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

Dag counter

Running your first Dag

Airflow Dags

Creating a simple Dag

Working with Dags and the Airflow shell

Examining Airflow commands

Testing a task in Airflow

Airflow web interface

Navigating the Airflow UI

Examining Dags with the Airflow UI

Dag errors

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

Decorating a task

Defining a BashOperator with @task.bash

Define order of Tasks

More @tasks

Scheduling Dags

Schedule a Dag via Python

Deciphering Airflow schedules

Troubleshooting Dag runs

Passing data between tasks with XCom

Passing data with XCom

XCom variables

Airflow Sensors

Determining the order of tasks

Creating a FileSensor

Sensory Deprivation

Building Dags in Airflow

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.

Pemantauan, Peringatan, dan Callback

Mengimplementasikan fungsi callback

Menerapkan SmtpNotifier

Apa yang ada di log?

Templating dengan Jinja

Menulis dengan Jinja

Mengganti bagian

Variabel Airflow

Jawabannya adalah Variable

Membaca variabel di Python

Variabel kurang tepat

Debugging dan pemecahan masalah di Airflow

Pemecahan masalah Dag

from airflow.sdk import dag, task
#from airflow.providers.standard.sensors.filesystem import FileSensor
from pendulum import datetime

@dag(
    dag_id="execute_report",
    schedule="0 0 * * *",
    start_date=datetime(2026, 3, 20),
)
def execute_report():

    check_for_datafile = FileSensor(
        task_id="check_for_datafile",
        filepath="salesdata_ready.csv",
        mode="poke",
    )

    @task()
    def generate_report():
        print("Generating report...")

    check_for_datafile >> generate_report()

execute_report()

from airflow.sdk import dag, task
from pendulum import datetime

@dag(
    dag_id="sample_dag",
    schedule="0 0 * * *",
    start_date=datetime(2023, 2, 20),
)
def sample_dag():

    @task()
    def sample():
        print("Running sample task...")

    sample()

sample_dag()

from airflow.sdk import dag, task
from airflow.providers.standard.sensors.filesystem import FileSensor
from pendulum import datetime

@dag(
    dag_id="execute_report",
    schedule="0 0 * * *",
    start_date=datetime(2026, 3, 20),
)
def execute_report():

    check_for_datafile = FileSensor(
        task_id="check_for_datafile",
        filepath="salesdata_ready.csv",
        mode="poke",
    )

    @task()
    def generate_report():
        print("Generating report...")

    check_for_datafile >> generate_report()

execute_report()

Dag Hilang

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.

Triggers and Fault Tolerance

Implementing a Trigger Rule

Adding retries

Triggering a child Dag

Branching

Define a @task.branch

Branch troubleshooting

The HITL Operator

Adding Human approval

ApprovalOperator Task

Creating a production pipeline

Setting up the sales ETL pipeline

Adding monthly branching

Adding a human approval gate

Congratulations!

Controlling Dag Logic

Kini diperbarui ke Apache Airflow 3.1.6 - Menyajikan data sesuai jadwal sering kali merupakan proses manual. Anda menulis skrip, menambahkan tugas cron yang kompleks, dan mencoba berbagai cara untuk memenuhi persyaratan yang terus berubah—dan akan makin rumit saat harus mengelola semuanya bersama rekan kerja. 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 mempelajari cara menggunakan Directed Acyclic Graphs (Dag), mengotomatisasi alur kerja rekayasa data, serta menerapkan tugas-tugas rekayasa data dengan cara yang mudah dan dapat diulang—membantu Anda tetap terkendali.

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

Pemecahan masalah Dag

Pengantar Apache Airflow dengan Python

Latihan interaktif langsung