Throughout this course, you'll explore virtualization, containerization, and Kubernetes, mastering the very tools that power data engineering in the industry. Each week presents a new set of tools and platforms that are indispensable in data engineering. From mastering Docker and Kubernetes to exploring advanced topics such as AI-driven coding with GitHub Copilot, efficient container image management with Azure and Amazon Elastic Container Registries, and Site Reliability Engineering (SRE) practices, you'll go beyond the basics and acquire the expertise needed to thrive in the dynamic and data-driven landscape of advanced data engineering. Whether you're a current student looking to expand your skills or a working professional aiming to take your expertise to the next level, this course is tailored to equip you with the advanced knowledge and hands-on experience necessary for success.

Virtualization, Docker, and Kubernetes for Data Engineering

Virtualization, Docker, and Kubernetes for Data Engineering
This course is part of Applied Python Data Engineering Specialization



Instructors: Noah Gift
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What you'll learn
Master virtualization, containerization, and Docker, including Dockerfile creation and multi-container orchestration with Compose and Airflow.
Develop expertise in Kubernetes core concepts, cluster architecture, and deployment using cloud environments, GitHub Codespaces, and AI-driven tools.
Navigate data scenarios mastering containerization, deploying apps, and addressing production issues with cloud orchestration and SRE practices.
Skills you'll gain
Tools you'll learn
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There are 4 modules in this course
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Reviewed on Feb 18, 2024
It was great but the last module of week 4 felt isolated from the rest of the course.
Reviewed on Mar 19, 2025
For the first time I really understood what "virtualization" is and how things sort of expand for larger and larger units. Really solid stuff on CI/CD stuff also.
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