Kubernetes for Data Engineering with Python

Course Description

"Kubernetes for Data Engineering with Python" is an exciting and comprehensive course that delves into the world of virtualization, containerization, and orchestration for data engineering. This course is designed to equip students with the practical skills and knowledge needed to build, deploy, and manage containerized data solutions at scale using industry-standard tools and techniques.

What Students Will Learn

  • Virtualization fundamentals and working with virtual machines
  • Docker containers and building scalable microservices
  • Orchestrating containers using Kubernetes and cloud platforms
  • Utilizing cloud development environments like GitHub Codespaces
  • Production best practices, including monitoring, testing, and CI/CD
  • Practical experience with industry-standard tools and techniques
  • Skills to build, deploy, and manage containerized data solutions at scale

Prerequisites

This course is listed as introductory level, and there are no specific prerequisites mentioned. However, a basic understanding of programming concepts and familiarity with Python would be beneficial.

Course Coverage

  • Virtualization theory and concepts
  • Docker containers and their usage
  • Kubernetes architecture and deployments
  • Cloud development with GitHub Codespaces
  • Container registries for Kubernetes
  • Cloud-based Kubernetes solutions (AWS, GCP, Azure)
  • Production monitoring, testing, and CI/CD pipelines
  • Building and deploying microservices
  • Load testing with Locust
  • Site Reliability Engineering (SRE) mindset for MLOps

Who This Course Is For

This course is ideal for students, data professionals, and anyone looking to enhance their data engineering capabilities. It's perfect for those who want to gain practical skills in containerization and orchestration, which are increasingly important in modern data engineering and cloud computing environments.

Real-World Applications

  • Design and implement scalable data solutions in cloud environments
  • Improve efficiency and resource utilization in data processing pipelines
  • Develop and deploy microservices-based applications
  • Implement robust CI/CD pipelines for data engineering projects
  • Enhance the reliability and performance of data systems using SRE practices
  • Collaborate more effectively in cloud-based development environments
  • Optimize container-based workflows in data engineering teams

Syllabus

Module 1: Virtualization Theory and Concepts (6 hours)

  • Virtualization basics
  • Scaling applications
  • Hardware utilization
  • Virtual machines and VirtualBox
  • Container concepts
  • Introduction to Docker

Module 2: Using Docker (5 hours)

  • Docker client and command line
  • Creating volumes and running databases in containers
  • Building Docker images and using Dockerfiles
  • Orchestration with Docker Compose
  • Introduction to Airflow

Module 3: Kubernetes: Container Orchestration in Action (6 hours)

  • Kubernetes key concepts, clusters, nodes, and service deployments
  • Cloud developer workspaces and GitHub ecosystem
  • Using GitHub Codespaces and Copilot
  • Running Minikube in GitHub Codespaces
  • Deploying services with Minikube

Module 4: Building Kubernetes Solutions (9 hours)

  • Building microservices in various environments (GitHub Codespaces, Cloud9)
  • Deploying containerized applications to cloud platforms
  • Container orchestration options (GCP Cloud Run, AWS Copilot)
  • Load testing with Locust
  • Monitoring systems and SRE mindset for MLOps
  • CI/CD for microservices