Creating a basic DAG file in Apache Airflow - Why You Should Know This

The Big Idea

What if your daily tasks could run themselves perfectly on time, every time?

The Scenario

Imagine you have a list of tasks to do every day, like watering plants, feeding pets, and checking emails. You write them down on paper and try to remember the order and timing yourself.

The Problem

This manual way is slow and easy to forget. You might water plants twice or miss feeding pets. It's hard to keep track and adjust the schedule when things change.

The Solution

Creating a basic DAG file in Airflow lets you write down your tasks and their order in a simple file. Airflow then runs them automatically at the right time, making sure nothing is missed or repeated.

Before vs After

✗ Before

Water plants
Feed pets
Check emails

✓ After

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

dag = DAG('daily_tasks', start_date=datetime(2024,1,1), schedule_interval='@daily')
start = DummyOperator(task_id='start', dag=dag)
water = DummyOperator(task_id='water_plants', dag=dag)
feed = DummyOperator(task_id='feed_pets', dag=dag)
check = DummyOperator(task_id='check_emails', dag=dag)
start >> water >> feed >> check

What It Enables

You can automate and control complex workflows easily, saving time and avoiding mistakes.

Real Life Example

A company uses a DAG file to run data processing tasks every night, ensuring reports are ready every morning without anyone needing to start the process manually.

Key Takeaways

Manual task tracking is slow and error-prone.

DAG files automate task order and timing.

Automation saves time and reduces mistakes.