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.

Werken met 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)


Een getemplate BashOperator maken

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


Templates met meerdere argumenten

Meer 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)
                          


Lijsten gebruiken met templates

Parameteropties begrijpen

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)


Gemails met templates verzenden

Vertakken

Definieer een BranchPythonOperator

Problemen met branching oplossen

Een productie-pijplijn bouwen

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


Een productie-pijplijn maken #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


Een productie-pijplijn maken #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]

De laatste wijzigingen aan je pijplijn toevoegen

Gefeliciteerd!

Building production pipelines in Airflow

Nu geüpdatet naar Apache Airflow 2.7 - Gegevens volgens een planning leveren kan een handmatig proces zijn. Je schrijft scripts, voegt complexe cron-taken toe en probeert op allerlei manieren te voldoen aan steeds veranderende eisen—en het wordt nóg lastiger om alles te beheren wanneer je met teamgenoten werkt. Apache Airflow haalt deze zorg weg door planning, foutafhandeling en rapportage aan je workflows toe te voegen. In deze cursus leer je de basis van Apache Airflow en hoe je complexe data-engineeringpijplijnen in productie implementeert. Je leert ook hoe je Directed Acyclic Graphs (DAG's) gebruikt, data-engineeringworkflows automatiseert en data-engineeringtaken op een eenvoudige en herhaalbare manier uitvoert—zodat je het overzicht behoudt.

Intermediate Python

Introduction to Shell

Leer workflows voor data-engineering te plannen met Airflow en Python voor planning en foutafhandeling.

Introductie tot Apache Airflow in Python

Leer hoe u data-engineeringworkflows implementeert en plant.

De laatste wijzigingen aan je pijplijn toevoegen

Introductie tot Apache Airflow in Python

Praktische interactieve oefening