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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-airflow-from-basics-to-mastery/
课程评论：没有评论
课程名称：从基础到精通的Apache Airflow 课程概述：本课程是您提升工作流自动化技能的逐步指南。无论您是刚刚入门还是希望加深理解，该课程都会帮助您编写高效、可扩展且动态的管道，所需代码量极少。您将探索Apache Airflow的核心特性，学习如何从简单到复杂的系统组件过渡，同时避免常见错误并理解其发生的原因。 在课程中，您将通过真实案例获得实践经验，确保能够自信地将所学应用于实际场景。您将与Docker容器一起工作，创建第一个DAG，并探索关键功能，如分支、触发规则和装饰器。课程涵盖了基本的操作符、钩子和传感器，包括AWS S3、Postgres、SQL等。 随着课程的进展，您将深入学习调度、数据集管理，并了解Airflow强大的集成功能，例如与DBT的集成、使用Fernet密钥安全地管理密码，以及访问控制管理。此外，您将学习如何使用Git对代码进行版本控制，以确保工作流保持有序和可维护。 该课程旨在为希望提升Airflow技能或定期使用其进行工作流自动化的任何人设计。无论您是数据工程师、开发人员还是DevOps专业人士，都将获得Apache Airflow的坚实基础，并有信心构建稳健的数据管道。
Apache Airflow From Basics to Mastery is your step-by-step guide to advancing your workflow automation skills. Whether you're just getting started or looking to deepen your understanding, this course will help you write efficient, scalable, and dynamic pipelines with minimal code. You'll explore the core features of Apache Airflow, learning how to move from simple to complex system components while avoiding common mistakes and understanding why they happen.Throughout the course, you'll gain hands-on experience with real-world examples, ensuring you can confidently apply what you learn in practical scenarios. You'll work with Docker containers, create your first DAG, and explore key features like branching, trigger rules, and decorators. The course covers essential operators, hooks, and sensors, including AWS S3, Postgres, SQL, and more.As you progress, you'll dive into scheduling, dataset management, and Airflow's powerful integration capabilities, such as working with DBT, securing passwords with a Fernet key, and managing access control. Additionally, you'll learn best practices for versioning your code with Git, ensuring your workflows remain organized and maintainable.This course is designed for anyone looking to boost their Airflow skills or use it regularly for workflow automation. Whether you're a data engineer, developer, or DevOps professional, you'll walk away with a strong foundation in Apache Airflow and the confidence to build robust data pipelines.
