CF

Docker Fundamentals

1h 17m 4s
English
Paid

Docker is one of the most popular open-source platforms that every data engineer should know. It is a modern and lightweight alternative to virtual machines. With Docker, you can deploy your code, run tools in the cloud, and package applications into isolated images, which provides complete control over the environment. In this course, you will master all the basic skills necessary for confidently working with Docker in the data engineering profession.

Introduction to Docker Concepts

To start, we will explore the difference between virtual machines and Docker. You will learn why Docker has become the preferred solution in the modern world of Data Engineering. We will discuss key concepts:

  • Images
  • Containers
  • Registries
  • Tags and others

You will install Docker Desktop as the main development environment.

Working with DockerHub

In the second part, you will get acquainted with DockerHub—learn how to find and use ready-made images from developers and companies for production deployment, testing, and local development.

Practical Applications of Docker

Hands-On Experience

In the practical module you will:

  • Download and run ready-made images from DockerHub via the command line and Docker Compose
  • Develop and run your own image
  • Add modules and expand the functionality of the container
  • Create your own repository on DockerHub and publish your image there

This way you will fully master how Docker works and how to share your solutions.

Deploying Docker in Production

To help you understand how Docker is applied in real projects, we will look at deploying containers in a cloud environment. You will learn how to run containers using various cloud services and get acquainted with best security practices to protect your containers from external threats.

The course is ideal for beginners and those who want to confidently include Docker in their engineering stack.

Additional

https://github.com/team-data-science/Docker-Fundamentals

About the Author: Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

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#1: Course introduction
All Course Lessons (15)
#Lesson TitleDurationAccess
1
Course introduction Demo
03:09
2
Docker vs virtual machines
06:24
3
Docker terminology: Images, containers, registries and tags
05:57
4
How to install Docker Desktop & DockerHub introduction
04:10
5
Pulling images & running containers in CLI
06:35
6
CLI Cheat sheet
03:39
7
Docker compose explained
06:35
8
Build and run a simple Hello World image
06:29
9
Build an image requiring dependencies
05:06
10
Using the DockerHub image registry
04:25
11
Understanding image layers
07:56
12
Deployment of containers in production
05:48
13
Security best practices
04:10
14
Managing Docker images & containers with Portainer
04:05
15
Conclusion
02:36
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Frequently asked questions

What are the prerequisites for enrolling in this Docker course?
Before enrolling, it's beneficial to have a basic understanding of command-line interfaces and general programming concepts. Familiarity with cloud environments and virtual machines will also help in grasping the differences discussed in the course. However, the course is designed to introduce Docker from the ground up, so prior Docker experience is not necessary.
What will I build during the practical applications module?
In the practical module, you will download and run ready-made images from DockerHub using the command line and Docker Compose. You will also develop and run your own Docker image, add modules to expand the container's functionality, and create a repository on DockerHub to publish your image. These projects will reinforce your understanding of working with Docker in real-world scenarios.
Is this course suitable for beginners in data engineering?
Yes, this course is suitable for beginners in data engineering. It starts with the basics of Docker, including the differences between Docker and virtual machines, and gradually builds up to more complex topics like creating and managing Docker images and containers. The course is structured to help new data engineers integrate Docker into their workflows effectively.
How does the scope of this course compare to other Docker courses?
This course covers the foundational aspects of Docker, focusing on key concepts like images, containers, registries, and tags. It also provides practical experience with DockerHub and Docker Compose. While some courses may delve more deeply into specialized Docker use-cases or advanced orchestration tools, this course is designed to provide a comprehensive introduction, particularly for those in data engineering.
Which specific tools or platforms will I learn to use in this course?
You will learn to use Docker Desktop as your main development environment. The course also covers DockerHub, a popular image registry, and Docker Compose for running multi-container Docker applications. Additionally, you'll gain insights into managing Docker images and containers using Portainer, an open-source management tool.
What topics are not covered in this Docker course?
This course does not cover advanced Docker orchestration tools such as Kubernetes. While it provides a solid foundation in Docker basics and practical applications, students looking for knowledge in scaling Docker deployments or integrating Docker with CI/CD pipelines may need to explore additional resources.
How much time should I expect to commit to complete this course?
The course comprises 15 lessons, and although the total runtime is not specified, students should anticipate spending additional time on hands-on exercises and projects. A commitment of several hours per week over a few weeks should suffice to thoroughly understand the course content and complete the practical assignments.