What is Apache Airflow - Hands-On Activity

What is Apache Airflow

📖 Scenario: You are working in a team that needs to automate and schedule data processing tasks. Your team wants to understand how Apache Airflow can help manage these workflows efficiently.

🎯 Goal: Learn the basic concept of Apache Airflow and how it helps automate and schedule workflows.

📋 What You'll Learn

Understand what Apache Airflow is

Know the purpose of Apache Airflow

Recognize the main components of Apache Airflow

💡 Why This Matters

🌍 Real World

Teams use Apache Airflow to automate data processing jobs like daily reports or data cleaning without manual work.

💼 Career

Knowing Apache Airflow is useful for roles in data engineering, DevOps, and automation where managing workflows is important.

1

Introduction to Apache Airflow

Create a variable called definition and assign it the string value: "Apache Airflow is a platform to programmatically author, schedule, and monitor workflows."

Apache Airflow

# Create a variable called definition with the exact string value
# Your code here

Hint

Use a string variable named definition to store the exact sentence.

2

Add Purpose Explanation

Create a variable called purpose and assign it the string value: "It helps teams automate complex data pipelines and track their execution."

Apache Airflow

definition = "Apache Airflow is a platform to programmatically author, schedule, and monitor workflows."
# Create a variable called purpose with the exact string value
# Your code here

Hint

Use a string variable named purpose to store the exact sentence.

3

List Main Components

Create a list called components with these exact strings: "Scheduler", "Executor", "Web Server", "Metadata Database".

Apache Airflow

definition = "Apache Airflow is a platform to programmatically author, schedule, and monitor workflows."
purpose = "It helps teams automate complex data pipelines and track their execution."
# Create a list called components with the exact strings
# Your code here

Hint

Create a list named components with the four exact strings in the given order.

4

Display Apache Airflow Summary

Write a print statement that outputs the three variables definition, purpose, and components each on a new line.

Apache Airflow

definition = "Apache Airflow is a platform to programmatically author, schedule, and monitor workflows."
purpose = "It helps teams automate complex data pipelines and track their execution."
components = ["Scheduler", "Executor", "Web Server", "Metadata Database"]
# Print definition, purpose, and components each on a new line
# Your code here

Hint

Use three print statements, one for each variable.

Practice

1. What is the main purpose of Apache Airflow?

easy

A. To monitor network traffic

B. To store large amounts of data

C. To create user interfaces for websites

D. To automate and schedule tasks in a specific order

Solution

1. Step 1: Understand Apache Airflow's role

   Apache Airflow is designed to automate and schedule tasks, not to store data or create interfaces.

2. Step 2: Identify the correct purpose

   It manages workflows by running tasks in a defined order automatically.

3. Final Answer:

   To automate and schedule tasks in a specific order -> Option D

4. Quick Check:

   Automation and scheduling = A [OK]

Hint: Airflow schedules and runs tasks automatically [OK]

Common Mistakes:

• Confusing Airflow with data storage tools
• Thinking Airflow builds websites
• Assuming Airflow monitors networks

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

easy

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

B. dag = DAG('my_dag' schedule_interval='@daily')

C. dag = DAG['my_dag', schedule_interval='@daily']

D. dag = DAG{'my_dag', schedule_interval='@daily'}

Solution

1. Step 1: Check Python syntax for function calls

   Function calls use parentheses with arguments separated by commas.

2. Step 2: Identify correct syntax

   dag = DAG('my_dag', schedule_interval='@daily') uses parentheses and commas correctly; others have missing commas or wrong brackets.

3. Final Answer:

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

4. Quick Check:

   Function call syntax = A [OK]

Hint: Use parentheses and commas for function arguments [OK]

Common Mistakes:

• Missing commas between arguments
• Using square or curly brackets instead of parentheses
• Incorrect argument separation

3. Given the following Airflow DAG snippet, what will be the order of task execution?

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

dag = DAG('example_dag', start_date=datetime(2023,1,1))

t1 = DummyOperator(task_id='task1', dag=dag)
t2 = DummyOperator(task_id='task2', dag=dag)
t3 = DummyOperator(task_id='task3', dag=dag)

t1 >> t2 >> t3

medium

A. All tasks run at the same time

B. task3 runs, then task2, then task1

C. task1 runs, then task2, then task3

D. task2 runs first, then task1, then task3

Solution

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

   The '>>' operator sets task dependencies, meaning left task runs before right task.

2. Step 2: Trace the task order

   t1 >> t2 >> t3 means task1 runs first, then task2, then task3.

3. Final Answer:

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

4. Quick Check:

   Dependency chain = task1 -> task2 -> task3 [OK]

Hint: The '>>' operator means 'runs before' [OK]

Common Mistakes:

• Thinking tasks run in reverse order
• Assuming tasks run in parallel without dependencies
• Misreading the '>>' operator

4. You wrote this Airflow DAG code but it fails to run:

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

dag = DAG('my_dag', start_date=datetime(2023,1,1))

t1 = DummyOperator(task_id='task1')
t2 = DummyOperator(task_id='task2')

t1 >> t2

What is the error?

medium

A. Task IDs must be integers

B. Missing DAG parameter in DummyOperator tasks

C. start_date must be a string

D. Wrong import for DummyOperator

Solution

1. Step 1: Check task definitions

   Tasks need to be linked to a DAG by passing the 'dag' parameter.

2. Step 2: Identify missing 'dag' argument

   t1 and t2 are missing 'dag=dag', so Airflow doesn't know which DAG they belong to.

3. Final Answer:

   Missing DAG parameter in DummyOperator tasks -> Option B

4. Quick Check:

   Tasks need DAG assigned = D [OK]

Hint: Always assign tasks to a DAG with dag=dag [OK]

Common Mistakes:

• Forgetting to pass dag=dag to tasks
• Incorrect imports causing errors
• Wrong data types for start_date or task_id

5. You want to create an Airflow workflow that retries a task up to 3 times if it fails. Which DAG task parameter should you set?

hard

A. retries=3

B. retry_count=3

C. max_retry=3

D. retry_limit=3

Solution

1. Step 1: Recall Airflow retry parameter

   Airflow uses 'retries' to specify how many times to retry a failed task.

2. Step 2: Match parameter name

   The correct parameter is 'retries', not retry_count, max_retry, or retry_limit.

3. Final Answer:

   retries=3 -> Option A

4. Quick Check:

   Retries parameter controls retry count = B [OK]

Hint: Use 'retries' to set retry attempts in Airflow tasks [OK]

Common Mistakes:

• Using incorrect parameter names like retry_count
• Confusing retry count with timeout
• Not setting retries at all