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 sensörleri

Options

Sensors

Both

Operators

Sensörler ve operatörler

Duyusal yoksunluk

Airflow yürütücüleri

Executor'ı belirleme

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


Yürütücü etkileri

Airflow'da hata ayıklama ve sorun giderme

Çantadaki DAG'ler

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


Eksik DAG

Airflow'da SLA'lar ve raporlama

Bir SLA tanımlama

Bir görev için SLA tanımlama

Bir rapor oluştur ve e-postayla gönder

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


Durum e-postaları ekleme

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

Apache Airflow 2.7 ile güncellendi - Veriyi bir takvime bağlı olarak teslim etmek çoğu zaman manuel bir süreçtir. Betikler yazarsın, karmaşık cron görevleri eklersin ve sürekli değişen gereksinimlere uymak için türlü yollar denersin — ekip arkadaşlarınla çalışırken her şeyi yönetmek daha da zorlaşır. Apache Airflow, iş akışlarına zamanlama, hata yönetimi ve raporlama ekleyerek bu baş ağrısını ortadan kaldırabilir. Bu derste, Apache Airflow’un temellerini öğrenecek ve üretim ortamında karmaşık veri mühendisliği boru hatlarını nasıl uygulayacağını keşfedeceksin. Ayrıca Directed Acyclic Graphs (DAG) kullanmayı, veri mühendisliği iş akışlarını otomatikleştirmeyi ve görevleri kolay ve tekrarlanabilir bir şekilde hayata geçirmeyi öğreneceksin — böylece akıl sağlığını korumana yardımcı olacak.

Intermediate Python

Introduction to Shell

Airflow ve Airflow DAG'leri ile Python'da veri mühendisliği iş akışlarını planlayıp uygulayın.

Python ile Apache Airflow'a Giriş

Veri mühendisliği iş akışlarını nasıl uygulayacağınızı ve planlayacağınızı öğrenin.

Veri Mühendisi Python'da

Bir görev için SLA tanımlama

Python ile Apache Airflow'a Giriş

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