Secrets management in Apache Airflow - Commands & Configuration

Introduction

Secrets management helps keep sensitive information like passwords and API keys safe when running workflows. It prevents exposing secrets directly in code or configuration files.

When you need to store database passwords securely for your Airflow tasks.

When you want to use API keys in your workflows without hardcoding them.

When multiple team members run workflows but should not see the secrets directly.

When you want to rotate or update secrets without changing your DAG code.

When you want to comply with security policies that forbid storing secrets in plain text.

Config File - airflow.cfg

airflow.cfg

[secrets]
backend = airflow.providers.hashicorp.secrets.vault.VaultBackend
backend_kwargs = {"connections_path": "airflow/connections", "kv_engine_version": 2, "url": "http://localhost:8200"}

This configuration tells Airflow to use HashiCorp Vault as the secrets backend.

backend: The Python class that manages secrets retrieval.

backend_kwargs: Parameters for connecting to Vault, including the path where secrets are stored and Vault URL.

Commands

Stores the database password securely in Vault under the path airflow/connections/my_db.

Terminal

vault kv put airflow/connections/my_db password='mysecretpassword'

Expected Output

Success! Data written to: airflow/connections/my_db

Retrieves the database connection details from Airflow, which fetches the password from Vault using the configured secrets backend.

Terminal

airflow connections get my_db

Expected Output

Conn Id: my_db

Conn Type: 

Host: 

Login: 

Password: mysecretpassword

Schema: 

Port: 

Extra: 

Lists all DAGs to confirm Airflow is running and can access secrets without errors.

Terminal

airflow dags list

Expected Output

dag_id

example_bash_operator

example_python_operator

Key Concept

If you remember nothing else from this pattern, remember: never store secrets directly in your DAG code or config files; always use a secrets backend.

Common Mistakes

Hardcoding passwords directly in DAG Python files.

This exposes sensitive data to anyone who can read the code and makes rotating secrets difficult.

Use Airflow's secrets backend to fetch passwords at runtime securely.

Not configuring the secrets backend in airflow.cfg.

Airflow will not know where to get secrets from and may fail or expose secrets in logs.

Set the backend and backend_kwargs properly in airflow.cfg before running workflows.

Storing secrets in plain text environment variables without encryption.

Environment variables can be exposed in process lists or logs.

Use a dedicated secrets manager like Vault integrated with Airflow.

Summary

Configure airflow.cfg to use a secrets backend like HashiCorp Vault.

Store secrets securely in Vault using the vault CLI.

Airflow fetches secrets at runtime, keeping them out of code and config files.

Practice

1. What is the main purpose of secrets management in Apache Airflow?

easy

A. To keep sensitive information like passwords out of the code

B. To speed up DAG execution

C. To store large datasets

D. To monitor task progress visually

Solution

1. Step 1: Understand what secrets management means

   Secrets management is about protecting sensitive data such as passwords or API keys from being exposed in code.

2. Step 2: Identify the main goal in Airflow context

   Airflow uses secrets management to keep such sensitive info safe and separate from DAG code.

3. Final Answer:

   To keep sensitive information like passwords out of the code -> Option A

4. Quick Check:

   Secrets management = protect sensitive info [OK]

Hint: Secrets management means hiding passwords from code [OK]

Common Mistakes:

• Confusing secrets management with data storage
• Thinking it speeds up DAGs
• Assuming it monitors tasks

2. Which of the following is the correct way to retrieve a secret stored as an Airflow Variable in a DAG?

easy

A. get_variable('my_secret')

B. Variable.get('my_secret')

C. Variable.fetch('my_secret')

D. fetch_variable('my_secret')

Solution

1. Step 1: Recall Airflow's Variable API

   Airflow provides a Variable class with a get() method to access stored variables or secrets.

2. Step 2: Match the correct syntax

   The correct method call is Variable.get('variable_name'), so Variable.get('my_secret') is correct.

3. Final Answer:

   Variable.get('my_secret') -> Option B

4. Quick Check:

   Use Variable.get() to access secrets [OK]

Hint: Use Variable.get() to fetch secrets in Airflow [OK]

Common Mistakes:

• Using non-existent methods like fetch or get_variable
• Confusing function names
• Missing the Variable class prefix

3. Given this Airflow DAG snippet, what will be the output if the secret 'db_password' is stored as 'mypassword123' in Airflow Variables?

from airflow.models import Variable

password = Variable.get('db_password')
print(password)

medium

A. Error: Variable not found

B. db_password

C. Variable.get('db_password')

D. mypassword123

Solution

1. Step 1: Understand Variable.get() behavior

   Variable.get('db_password') fetches the value stored under 'db_password' key, which is 'mypassword123'.

2. Step 2: Predict the print output

   Printing the variable will output the string 'mypassword123'.

3. Final Answer:

   mypassword123 -> Option D

4. Quick Check:

   Variable.get() returns stored secret value [OK]

Hint: Variable.get() returns the secret's stored value [OK]

Common Mistakes:

• Thinking it prints the key name
• Expecting the method call as output
• Assuming an error without checking storage

4. You wrote this code in your Airflow DAG to get a secret:

from airflow.models import Variable

api_key = Variable.get('api_key')

But you get an error: "KeyError: 'api_key' not found". What is the best way to fix this?

medium

A. Add a default value like Variable.get('api_key', default_var='default')

B. Change Variable.get to Variable.fetch

C. Remove the import statement

D. Rename 'api_key' to 'API_KEY'

Solution

1. Step 1: Understand the error cause

   The error means 'api_key' variable does not exist in Airflow Variables, so Variable.get() fails.

2. Step 2: Use default_var to avoid error

   Adding default_var='default' returns 'default' if the variable is missing, preventing the error.

3. Final Answer:

   Add a default value like Variable.get('api_key', default_var='default') -> Option A

4. Quick Check:

   Use default_var to handle missing secrets safely [OK]

Hint: Use default_var to avoid missing variable errors [OK]

Common Mistakes:

• Using non-existent Variable.fetch method
• Removing imports breaks code
• Changing variable name without updating storage

5. You want to securely store and access AWS credentials in Airflow using AWS Secrets Manager. Which approach correctly integrates this with Airflow's secrets backend?

hard

A. Hardcode AWS credentials in DAG files for faster access

B. Store credentials in Airflow Variables and access with Variable.get() only

C. Configure airflow.cfg with [secrets] backend = airflow.providers.amazon.aws.secrets.secrets_manager.SecretsManagerBackend

D. Use environment variables without any Airflow configuration

Solution

1. Step 1: Identify how Airflow integrates with AWS Secrets Manager

   Airflow supports multiple secrets backends configured in airflow.cfg under the [secrets] section.

2. Step 2: Correctly configure the secrets backend

   Setting backend to airflow.providers.amazon.aws.secrets.secrets_manager.SecretsManagerBackend enables Airflow to fetch secrets from AWS Secrets Manager securely.

3. Final Answer:

   Configure airflow.cfg with [secrets] backend = airflow.providers.amazon.aws.secrets.secrets_manager.SecretsManagerBackend -> Option C

4. Quick Check:

   Configure secrets backend for AWS Secrets Manager [OK]

Hint: Set secrets backend in airflow.cfg for AWS Secrets Manager [OK]

Common Mistakes:

• Hardcoding credentials in DAGs (unsafe)
• Using Variables only without backend config
• Ignoring Airflow secrets backend configuration