We may earn an affiliate commission when you visit our partners.
Noah Gift
and
Kennedy Behrman
Dive into the world of virtualization, containerization, and orchestration for data engineering:
- Understand virtualization fundamentals and work with virtual machines
- Explore Docker containers and build scalable microservices
- Orchestrate containers using Kubernetes and cloud platforms
- Utilize cloud development environments like GitHub Codespaces
- Learn production best practices, including monitoring, testing, and CI/CD
Read more
Dive into the world of virtualization, containerization, and orchestration for data engineering:
- Understand virtualization fundamentals and work with virtual machines
- Explore Docker containers and build scalable microservices
- Orchestrate containers using Kubernetes and cloud platforms
- Utilize cloud development environments like GitHub Codespaces
- Learn production best practices, including monitoring, testing, and CI/CD
Read more
Dive into the world of virtualization, containerization, and orchestration for data engineering:
- Understand virtualization fundamentals and work with virtual machines
- Explore Docker containers and build scalable microservices
- Orchestrate containers using Kubernetes and cloud platforms
- Utilize cloud development environments like GitHub Codespaces
- Learn production best practices, including monitoring, testing, and CI/CD
Gain practical experience with industry-standard tools and techniques. Develop the skills to build, deploy, and manage containerized data solutions at scale. Whether you're a student or data professional, level up your data engineering capabilities.
Register for this course and see more details by visiting:
OpenCourser.com/course/eaxo09/virtualization
What's inside
Learning objectives
- Virtualization concepts and virtual machines
- Docker containers and microservices
- Kubernetes architecture and deployments
- Cloud development with github codespaces
- Container registries for kubernetes
- Cloud-based kubernetes solutions
- Production monitoring, testing, and ci/cd
- Virtualization concepts and virtual machines
- Docker containers and microservices
- Kubernetes architecture and deployments
- Cloud development with github codespaces
- Container registries for kubernetes
- Cloud-based kubernetes solutions
- Production monitoring, testing, and ci/cd
Syllabus
\- Module 1: Virtualization Theory and Concepts (6 hours to complete)
\- 8 videos (Total 26 minutes)
\- Virtualization (2 minutes)
\- Scaling Applications (1 minute)
Read more
Syllabus
\- Module 1: Virtualization Theory and Concepts (6 hours to complete)
\- 8 videos (Total 26 minutes)
\- Virtualization (2 minutes)
\- Scaling Applications (1 minute)
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Well-suited for students with some exposure to Python
Builds a strong foundation in Docker containers and Kubernetes orchestration
Develops hands-on experience with industry-standard tools like Docker and Kubernetes
Covers industry-relevant topics such as container orchestration and DevOps best practices
May require additional background knowledge in cloud computing or DevOps
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical containerization for data engineering
According to students, this course provides a strong foundation in virtualization, Docker, and Kubernetes specifically tailored for data engineering. Learners often highlight the practical, hands-on labs and real-world applicability of the content. While it offers comprehensive coverage of various tools like GitHub Codespaces, AWS, and GCP, some note the fast pace and potential setup challenges, suggesting it's most beneficial for those with some prior technical background. Overall, it's considered a valuable resource for professional skill development.
Pacing can be fast for beginners; some prior knowledge helps.
"As a beginner, some sections felt rushed, and I had to pause and do extra research."
"I think some prior familiarity with command line and basic cloud concepts is beneficial."
"The course is great if you have some existing tech background; otherwise, prepare for a steep curve."
Content strives to stay updated with evolving technologies.
"The mention of Codespaces and Copilot suggests the course tries to stay up-to-date with modern tools."
"In such a rapidly evolving field, I appreciate the effort to include current technologies."
"It feels relevant to what's used in industry right now for containerized data solutions."
Tailored concepts and tools for data engineering workflows.
"The course effectively shows how these technologies apply specifically to data engineering problems."
"I appreciated the examples that tied Docker and Kubernetes directly to data pipelines and MLOps."
"It's not just generic containerization; it's focused on data tasks, which is what I needed."
Covers a wide array of relevant data engineering tools.
"I found the course to be a great overview of virtualization, Docker, and Kubernetes for data tasks."
"It touches on many important aspects, from Dockerfiles to cloud deployments on AWS and GCP."
"The breadth of topics from CI/CD to SRE provides a holistic view for data professionals."
Provides crucial practical experience with core tools.
"The hands-on coding and projects are the strongest part of the course for me, solidifying concepts."
"I really appreciated the practical demos using Codespaces and Minikube, they made the concepts click."
"The exercises allowed me to apply what I learned immediately, which is essential for these topics."
Some learners faced difficulties with environment configuration.
"Getting the local environment set up was a bit frustrating, even with Codespaces mentioned."
"I encountered some issues with dependencies and versions during the initial Docker setup."
"While the course attempts to simplify setup, new users might still find it challenging."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Virtualization, Docker, and Kubernetes for Data Engineering with these
activities:
Review Virtualization
Show steps
Reviewing this topic will help you be prepared for the upcoming course on virtualization, containerization, and orchestration for data engineering.
Browse courses on
Virtualization
Show steps
-
Read an article about virtualization.
-
Watch a tutorial about virtualization.
-
Take a quiz about virtualization.
Explore Kubernetes Architecture
Show steps
This tutorial will help you understand the fundamentals of Kubernetes architecture, which is essential for building and managing containerized applications.
Browse courses on
Kubernetes
Show steps
-
Find a tutorial on Kubernetes architecture.
-
Follow the steps in the tutorial.
-
Create a Kubernetes cluster.
-
Deploy a containerized application to the cluster.
Practice Deploying Containers
Show steps
Practice deploying containers will help you develop the skills you need to build and manage containerized applications.
Browse courses on
Container Deployment
Show steps
-
Create a Dockerfile for your application.
-
Build a Docker image.
-
Run a container from the image.
-
Deploy the container to a Kubernetes cluster.
Show all three activities
Review Virtualization
Show steps
Reviewing this topic will help you be prepared for the upcoming course on virtualization, containerization, and orchestration for data engineering.
Browse courses on
Virtualization
Show steps
Show steps
Show steps
- Read an article about virtualization.
- Watch a tutorial about virtualization.
- Take a quiz about virtualization.
Explore Kubernetes Architecture
Show steps
This tutorial will help you understand the fundamentals of Kubernetes architecture, which is essential for building and managing containerized applications.
Browse courses on
Kubernetes
Show steps
Show steps
Show steps
- Find a tutorial on Kubernetes architecture.
- Follow the steps in the tutorial.
- Create a Kubernetes cluster.
- Deploy a containerized application to the cluster.
Practice Deploying Containers
Show steps
Practice deploying containers will help you develop the skills you need to build and manage containerized applications.
Browse courses on
Container Deployment
Show steps
Show steps
Show steps
- Create a Dockerfile for your application.
- Build a Docker image.
- Run a container from the image.
- Deploy the container to a Kubernetes cluster.
Show all three activities
Career center
Learners who complete Virtualization, Docker, and Kubernetes for Data Engineering will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/eaxo09/virtualization
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/eaxo09/virtualization
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Noah Gift
Kennedy Behrman
Language
English
Transcript
English
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser