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.

Airflow sensors

Sensors vs operators

Sensory deprivation

Airflow executors

Determining the executor

Executor implications

Debugging and troubleshooting in Airflow

DAGs in the bag

Missing DAG

SLAs and reporting in Airflow

Defining an SLA

Defining a task SLA

Generate and email a report

Adding status emails

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.

Trabalhando com templates

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

default_args = {
  'start_date': datetime(2023, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Create a templated command to execute
# 'bash cleandata.sh datestring'


# Modify clean_task to use the templated command
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command='bash cleandata.sh datestring',
                          dag=cleandata_dag)


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

default_args = {
  'start_date': datetime(2023, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Create a templated command to execute
# 'bash cleandata.sh datestring'
templated_command = """
bash cleandata.sh {{ ds_nodash }}
"""

# Modify clean_task to use the templated command
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command=templated_command,
                          dag=cleandata_dag)


Criando um BashOperator com template

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

default_args = {
  'start_date': datetime(2023, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Modify the templated command to handle a
# second argument called filename.
templated_command = """
  bash cleandata.sh {{ ds_nodash }} ____
"""

# Modify clean_task to pass the new argument
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command=templated_command,
                          params={'filename': '____'},
                          dag=cleandata_dag)

# Create a new BashOperator clean_task2
clean_task2 = BashOperator(task_id='cleandata_task2',
                           ____,
                           ____,
                           ____)
                           
# Set the operator dependencies
____


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

default_args = {
  'start_date': datetime(2023, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Modify the templated command to handle a
# second argument called filename.
templated_command = """
  bash cleandata.sh {{ ds_nodash }} {{ params.filename }}
"""

# Modify clean_task to pass the new argument
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command=templated_command,
                          params={'filename': 'salesdata.txt'},
                          dag=cleandata_dag)
                          
# Create a new BashOperator clean_task2
clean_task2 = BashOperator(task_id='cleandata_task2',
                           bash_command=templated_command,
                           params={'filename': 'supportdata.txt'},
                           dag=cleandata_dag)
                           
# Set the operator dependencies
clean_task >> clean_task2


Modelos com vários argumentos

Mais templates

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

filelist = [f'file{x}.txt' for x in range(30)]

default_args = {
  'start_date': datetime(2020, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Modify the template to handle multiple files in a 
# single run.
templated_command = """
  <% for filename in ____ %>
  bash cleandata.sh {{ ds_nodash }} {{ filename }};
  <% endfor %>
"""

# Modify clean_task to use the templated command
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command=templated_command,
                          params={'filenames': ____},
                          dag=cleandata_dag)


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

filelist = [f'file{x}.txt' for x in range(30)]

default_args = {
  'start_date': datetime(2020, 4, 15),
}

cleandata_dag = DAG('cleandata',
                    default_args=default_args,
                    schedule_interval='@daily')

# Modify the template to handle multiple files in a 
# single run.
templated_command = """
  <% for filename in params.filenames %>
  bash cleandata.sh {{ ds_nodash }} {{ filename }};
  <% endfor %>
"""

# Modify clean_task to use the templated command
clean_task = BashOperator(task_id='cleandata_task',
                          bash_command=templated_command,
                          params={'filenames': filelist},
                          dag=cleandata_dag)
                          


Usando listas com templates

Entendendo opções de parâmetros

from airflow import DAG
from airflow.operators.email import EmailOperator
from datetime import datetime

# Create the string representing the html email content
html_email_str = """
Date: {{ ____ }}
Username: {{ ____ }}
"""

email_dag = DAG('template_email_test',
                default_args={'start_date': datetime(2023, 4, 15)},
                schedule_interval='@weekly')
                
email_task = EmailOperator(task_id='email_task',
                           to='testuser@datacamp.com',
                           subject="{{ ____ }}",
                           html_content=____,
                           params={'____': 'testemailuser'},
                           dag=email_dag)


from airflow import DAG
from airflow.operators.email import EmailOperator
from datetime import datetime

# Create the string representing the html email content
html_email_str = """
Date: {{ ds }}
Username: {{ params.username }}
"""

email_dag = DAG('template_email_test',
                default_args={'start_date': datetime(2023, 4, 15)},
                schedule_interval='@weekly')
                
email_task = EmailOperator(task_id='email_task',
                           to='testuser@datacamp.com',
                           subject="{{ macros.uuid.uuid4() }}",
                           html_content=html_email_str,
                           params={'username': 'testemailuser'},
                           dag=email_dag)


Enviando emails com templates

Ramificação

Defina um BranchPythonOperator

Solucionando problemas de ramificação

Criando um pipeline de produção

from airflow import DAG
from airflow.sensors.filesystem import FileSensor

# Import the needed operators
from airflow.operators.bash import ____
from airflow.operators.python import ____
from datetime import date, datetime

def process_data(**context):
  file = open('/home/repl/workspace/processed_data.tmp', 'w')
  file.write(f'Data processed on {date.today()}')
  file.close()

    
dag = DAG(dag_id='etl_update', default_args={'start_date': datetime(2023,4,1)})

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    poke_interval=5,
                    timeout=15,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             dag=dag)

sensor >> bash_task >> python_task


from airflow import DAG
from airflow.sensors.filesystem import FileSensor

# Import the needed operators
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from datetime import date, datetime

def process_data(**context):
  file = open('/home/repl/workspace/processed_data.tmp', 'w')
  file.write(f'Data processed on {date.today()}')
  file.close()

    
dag = DAG(dag_id='etl_update', default_args={'start_date': datetime(2023,4,1)})

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    poke_interval=5,
                    timeout=15,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             dag=dag)

sensor >> bash_task >> python_task


Criando um pipeline de produção #1

from airflow import DAG
from airflow.sensors.filesystem import FileSensor
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from dags.process import process_data
from datetime import timedelta, datetime

# Update the default arguments and apply them to the DAG
default_args = {
  'start_date': datetime(2023,1,1),
  ____
}

dag = DAG(dag_id='etl_update', default_args=default_args)

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    ____,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             ____
                             dag=dag)

sensor >> bash_task >> python_task


from datetime import date

def process_data(**kwargs):

    file = open("/home/repl/workspace/processed_data-" + kwargs['ds'] + ".tmp", "w")
    
    file.write(f"Data processed on {date.today()}")
    
    file.close()
  


from airflow import DAG
from airflow.sensors.filesystem import FileSensor
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from dags.process import process_data
from datetime import timedelta, datetime

# Update the default arguments and apply them to the DAG
default_args = {
  'start_date': datetime(2023,1,1),
  'sla': timedelta(minutes=90)
}

dag = DAG(dag_id='etl_update', default_args=default_args)

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    poke_interval=45,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             provide_context=True,
                             dag=dag)

sensor >> bash_task >> python_task


Criando um pipeline de produção #2

from airflow import DAG
from airflow.sensors.filesystem import FileSensor
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.operators.python import ____
from airflow.operators.empty import EmptyOperator
from airflow.operators.____ import EmailOperator
from dags.process import process_data
from datetime import datetime, timedelta

# Update the default arguments and apply them to the DAG.

default_args = {
  'start_date': datetime(2023,1,1),
  'sla': timedelta(minutes=90)
}
    
dag = DAG(dag_id='etl_update', default_args=default_args)

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    poke_interval=45,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             provide_context=True,
                             dag=dag)

email_subject="""
  Email report for {{ params.department }} on {{ ds_nodash }}
"""

email_report_task = EmailOperator(task_id='email_report_task',
                                  to='sales@mycompany.com',
                                  subject=email_subject,
                                  html_content='',
                                  params={____: 'Data subscription services'},
                                  dag=dag)

no_email_task = EmptyOperator(task_id='no_email_task', dag=dag)

def check_weekend(**kwargs):
    dt = datetime.strptime(kwargs['execution_date'],"%Y-%m-%d")
    # If dt.weekday() is 0-4, it's Monday - Friday. If 5 or 6, it's Sat / Sun.
    if (dt.weekday() < 5):
        return ____
    else:
        return ____
    
branch_task = BranchPythonOperator(task_id='check_if_weekend',
                                   python_callable=____,
                                   ____=True,                                
                                   dag=dag)

sensor >> bash_task >> python_task

python_task >> branch_task >> [email_report_task, no_email_task]

from datetime import date

def process_data(**kwargs):
    file = open("/home/repl/workspace/processed_data-" + kwargs['ds'] + ".tmp", "w")
    file.write(f"Data processed on {date.today()}")
    file.close()
  
  


from airflow import DAG
from airflow.sensors.filesystem import FileSensor
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.operators.python import BranchPythonOperator
from airflow.operators.empty import EmptyOperator
from airflow.operators.email import EmailOperator
from dags.process import process_data
from datetime import datetime, timedelta

# Update the default arguments and apply them to the DAG.

default_args = {
  'start_date': datetime(2023,1,1),
  'sla': timedelta(minutes=90)
}
    
dag = DAG(dag_id='etl_update', default_args=default_args)

sensor = FileSensor(task_id='sense_file', 
                    filepath='/home/repl/workspace/startprocess.txt',
                    poke_interval=45,
                    dag=dag)

bash_task = BashOperator(task_id='cleanup_tempfiles', 
                         bash_command='rm -f /home/repl/*.tmp',
                         dag=dag)

python_task = PythonOperator(task_id='run_processing', 
                             python_callable=process_data,
                             provide_context=True,
                             dag=dag)

email_subject="""
  Email report for {{ params.department }} on {{ ds_nodash }}
"""

email_report_task = EmailOperator(task_id='email_report_task',
                                  to='sales@mycompany.com',
                                  subject=email_subject,
                                  html_content='',
                                  params={'department': 'Data subscription services'},
                                  dag=dag)

no_email_task = EmptyOperator(task_id='no_email_task', dag=dag)

def check_weekend(**kwargs):
    dt = datetime.strptime(kwargs['execution_date'],"%Y-%m-%d")
    # If dt.weekday() is 0-4, it's Monday - Friday. If 5 or 6, it's Sat / Sun.
    if (dt.weekday() < 5):
        return 'email_report_task'
    else:
        return 'no_email_task'
    
branch_task = BranchPythonOperator(task_id='check_if_weekend',
                                   python_callable=check_weekend,
                                   provide_context=True,
                                   dag=dag)

sensor >> bash_task >> python_task

python_task >> branch_task >> [email_report_task, no_email_task]

Adicionando as alterações finais ao seu pipeline

Parabéns!

Building production pipelines in Airflow

Agora atualizado para o Apache Airflow 2.7 — Entregar dados em um cronograma pode ser um processo manual. Você escreve scripts, adiciona tarefas cron complexas e tenta várias formas de atender a requisitos que mudam o tempo todo — e gerenciar tudo isso fica ainda mais difícil trabalhando em equipe. O Apache Airflow resolve esse problema ao adicionar agendamento, tratamento de erros e relatórios aos seus fluxos de trabalho. Neste curso, você vai dominar o básico do Apache Airflow e aprender a implementar pipelines complexos de engenharia de dados em produção. Você também vai aprender a usar Grafos Acíclicos Dirigidos (DAGs), automatizar fluxos de trabalho de engenharia de dados e implementar tarefas de engenharia de dados de forma simples e reproduzível — ajudando você a manter a sanidade.

Intermediate Python

Introduction to Shell

Aprenda a implementar e agendar fluxos de engenharia de dados com Airflow e DAGs em Python.

Introdução ao Apache Airflow em Python

Saiba como implementar e programar fluxos de trabalho de engenharia de dados.

Adicionando as alterações finais ao seu pipeline

Introdução ao Apache Airflow em Python

Exercício interativo prático