In this chapter, you’ll gain a complete introduction to the components of Apache Airflow and learn how and why you should use them.

Pengantar Apache Airflow

Menguji sebuah task di Airflow

Menguji perintah Airflow

DAG Airflow

Mendefinisikan DAG sederhana

Bekerja dengan DAG dan shell Airflow

from airflow import DAG

default_args = {
  'owner': 'jdoe',
  'start_date': '2023-01-01'
}
with DAG("etl_update",
         default_args=default_args
        ):
    pass

____

default_args = {
  'owner': 'jdoe',
  'email': 'jdoe@datacamp.com'
}
with DAG( 'refresh_data', 
          default_args=default_args 
        ):
    pass

from airflow import DAG

default_args = {
  'owner': 'jdoe',
  'email': 'jdoe@datacamp.com'
}
with DAG( 'refresh_data', 
          default_args=default_args 
        ):
    pass

Pemecahan Masalah Pembuatan DAG

Antarmuka web Airflow

Menjalankan webserver Airflow

Menavigasi UI Airflow

Menguji DAG dengan Antarmuka Airflow

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.

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

Kini Diperbarui ke Apache Airflow 2.7 - Menyajikan data sesuai jadwal bisa menjadi proses manual. Anda menulis skrip, menambahkan tugas cron yang rumit, dan mencoba berbagai cara untuk memenuhi serangkaian kebutuhan yang terus berubah—dan mengelola semuanya menjadi makin sulit saat bekerja dalam tim. Apache Airflow dapat menghilangkan kerumitan ini dengan menambahkan penjadwalan, penanganan galat, dan pelaporan ke alur kerja Anda. Dalam kursus ini, Anda akan menguasai dasar-dasar Apache Airflow dan mempelajari cara menerapkan pipeline rekayasa data yang kompleks di lingkungan produksi. Anda juga akan belajar menggunakan Directed Acyclic Graphs (DAG), mengotomatisasi alur kerja rekayasa data, dan menerapkan tugas rekayasa data dengan cara yang mudah dan berulang—membantu Anda tetap waras.

Intermediate Python

Introduction to Shell

Pelajari implementasi dan penjadwalan alur kerja data dengan Airflow dan DAG Airflow di Python.

Pengantar Apache Airflow dengan Python

Pelajari cara mengimplementasikan dan menjadwalkan alur kerja rekayasa data.

Data Engineer dalam Python

Mendefinisikan DAG sederhana

Pengantar Apache Airflow dengan Python

Latihan interaktif praktis