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Virtualization, Docker, and Kubernetes for Data Engineering

Noah Gift and Kennedy Behrman

Dive into the world of virtualization, containerization, and orchestration for data engineering:

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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.

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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

Syllabus

\- Module 1: Virtualization Theory and Concepts (6 hours to complete)
\- 8 videos (Total 26 minutes)
\- Virtualization (2 minutes)
\- Scaling Applications (1 minute)
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\- Hardware Utilization (0 minutes)
\- Introduction to Virtual Machines (2 minutes)
\- Virtual Box Demo (10 minutes)
\- Container Concepts (2 minutes)
\- Introduction to Docker (5 minutes)
\- Docker Architecture (1 minute)
\- 8 readings (Total 80 minutes)
\- Welcome to Kubernetes for Data Engineering with Python! (10 minutes)
\- Meet your Instructors: Noah Gift and Kennedy Behrman (10 minutes)
\- Tools and Platforms (10 minutes)
\- What is Virtualization? (10 minutes)
\- What is a Virtual Machine? (10 minutes)
\- Introduction to Containers (10 minutes)
\- Docker: The Container Platform (10 minutes)
\- Spin up a local Docker container (10 minutes)
\- 8 quizzes (Total 240 minutes)
\- Virtualization (30 minutes)
\- Scaling Applications (30 minutes)
\- Introduction to Virtual Machines (30 minutes)
\- Virtual Box (30 minutes)
\- Containers (30 minutes)
\- Introduction to Docker (30 minutes)
\- Docker Architecture (30 minutes)
\- 2 discussion prompts (Total 20 minutes)
\- Meet and Greet (optional) (10 minutes)
\- Let Us Know if Something's Not Working (10 minutes)
\- Module 2: Using Docker (5 hours to complete)
\- 9 videos (Total 42 minutes)
\- Docker Client (5 minutes)
\- Creating a Volume (5 minutes)
\- Running a Database in a Container (6 minutes)
\- Building an Image (4 minutes)
\- Dockerfiles (2 minutes)
\- Dockerfile Examples (4 minutes)
\- Orchestration with Docker Compose (3 minutes)
\- Introduction to Airflow (5 minutes)
\- Airflow Demonstration using Compose (4 minutes)
\- 6 readings (Total 60 minutes)
\- Use the Docker Command Line (10 minutes)
\- Creating a Docker Image (Step-by-Step) (10 minutes)
\- Getting Started with Docker Compose (10 minutes)
\- Getting Started with Apache Airflow (10 minutes)
\- Docker vs. Kubernetes: A Primer (10 minutes)
\- Use Docker to Spin Up Airflow (10 minutes)
\- Docker (30 minutes)
\- Docker Client (30 minutes)
\- Volumes (30 minutes)
\- Running a Database in a Container (30 minutes)
\- Building an Image (30 minutes)
\- Dockerfiles (30 minutes)
\- Compose (30 minutes)
\- Airflow (30 minutes)
\- Module 3: Kubernetes: Container Orchestration in Action (6 hours to complete)
\- 14 videos (Total 52 minutes)
\- Kubernetes Key Concepts (1 minute)
\- Kubernetes Clusters (1 minute)
\- Kubernetes Nodes (1 minute)
\- Kubernetes Service Deployments (1 minute)
\- Cloud Developer Workspace Advantage (4 minutes)
\- Key Concepts in the GitHub Ecosystem (3 minutes)
\- Using GitHub Templates (2 minutes)
\- Using GitHub Codespaces (6 minutes)
\- Using OpenAI Codewhisper (1 minute)
\- Fine-Tuning a Model with Hugging Face (3 minutes)
\- Using GitHub Copilot (8 minutes)
\- GitHub Actions (3 minutes)
\- Running Minikube in GitHub Codespaces (6 minutes)
\- Deploying a Service with Minikube (7 minutes)
\- 7 readings (Total 70 minutes)
\- What is Kubernetes? (10 minutes)
\- Virtualization, Containerization, and Elasticity (10 minutes)
\- Fine-Tune a Pretrained Model (10 minutes)
\- Getting Started with GitHub Copilot (10 minutes)
\- Hello Minikube (10 minutes)
\- Minikube + Kubernetes: A Recap (10 minutes)
\- Deploying FastAPI to AWS with ECR and App Runner (10 minutes)
\- Kubernetes, GitHub, and Minikube (30 minutes)
\- Kubernetes Key Concepts (30 minutes)
\- Kubernetes Clusters (30 minutes)
\- Kubernetes Nodes (30 minutes)
\- Kubernetes Service Deployments (30 minutes)
\- Key Concepts in the GitHub Ecosystem (30 minutes)
\- Running Minikube in GitHub Codespaces (30 minutes)
\- Deploying a Service with Minikube (30 minutes)
\- Module 4: Building Kubernetes Solutions (9 hours to complete)
\- 13 videos (Total 66 minutes)
\- Build a Tiny Bash Container using GitHub Codespaces (8 minutes)
\- Build FastAPI Microservice in Cloud9 in Python (5 minutes)
\- Deploy a FastAPI PyTorch Containerized Application to AWS App Runner (7 minutes)
\- Options for Container Orchestration (2 minutes)
\- GCP Cloud Run (4 minutes)
\- Build Microservice in Cloud9 in C# (6 minutes)
\- AWS Copilot - Command Line Interface for Containerized Applications (9 minutes)
\- Load-Testing with Locust (3 minutes)
\- Monitoring Systems (1 minute)
\- SRE Mindset for MLOps (5 minutes)
\- Operationalize Microservices (1 minute)
\- CI for Microservices (6 minutes)
\- What is Continuous Delivery? (2 minutes)
\- Using Container Registries with Kubernetes: Azure Container Registry and Amazon Elastic Container Registry (ECR) (10 minutes)
\- Kubernetes and Google Cloud (10 minutes)
\- Deploying Containerized Applications and Kubernetes in the Cloud with AWS (10 minutes)
\- Getting Started with Site Reliability Engineering (SRE) (10 minutes)
\- Continuous Delivery of FastAPI App to AWS App Runner (10 minutes)
\- Final Project Explained (10 minutes)
\- Next Steps (10 minutes)
\- 14 quizzes (Total 420 minutes)
\- Kubernetes Data Engineering Solutions (30 minutes)
\- Build a Tiny Bash Container using GitHub Codespaces (30 minutes)
\- Build FastAPI Microservice in Cloud9 in Python (30 minutes)
\- Deploying a FastAPI PyTorch Containerized Application to AWS App Runner (30 minutes)
\- Options for Container Orchestration (30 minutes)
\- GCP Cloud Run (30 minutes)
\- Build Microservice in Cloud9 in C# (30 minutes)
\- AWS Copilot (30 minutes)
\- Load-Testing with Locust (30 minutes)
\- Monitoring Systems (30 minutes)
\- SRE Mindset for MLOps (30 minutes)
\- Operationalize Microservices (30 minutes)
\- CI for Microservices (30 minutes)
\- Continuous Delivery (30 minutes)

Good to know

Know what's good
, what to watch for
, 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

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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
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
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
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

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Course image
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Noah Gift
Kennedy Behrman
Session
March 31, 2024 - March 31, 2025
Language
English
Transcript
English
Good to know
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
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