Introduction to Containers for Data Science / Data Engineering


In this hands on session, participants will learn how to leverage containers for data science / data engineering workflows. Containers allows us to bundle our application dependencies and configuration into an image, which can be more easily shared with others and deployed to the cloud. The session will explain how to use and build images, configure and run them and handle inter-dependencies between the product you're building and other services such as databases.

Specifics covered:
- how containers work
- what are their advantages in data science / data engineering
- finding images on repositories (docker hub /
- creating containers from the image and running it
- exposing resources like ports and volumes
- orchestration with docker-compose
- building / configuring / customizing images to include your specific project dependencies
- integrating your container with the visual studio code editors
- containerizing dependent services like databases and integrating them with your project

Source code from the workshop will be available for attendees on github.


Michael Fudge is a Professor of Practice and program director for the Master’s degree in Information Systems at Syracuse University’s iSchool ( Michael brings decades of work experience in IT into the classroom, specifically in areas of infrastructure, software development, databases, and data warehousing. Michael maintains a Jupyterhub-on-Kubernetes infrastructure to support courses in data science and programming. He has a true passion for teaching and sharing his knowledge and experiences with others.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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