Visão Geral

O Curso Containerizing Python Apps with Docker ensina como empacotar, distribuir e executar aplicações Python em contêineres Docker de forma eficiente e escalável. Os participantes aprenderão desde os fundamentos da conteinerização até práticas avançadas de otimização de imagens, uso de Docker Compose, e integração com pipelines CI/CD e ambientes de produção.

Objetivo

Após realizar este Curso Containerizing Python Apps with Docker, você será capaz de:

• Criar e configurar contêineres Docker para aplicações Python
• Gerenciar dependências e ambientes de execução com imagens personalizadas
• Trabalhar com Docker Compose para orquestração local
• Automatizar builds e deploys com Docker em pipelines CI/CD
• Aplicar boas práticas de segurança e performance em aplicações conteinerizadas

Publico Alvo

• Desenvolvedores Python, engenheiros DevOps, administradores de sistemas e arquitetos de software que desejam modernizar o ciclo de desenvolvimento e implantação de aplicações usando Docker.

Pre-Requisitos

• Conhecimento básico de Python
• Noções de terminal/Linux
• Familiaridade com conceitos de virtualização e rede (desejável)

Materiais

Conteúdo Programatico

Introduction to Containerization

1. Understanding virtualization vs containerization
2. Benefits of Docker for Python applications
3. Overview of Docker architecture and components

Setting Up the Environment

1. Installing Docker on Windows, macOS, and Linux
2. Configuring Docker Desktop and CLI
3. Verifying installation and basic Docker commands

Building Your First Python Container

1. Creating a simple Python application
2. Writing a basic Dockerfile
3. Building and running Docker images

Working with Docker Images and Containers

1. Managing images and containers via CLI
2. Understanding layers, tags, and registries
3. Cleaning up unused containers and images

Optimizing Dockerfiles for Python Apps

1. Using multi-stage builds
2. Reducing image size and improving build time
3. Handling dependencies with requirements.txt and virtual environments

Managing Environment Variables and Configuration

1. Passing configuration via environment variables
2. Using .env files for sensitive data
3. Dynamic configuration and runtime customization

Networking and Volumes in Docker

1. Exposing ports and linking containers
2. Using Docker volumes for data persistence
3. Networking best practices for Python microservices

Docker Compose for Multi-Container Applications

1. Introduction to Docker Compose syntax
2. Defining multi-service setups (web, database, cache)
3. Running and managing multi-container environments

Testing and Debugging Containers

1. Inspecting logs and container internals
2. Using docker exec for troubleshooting
3. Integrating pytest and debugging tools in Docker

Container Security and Best Practices

1. Managing user permissions and security context
2. Scanning images for vulnerabilities
3. Using official and minimal base images

Integrating Docker with CI/CD Pipelines

1. Building and pushing Docker images in Jenkins or GitLab CI
2. Automating tests and deployments
3. Using Docker Hub and AWS ECR for image hosting

Deploying Python Containers in Production

1. Running containers in cloud environments (AWS, GCP, Azure)
2. Managing scalability with Docker Swarm and Kubernetes
3. Monitoring containerized Python applications

Final Project

1. Containerizing and deploying a full Python web application
2. Integrating Compose, environment variables, and CI/CD automation