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Python 中的 Apache Airflow 入门

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更新时间 2026年5月

学习如何实现并调度数据工程工作流。

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AirflowData Engineering

4小时

16 视频

57 道练习

4,000 XP

62,366

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课程描述

现已更新至 Apache Airflow 3.1.6。按计划交付数据常常需要大量手动操作：您要编写脚本、配置复杂的 cron 任务，并不断调整以满足变化的需求——与团队协作时管理起来更棘手。Apache Airflow 通过为工作流加入调度、错误处理和报告功能，可以帮您省去这些麻烦。在本课程中，您将掌握 Apache Airflow 的基础知识，并学习如何在生产环境中实现复杂的数据工程流水线。您还将学习如何使用有向无环图（DAG），自动化数据工程工作流，并以简洁且可复用的方式实现数据工程任务，帮助您更高效、更省心地完成工作。

先决条件

Intermediate Python Introduction to Shell

1

Intro to Airflow

In this chapter, you’ll gain a complete introduction to the components of Apache Airflow and learn how and why you should use them.

Introduction to Apache Airflow

Dag counter

Running your first Dag

Airflow Dags

Creating a simple Dag

Working with Dags and the Airflow shell

Examining Airflow commands

Testing a task in Airflow

Airflow web interface

Navigating the Airflow UI

Examining Dags with the Airflow UI

2

Building Dags in Airflow

What’s up Dag? Now it’s time to learn the basics of implementing Airflow Dags. Through hands-on activities, you’ll learn how to set up and deploy operators, tasks, and scheduling.

Airflow operators

Decorating a task

Defining a BashOperator with @task.bash

Define order of Tasks

More @tasks

Scheduling Dags

Schedule a Dag via Python

Deciphering Airflow schedules

Troubleshooting Dag runs

Passing data between tasks with XCom

Passing data with XCom

XCom variables

Airflow Sensors

Determining the order of tasks

Creating a FileSensor

Sensory Deprivation

3

Maintaining and monitoring Airflow workflows

In this chapter, you’ll learn how to save yourself time using Airflow components such as sensors and executors while monitoring and troubleshooting Airflow workflows.

Monitoring, Alerting, and Callbacks

Implementing a callback function

Implementing an SmtpNotifier

What's in the logs?

Templating with Jinja

Writing with Jinja

Subbing out

Airflow Variables

The answer is Variable

Reading a variable in Python

Variables not quite right

Debugging and troubleshooting in Airflow

Dag troubleshooting

Missing Dag

4

Controlling Dag Logic

Put it all together. In this final chapter, you’ll apply everything you've learned to build a production-quality workflow in Airflow.

Triggers and Fault Tolerance

Implementing a Trigger Rule

Adding retries

Triggering a child Dag

Define a @task.branch

Branch troubleshooting

The HITL Operator

Adding Human approval

ApprovalOperator Task

Creating a production pipeline

Setting up the sales ETL pipeline

Adding monthly branching

Adding a human approval gate

Congratulations!

Python 中的 Apache Airflow 入门

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