DAG concept (Directed Acyclic Graph) in Apache Airflow - Mini Project: Build & Apply

Basic Airflow DAG Creation

📖 Scenario: You are a data engineer setting up a simple workflow to run daily tasks using Apache Airflow. You want to create a Directed Acyclic Graph (DAG) that defines the order of tasks.

🎯 Goal: Build a basic Airflow DAG with two tasks where the second task runs after the first. This will help you understand how to define workflows in Airflow using DAGs.

📋 What You'll Learn

Create a DAG object with a specific dag_id and schedule

Define two PythonOperator tasks with simple Python functions

Set task dependencies so the second task runs after the first

Print the DAG structure by listing task IDs in order

💡 Why This Matters

🌍 Real World

Airflow DAGs are used to automate and schedule workflows like data pipelines, report generation, and system maintenance tasks.

💼 Career

Understanding DAGs is essential for roles like data engineer, DevOps engineer, and workflow automation specialist who manage task scheduling and orchestration.

1

Create the DAG object

Import DAG from airflow and create a DAG object called simple_dag with dag_id='simple_dag' and schedule_interval='@daily'. Use start_date=datetime(2024, 1, 1) and catchup=False.

Apache Airflow

from datetime import datetime
# Create DAG object called simple_dag with dag_id='simple_dag', schedule_interval='@daily', start_date=datetime(2024, 1, 1), catchup=False
# Your code here

Hint

Use the DAG class from airflow and pass the parameters exactly as shown.

2

Define two PythonOperator tasks

Import PythonOperator from airflow.operators.python. Define two Python functions called task_one_func and task_two_func that each print a simple message. Then create two tasks called task_one and task_two using PythonOperator with task_id matching the variable names and assign python_callable to the respective functions. Attach both tasks to the simple_dag DAG.

Apache Airflow

from datetime import datetime
from airflow import DAG
# Import PythonOperator
# Define task_one_func and task_two_func functions
# Create task_one and task_two PythonOperator tasks attached to simple_dag
# Your code here

Hint

Define simple functions that print messages and use them as python_callable in PythonOperator.

3

Set task dependencies

Set the dependency so that task_two runs after task_one by writing task_one >> task_two.

Apache Airflow

from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator

def task_one_func():
    print('Task one is running')

def task_two_func():
    print('Task two is running')

simple_dag = DAG(
    dag_id='simple_dag',
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False
)

task_one = PythonOperator(
    task_id='task_one',
    python_callable=task_one_func,
    dag=simple_dag
)

task_two = PythonOperator(
    task_id='task_two',
    python_callable=task_two_func,
    dag=simple_dag
)

# Set task_two to run after task_one
# Your code here

Hint

Use the >> operator to set the order of tasks.

4

Print the DAG task order

Write a print statement that outputs the list of task IDs in the DAG in the order they will run. Use print([task.task_id for task in simple_dag.topological_sort()]).

Apache Airflow

from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator

def task_one_func():
    print('Task one is running')

def task_two_func():
    print('Task two is running')

simple_dag = DAG(
    dag_id='simple_dag',
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False
)

task_one = PythonOperator(
    task_id='task_one',
    python_callable=task_one_func,
    dag=simple_dag
)

task_two = PythonOperator(
    task_id='task_two',
    python_callable=task_two_func,
    dag=simple_dag
)

task_one >> task_two

# Print the list of task IDs in execution order
# Your code here

Hint

Use simple_dag.topological_sort() to get tasks in order and print their task_id.

Practice

1. What is a DAG in Apache Airflow?

easy

A. A tool to store data in a database

B. A programming language for writing tasks

C. A type of server used to run Airflow

D. A Directed Acyclic Graph that defines task order without loops

Solution

1. Step 1: Understand the meaning of DAG

   DAG stands for Directed Acyclic Graph, which means tasks are connected in one direction without cycles or loops.

2. Step 2: Relate DAG to Airflow

   In Airflow, DAGs organize tasks so they run in a specific order safely and predictably.

3. Final Answer:

   A Directed Acyclic Graph that defines task order without loops -> Option D

4. Quick Check:

   DAG = Directed Acyclic Graph [OK]

Hint: Remember DAG means tasks flow one way, no loops [OK]

Common Mistakes:

• Thinking DAG is a database or server
• Confusing DAG with a programming language
• Assuming tasks can loop back in DAG

2. Which of the following is the correct way to define a DAG in Airflow using Python?

easy

A. dag = DAG('my_dag', schedule_interval='@daily')

B. dag = DAG('my_dag', run_every='daily')

C. dag = DAG('my_dag', interval='daily')

D. dag = DAG('my_dag', schedule='daily')

Solution

1. Step 1: Recall Airflow DAG parameters

   The correct parameter to set the schedule is schedule_interval.

2. Step 2: Match the correct syntax

   Only dag = DAG('my_dag', schedule_interval='@daily') uses schedule_interval='@daily', which is valid syntax.

3. Final Answer:

   dag = DAG('my_dag', schedule_interval='@daily') -> Option A

4. Quick Check:

   Use schedule_interval to set DAG schedule [OK]

Hint: Use schedule_interval, not run_every or schedule [OK]

Common Mistakes:

• Using wrong parameter names like run_every
• Forgetting quotes around schedule value
• Using invalid schedule strings

3. Given this DAG snippet, what is the order of task execution?

task1 >> task2 >> task3

medium

A. task3 runs first, then task2, then task1

B. All tasks run at the same time

C. task1 runs first, then task2, then task3

D. task2 runs first, then task1, then task3

Solution

1. Step 1: Understand the '>>' operator in Airflow

   The '>>' operator means 'set downstream', so task1 runs before task2, and task2 before task3.

2. Step 2: Determine execution order

   Tasks run in the order task1, then task2, then task3.

3. Final Answer:

   task1 runs first, then task2, then task3 -> Option C

4. Quick Check:

   task1 >> task2 >> task3 means sequential order [OK]

Hint: Remember '>>' means run left task before right task [OK]

Common Mistakes:

• Thinking '>>' means run tasks in parallel
• Reversing the order of tasks
• Assuming tasks run simultaneously

4. What is wrong with this DAG task dependency code?

task1.set_downstream(task2)
task2.set_downstream(task1)

medium

A. It creates a cycle, which is not allowed in DAGs

B. The syntax for set_downstream is incorrect

C. Tasks must be named differently

D. No error, this is valid

Solution

1. Step 1: Analyze the dependencies

   task1 depends on task2, and task2 depends on task1, creating a loop.

2. Step 2: Understand DAG rules

   DAGs cannot have cycles; tasks must not depend on each other circularly.

3. Final Answer:

   It creates a cycle, which is not allowed in DAGs -> Option A

4. Quick Check:

   DAGs must be acyclic, no loops allowed [OK]

Hint: Check for circular dependencies to avoid cycles [OK]

Common Mistakes:

• Ignoring circular dependencies
• Assuming set_downstream syntax is wrong
• Thinking task names cause errors

5. You want to create a DAG where task3 runs only after both task1 and task2 complete. Which code correctly sets this dependency?

hard

A. task3 >> task1
task3 >> task2

B. task1 >> task3
task2 >> task3

C. task1 << task3
task2 << task3

D. task3.set_downstream([task1, task2])

Solution

1. Step 1: Understand dependency direction

   To run task3 after task1 and task2, task1 and task2 must be upstream of task3.

2. Step 2: Check correct syntax

   Using '>>' from task1 and task2 to task3 sets task3 to run after both complete.

3. Final Answer:

   task1 >> task3 task2 >> task3 -> Option B

4. Quick Check:

   Upstream tasks >> downstream task [OK]

Hint: Use 'task1 >> task3' and 'task2 >> task3' for multiple dependencies [OK]

Common Mistakes:

• Reversing dependency arrows
• Using set_downstream incorrectly with lists
• Assuming task3 runs before task1 and task2