Writing a data pipeline is only the beginning. Getting that pipeline safely into production and keeping it reliable is where many data teams struggle.

In this hands-on workshop, we'll take a simple Python-based data pipeline and progressively apply DevOps practices to turn it into a production-ready data workflow. We'll explore what changes when a data pipeline becomes software that a team needs to deploy, test, monitor, and maintain.

Participants will work through practical examples covering version control, automated testing, CI/CD, environment management, data-quality validation, deployment automation, and observability. We'll also look at how to catch problems before they reach production and how to make failures visible when they inevitably occur.

Rather than treating DataOps as another collection of tools, the workshop focuses on the engineering principles behind it: automate repetitive work, test changes before deployment, make environments reproducible, validate what you're deploying, and monitor systems after they go live.

By the end of the workshop, participants will have a practical blueprint for taking a data pipeline from a developer's machine to a repeatable and observable production workflow and will understand how DevOps practices can make data teams faster without sacrificing reliability.