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

Options

Sensors

Both

Operators

Sensors vs operators

Sensory deprivation

Airflow executors

Determining the executor

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


Executor implications

Debugging and troubleshooting in Airflow

DAGs in the bag

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


Missing DAG

SLAs and reporting in Airflow

Defining an SLA

Defining a task SLA

Generate and email a report

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


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.

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

Now Updated to Apache Airflow 2.7 - Delivering data on a schedule can be a manual process. You write scripts, add complex cron tasks, and try various ways to meet an ever-changing set of requirements—and it's even trickier to manage everything when working with teammates. Apache Airflow can remove this headache by adding scheduling, error handling, and reporting to your workflows. In this course, you'll master the basics of Apache Airflow and learn how to implement complex data engineering pipelines in production. You'll also learn how to use Directed Acyclic Graphs (DAGs), automate data engineering workflows, and implement data engineering tasks in an easy and repeatable fashion—helping you to maintain your sanity.

Intermediate Python

Introduction to Shell

Learn to implement and schedule data engineering workflows by adding scheduling, error handling, and reporting using Airflow and Airflow DAGs in Python. 

Introduction to Airflow in Python

Learn how to implement and schedule data engineering workflows.

Introduction to Apache Airflow in Python

Data Engineer in Python

DAGs in the bag

Introduction to Apache Airflow in Python

Hands-on interactive exercise