Airflow class library to implement instance tutorials for data pipelines

The AirFlow class library is a tool for building, scheduling and monitoring data pipelines.It is written in Python, which can be used to establish complex workflows and provide visual interface for management and monitoring. In this tutorial, I will introduce how to use the AirFlow class library to achieve a simple data pipeline.First, we need to install the AirFlow class library.You can use the following command to install: pip install apache-airflow After the installation is completed, we can start writing code and configuration. The first step is to create an AirFlow Dag (Direct Acyclic Graph).DAG is composed of a series of tasks (TASK) and the dependency relationship between them.Each task represents an executable operation.We save the code in a Python script, such as `my_dag.py`. python from datetime import datetime from airflow import DAG from airflow.operators.python_operator import PythonOperator def task1(): # The code of the first task def task2(): # The code of the second task def task3(): # The code of the third task default_args = { 'owner': 'your_name', 'start_date': datetime(2022, 1, 1) } with DAG('my_dag', default_args=default_args, schedule_interval='@daily') as dag: t1 = PythonOperator(task_id='task1', python_callable=task1) t2 = PythonOperator(task_id='task2', python_callable=task2) t3 = PythonOperator(task_id='task3', python_callable=task3) t1 >> t2 >> t3 In the above examples, we define three tasks, `task1`,` task2` and `task3`, and passed them to the` Pythonoperator`.We then use the `>>` to define the dependency relationship between them. Next, we need to make some configurations.In the `Airflow.cfg` file, the relevant configuration of the scheduler can be set, such as execution methods and concurrency.You can also configure the connection information and log storage position of the database. Finally, start the scheduler in the command line to use the following command: airflow scheduler After the start is successful, you can access the web interface of AirFlow in the browser, and access it through http: // localhost: 8080. In the web interface, we can see the status of the DAG and tasks we created.You can manually trigger the operation of the task or set the scheduling time of the task. In summary, the AirFlow class library provides a convenient way to build, schedule and monitor data pipelines.By writing code and configuration files, you can easily define the dependencies between tasks and them, and realize automated workflows.