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Learn Enough Docker to be Useful

Part 1: The Conceptual Landscape

Jeff Hale
Towards Data Science
7 min readJan 9, 2019

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Containers are hugely helpful for improving security, reproducibility, and scalability in software development and data science. Their rise is one of the most important trends in technology today.

Docker is a platform to develop, deploy, and run applications inside containers. Docker is essentially synonymous with containerization. If you’re a current or aspiring software developer or data scientist, Docker is in your future.

Don’t fret if you aren’t yet up to speed — this article will help you understand the conceptual landscape — and you’ll get to make some pizza along the way.

In the next five articles in this series we’ll jump into Docker terms, Dockerfiles, Docker images, Docker commands, and data storage. Part 2 is now live:

By the end of the series (and with a little practice) you should know enough Docker to be useful 😃!

Docker Metaphors

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Towards Data Science
Towards Data Science

Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Jeff Hale
Jeff Hale

Written by Jeff Hale

I write about data things. Follow me on Medium and join my Data Awesome mailing list to stay on top of the latest data tools and tips: https://dataawesome.com

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