Airflow is ideal for orchestrating complex workflows that require more than just data transformation, such as tasks that involve data extraction, loading, and other operational steps, making it suitable for end-to-end pipeline management. Use Airflow when you need to integrate multiple data processing tasks that are not limited to SQL-based transformations, such as triggering an API call before loading data into a warehouse. For example, if you need to first fetch data from an API, transform it, and then load it into a database, Airflow would be the better choice.
You would use dbt over Airflow when you need to perform data transformations and modeling directly within your data warehouse using SQL. It is especially useful when your primary goal is to create analytics-ready tables and maintain data integrity through testing. A specific scenario to use dbt would be if your team needs to build a new reporting dashboard that requires transforming raw data into a clean and user-friendly dataset. Instead of using Airflow to orchestrate SQL scripts, you could use dbt to define the transformations as a series of SQL models, benefiting from its version control and testing capabilities.