
<p>So, there you have it, a sneak peek into the world of Scala. Let’s dive deeper.</p>
<h2>It’s all about Spark</h2>
<p>In the realm of data engineering, one framework stands out, and most Data Engineers nod in agreement: <strong>Apache Spark</strong> reigns supreme in the kingdom of big data processing (well, for now at least — data tech evolves faster than a chameleon at a disco). Think of it as the Gandalf of open-source frameworks, wielding its magic for massive data munching.</p>
<p><a href="https://medium.com/@henrihapponen/why-scala-still-dominates-data-engineering-fd0fd519cf1">Website</a></p>