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

Testing a task in Airflow

Examining Airflow commands

Airflow DAGs

Defining a simple DAG

Working with DAGs and the Airflow shell

Troubleshooting DAG creation

Airflow web interface

Starting the Airflow webserver

Navigating the Airflow UI

Examining DAGs with the Airflow UI

Intro to 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

Defining a BashOperator task

Multiple BashOperators

Airflow tasks

Define order of BashOperators

Determining the order of tasks

Troubleshooting DAG dependencies

Additional operators

Using the PythonOperator

More PythonOperators

EmailOperator and dependencies

Airflow scheduling

Schedule a DAG via Python

Deciphering Airflow schedules

Troubleshooting DAG runs

Implementing Airflow DAGs

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.

Sensores de Airflow

Options

Sensors

Both

Operators

Sensores vs operadores

Privación sensorial

Ejecutores de Airflow

Determinar el ejecutor

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *"
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2024,1,20),
    mode='poke',
    dag=report_dag
)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2024,1,20),
    dag=report_dag
)

precheck >> generate_report_task


from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *"
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2024,1,20),
    mode='reschedule',
    dag=report_dag
)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2024,1,20),
    dag=report_dag
)

precheck >> generate_report_task


Implicaciones del ejecutor

Depuración y resolución de problemas en Airflow

DAGs a punto

from airflow import DAG
#from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *"
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2023,2,20),
    mode='poke',
    dag=report_dag)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2023,2,20),
    dag=report_dag
)

precheck >> generate_report_task


from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

sample_dag = DAG(
    dag_id = 'sample_dag',
    schedule_interval = "0 0 * * *"
)

sample_task = BashOperator(
    task_id='sample',
    bash_command='generate_sample.sh',
    start_date=datetime(2023,2,20),
    dag=sample_dag
)


from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *"
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2023,2,20),
    mode='reschedule',
    dag=report_dag)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2023,2,20),
    dag=report_dag
)

precheck >> generate_report_task


DAG no encontrado

SLA y generación de informes en Airflow

Definir un SLA

Definir un SLA de tarea

Generar y enviar por email un informe

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

default_args={
    '____': [____],
    '____': True,
    'email_on_success': ____
}

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *",
    default_args=default_args
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2023,2,20),
    mode='reschedule',
    dag=report_dag)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2023,2,20),
    dag=report_dag
)

precheck >> generate_report_task


from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.sensors.filesystem import FileSensor
from datetime import datetime

default_args={
    'email': ['airflowalerts@datacamp.com', 'airflowadmin@datacamp.com'],
    'email_on_failure': True,
    'email_on_success': True
}

report_dag = DAG(
    dag_id = 'execute_report',
    schedule_interval = "0 0 * * *",
    default_args=default_args
)

precheck = FileSensor(
    task_id='check_for_datafile',
    filepath='salesdata_ready.csv',
    start_date=datetime(2023,2,20),
    mode='reschedule',
    dag=report_dag)

generate_report_task = BashOperator(
    task_id='generate_report',
    bash_command='generate_report.sh',
    start_date=datetime(2023,2,20),
    dag=report_dag
)

precheck >> generate_report_task


Añadir correos de estado

Maintaining and monitoring Airflow workflows

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

Working with templates

Creating a templated BashOperator

Templates with multiple arguments

More templates

Using lists with templates

Understanding parameter options

Sending templated emails

Branching

Define a BranchPythonOperator

Branch troubleshooting

Creating a production pipeline

Creating a production pipeline #1

Creating a production pipeline #2

Adding the final changes to your pipeline

Congratulations!

Building production pipelines in Airflow

Actualizado a Apache Airflow 2.7: entregar datos con una programación puede convertirse en un proceso manual. Escribes scripts, añades tareas cron complejas y pruebas distintas formas de cumplir unos requisitos que cambian constantemente, y gestionar todo esto es aún más complicado cuando trabajas en equipo. Apache Airflow puede quitarte este dolor de cabeza añadiendo planificación, gestión de errores e informes a tus flujos de trabajo. En este curso, dominarás lo básico de Apache Airflow y aprenderás a implementar en producción canalizaciones complejas de ingeniería de datos. También aprenderás a usar grafos acíclicos dirigidos (DAGs), automatizar flujos de trabajo de ingeniería de datos e implementar tareas de ingeniería de datos de forma sencilla y repetible, ayudándote a mantener la calma.

Intermediate Python

Introduction to Shell

Aprende a implementar y programar flujos de trabajo de ingeniería de datos con Airflow y DAGs en Python.

Introducción a Apache Airflow en Python

Aprende a implantar y programar flujos de trabajo de ingeniería de datos.

Ingeniero de datos en Python

Añadir correos de estado

Introducción a Apache Airflow en Python

Ejercicio interactivo práctico