Multi-environment deployment (dev, staging, prod) in Apache Airflow - Mini Project: Build & Apply

Multi-environment deployment (dev, staging, prod) with Airflow

📖 Scenario: You are working as a data engineer managing workflows using Apache Airflow. You want to deploy the same workflow to three different environments: development, staging, and production. Each environment has its own configuration settings like schedule interval and retries.This project will guide you to create a simple Airflow DAG that adapts its behavior based on the environment it is deployed to.

🎯 Goal: Build an Airflow DAG that uses a configuration dictionary to set environment-specific parameters. You will create the base DAG, add environment configuration, apply the config to the DAG, and finally print the DAG details to verify the setup.

📋 What You'll Learn

Create a dictionary with environment configurations

Add a variable to select the current environment

Use the selected environment config to set DAG parameters

Print the DAG id and schedule interval to confirm correct setup

💡 Why This Matters

🌍 Real World

In real projects, teams deploy the same Airflow workflows to multiple environments to test changes safely before production. This setup helps avoid mistakes and downtime.

💼 Career

Understanding multi-environment deployment is essential for DevOps engineers and data engineers to manage workflows reliably and safely across development, testing, and production.

1

Create environment configurations dictionary

Create a dictionary called env_configs with these exact entries: 'dev', 'staging', and 'prod'. Each key should map to another dictionary with keys 'schedule_interval' and 'retries' set as follows:
'dev': {'schedule_interval': '@daily', 'retries': 1}
'staging': {'schedule_interval': '@hourly', 'retries': 2}
'prod': {'schedule_interval': '@once', 'retries': 3}

Apache Airflow

# Your code here

Hint

Use a dictionary with keys 'dev', 'staging', and 'prod'. Each key maps to another dictionary with 'schedule_interval' and 'retries'.

2

Set current environment variable

Create a variable called current_env and set it to the string 'staging' to select the staging environment.

Apache Airflow

env_configs = {
    'dev': {'schedule_interval': '@daily', 'retries': 1},
    'staging': {'schedule_interval': '@hourly', 'retries': 2},
    'prod': {'schedule_interval': '@once', 'retries': 3}
}
# Your code here

Hint

Assign the string 'staging' to the variable named current_env.

3

Create DAG using environment config

Import DAG and datetime from Airflow and Python respectively. Then create a DAG object called dag with dag_id set to 'example_dag', start_date set to datetime(2024, 1, 1), schedule_interval and default_args['retries'] set using the values from env_configs[current_env].

Apache Airflow

env_configs = {
    'dev': {'schedule_interval': '@daily', 'retries': 1},
    'staging': {'schedule_interval': '@hourly', 'retries': 2},
    'prod': {'schedule_interval': '@once', 'retries': 3}
}

current_env = 'staging'

# Your code here

Hint

Use env_configs[current_env]['schedule_interval'] for schedule_interval and env_configs[current_env]['retries'] for retries in default_args.

4

Print DAG details to verify environment setup

Write two print statements: one to print the string 'DAG ID:' followed by dag.dag_id, and another to print 'Schedule Interval:' followed by dag.schedule_interval.

Apache Airflow

from airflow import DAG
from datetime import datetime

env_configs = {
    'dev': {'schedule_interval': '@daily', 'retries': 1},
    'staging': {'schedule_interval': '@hourly', 'retries': 2},
    'prod': {'schedule_interval': '@once', 'retries': 3}
}

current_env = 'staging'

default_args = {'retries': env_configs[current_env]['retries']}

dag = DAG(
    dag_id='example_dag',
    start_date=datetime(2024, 1, 1),
    schedule_interval=env_configs[current_env]['schedule_interval'],
    default_args=default_args
)

# Your code here

Hint

Use print('DAG ID:', dag.dag_id) and print('Schedule Interval:', dag.schedule_interval).

Practice

1. What is the main reason to use separate environments like dev, staging, and prod in Airflow deployment?

easy

A. To run all tasks faster by using multiple servers

B. To reduce the number of DAGs needed

C. To keep development, testing, and production work isolated and safe

D. To share the same database for all environments

Solution

1. Step 1: Understand environment separation purpose

   Separate environments prevent changes in development or testing from affecting production stability.

2. Step 2: Identify the correct purpose in options

   Only To keep development, testing, and production work isolated and safe mentions isolation and safety, which matches the main reason.

3. Final Answer:

   To keep development, testing, and production work isolated and safe -> Option C

4. Quick Check:

   Environment isolation = Safety [OK]

Hint: Remember: separate environments protect production [OK]

Common Mistakes:

• Thinking all environments share the same database
• Believing environments speed up task execution
• Assuming fewer DAGs are needed with multiple environments

2. Which of the following is the correct way to specify different Airflow configurations for dev and prod environments?

easy

A. Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment

B. Run Airflow without any configuration files

C. Use the same airflow.cfg file for all environments

D. Use a single database for all environments

Solution

1. Step 1: Understand configuration methods in Airflow

   Airflow supports environment variables to override settings per environment.

2. Step 2: Identify the correct method for environment-specific configs

   Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment correctly uses environment variables to set different database connections per environment.

3. Final Answer:

   Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment -> Option A

4. Quick Check:

   Env vars = environment-specific config [OK]

Hint: Use env vars to separate configs per environment [OK]

Common Mistakes:

• Using the same config file for all environments
• Not setting database connections separately
• Ignoring environment variables for configs

3. Given this command to start Airflow in the staging environment:

export AIRFLOW_HOME=/opt/airflow_staging
export AIRFLOW__CORE__SQL_ALCHEMY_CONN=postgresql+psycopg2://user:pass@staging-db:5432/airflow
airflow webserver

What will happen when you run this command?

medium

A. Airflow will start using the staging database and configs in /opt/airflow_staging

B. Airflow will start using the production database and default configs

C. Airflow will fail because environment variables cannot be set this way

D. Airflow will start but ignore the SQL connection string

Solution

1. Step 1: Analyze environment variables set before starting Airflow

   Setting AIRFLOW_HOME changes the config directory; setting AIRFLOW__CORE__SQL_ALCHEMY_CONN changes the database connection.

2. Step 2: Understand Airflow behavior on startup

   Airflow uses these environment variables to load configs and connect to the specified database.

3. Final Answer:

   Airflow will start using the staging database and configs in /opt/airflow_staging -> Option A

4. Quick Check:

   Env vars control config and DB connection [OK]

Hint: Env vars before airflow start set environment [OK]

Common Mistakes:

• Assuming Airflow ignores env vars
• Thinking Airflow uses production DB by default
• Believing env vars cannot be set inline

4. You deployed Airflow for prod but accidentally used the dev database connection string in the config. What is the best way to fix this?

medium

A. Change the airflow.cfg file in the dev environment

B. Delete all DAGs and restart Airflow

C. Run Airflow without any environment variables

D. Change the AIRFLOW__CORE__SQL_ALCHEMY_CONN environment variable to the production database and restart Airflow

Solution

1. Step 1: Identify the incorrect database connection setting

   The problem is the wrong database connection string used in production.

2. Step 2: Correct the environment variable and restart Airflow

   Updating AIRFLOW__CORE__SQL_ALCHEMY_CONN to the correct production DB and restarting applies the fix.

3. Final Answer:

   Change the AIRFLOW__CORE__SQL_ALCHEMY_CONN environment variable to the production database and restart Airflow -> Option D

4. Quick Check:

   Fix DB connection env var and restart [OK]

Hint: Fix DB env var, then restart Airflow [OK]

Common Mistakes:

• Deleting DAGs unnecessarily
• Ignoring environment variables
• Changing dev configs instead of prod

5. You want to deploy Airflow for dev, staging, and prod on the same server. Which approach ensures proper isolation and avoids conflicts?

hard

A. Run one Airflow instance and switch configs manually before each run

B. Run three separate Airflow instances with different AIRFLOW_HOME and database connections

C. Use the same database but different DAG folders for each environment

D. Run Airflow only in production mode and test DAGs there

Solution

1. Step 1: Understand isolation needs for multiple environments on one server

   Each environment needs its own config directory and database to avoid conflicts.

2. Step 2: Identify the correct deployment approach

   Running separate Airflow instances with distinct AIRFLOW_HOME and DB connections ensures isolation.

3. Final Answer:

   Run three separate Airflow instances with different AIRFLOW_HOME and database connections -> Option B

4. Quick Check:

   Separate instances + configs = isolation [OK]

Hint: Use separate AIRFLOW_HOME and DB per instance [OK]

Common Mistakes:

• Switching configs manually risks errors
• Sharing one database causes conflicts
• Running only production ignores testing needs