Visão Geral

Este Curso Advanced Python Automation, ensina aos desenvolvedores as principais habilidades do Python para escrever quase qualquer script de automação e executá-lo em distribuição. Os participantes aprendem como aplicar conteinerização com Docker, acessar dados de banco de dados com Python, enviar mensagens com Python e RabbitMQ, orquestrar tarefas distribuídas com Celery e executar comandos SSH remotos.

Objetivo

Após realizare este Curso Advanced Python Automation você será capaz de:

• Implante tarefas em escala com o Celery
• Empregue Python e Celery em um ambiente em contêiner
• Use o banco de dados PostgreSQL com Python
• Envie mensagens com Python e RabbitMQ
• Aplique habilidades básicas e mais avançadas de Python Celery

Pre-Requisitos

• Todos os alunos participantes deste curso, devem ter feito o Curso Python Task Automation ou ter experiência significativa com os tópicos abordados nesse curso

Materiais

Conteúdo Programatico

Introduction

Development Environment (Very Quick Overview)

1. Configure VS Code for Python script development
2. Code Reformatting with Black
3. Debugging Python Scripts with VS Code

Simple Task Distribution

1. Use SSH to run Remote Commands
2. Use SSH to Download/Upload Files
3. Write Python Scripts to run code on remote Linux Computers
4. Collect Data from remote Linux Computers

Containerization

1. What is a Container?
2. What is Docker?
3. What is Docker Hub?
4. Images and Containers
5. Create an Image with Dockerfile
6. Run Containers
7. Configure Containers with Environment Variables
8. Docker Compose
9. Docker Compose Networking
10. Docker Compose Volume

Remote Data Storage

1. What is Remote Data Storage?
2. Running a Data Storage in a Container
3. Running PostgreSQL in a Container
4. Running PostgreSQL Client Tool in a Container
5. Configure with Docker Compose
6. Read/Write Data to PostgreSQL with Python SQLAlchemy

Remote Message Broker

1. What is a Message Broker?
2. Running a Message Broker in a Container
3. Running RabbitMQ in a Container
4. Running RabbitMQ Client Tool in a Container
5. Configure with Docker Compose
6. Read/Write Data to RabbitMQ with Python and Pika

Task Automation at Scale with Celery

1. Overview of Celery and its features
2. Installing Celery and its dependencies
3. Setting up a simple Celery project

Celery Basics

1. Defining and running tasks
2. Task decorators and options
3. Passing arguments and results between tasks
4. Task retries and error handling
5. Monitoring and managing Celery workers

Advanced Celery Concepts

1. Task serialization and message brokers
2. Task routing and prioritization
3. Task result backends
4. Grouping and chaining tasks
5. Scheduling periodic tasks with Celery beat

Scaling and Deployment

1. Load balancing tasks with multiple workers
2. Deploying Celery in a production environment
3. Configuring Celery for high availability
4. Best practices for handling long-running tasks
5. Monitoring and performance tuning