Docker Containerization: Turning 'Works on My Machine' Into a Reproducible Artif

<p>When I joined Ericsson's R&amp;D team as an intern, our development environment had a problem that every engineer on the team silently accepted as normal: spinning up a full

<p>"I don't understand, it literally works perfectly on my machine!" </p> <p>Every developer has uttered this phrase. It usually happens right after a deployment brings down the

<p>You spend hours building a feature, everything runs perfectly on your laptop, and you proudly push your code. Then a teammate pulls it… and suddenly nothing works.</p>

<h2> Prologue: The Initial Skepticism </h2> <p>I am a software developer with over 8 years of experience building products. Over the last few years, the name "Docker" has been

<p>If you've ever heard <em>"works on my machine"</em> — containers are the permanent fix to that problem.</p> <h2> TL;DR </h2> <ul> <li>A container bundles your app + libraries +

<h2> The Problem with Fat Images </h2> <p>We've all been there: you pull an image, run <code>docker images</code>, and see a 1.2GB monster staring back at you. That's not just

<p>Most people learn Docker by copying a <code>Dockerfile</code> that works, shipping it, and moving on. That's fine until the day a build that used to take twenty seconds

<p>Last October, a "minor" dependency bump in a PySpark job cost us four hours of downtime and roughly $12,000 in cloud compute credits. A developer had tested a new UDF locally