Docker and Kubernetes for Data Engineering

Ready to move beyond running scripts on your local machine? Learn how modern data engineering teams package, deploy, and manage their applications for scalability and reliability. This course provides a practical foundation in containerization and orchestration, tailored specifically for data engineering tasks. You will learn to use Docker to create consistent, isolated environments for your data applications and then use Kubernetes to deploy and manage them efficiently, ensuring your data pipelines are robust and scalable. What you'll learn: - Understand core virtualization and containerization concepts from the ground up. - Build and manage custom Docker images for Python scripts and data applications. - Practice orchestrating multi-container data services locally with Docker Compose. - Learn the fundamentals of Kubernetes for deploying and scaling containerized data workloads. - Configure persistent storage and networking for stateful data applications in Kubernetes. - Apply basic CI/CD principles to automate the building and deployment of your container images. - Explore Site Reliability Engineering (SRE) concepts to improve the robustness of your data systems. The course starts with the essential theory behind containers before moving into practical exercises with Docker. You'll then progress to orchestrating applications with Kubernetes, focusing on patterns relevant to data pipelines. This course is designed for aspiring data engineers, data analysts, and developers new to infrastructure. No prior experience with Docker, Kubernetes, or virtualization is required. Start building and deploying data applications like a professional.