Orchestrating dbt with Airflow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of using Airflow with dbt?

Airflow helps schedule and automate dbt tasks, so data transformations run reliably and on time without manual work.

beginner

How does Airflow execute dbt commands?

Airflow runs dbt commands as tasks inside its workflows (DAGs), usually by calling shell commands or using operators designed for dbt.

beginner

What is a DAG in Airflow?

A DAG (Directed Acyclic Graph) is a set of tasks with dependencies that Airflow runs in order. It defines the workflow for dbt jobs.

intermediate

Why is it useful to separate dbt models into different Airflow tasks?

Separating models lets you control the order, retry failed parts, and monitor progress clearly, making the workflow more reliable and easier to manage.

intermediate

What is the benefit of using Airflow sensors with dbt?

Sensors wait for certain conditions (like data availability) before starting dbt tasks, ensuring dbt runs only when data is ready.

What does Airflow use to define the order of dbt tasks?

A DAG (Directed Acyclic Graph)

B SQL Query

C Python Script

D JSON File

Airflow uses DAGs to define task order and dependencies.

Which Airflow component can wait for data before running dbt?

A Operator

B Sensor

C Hook

D Executor

Sensors wait for conditions like data availability before triggering tasks.

How do you run a dbt command inside Airflow?

A Using Airflow sensors only

B Writing SQL in Airflow UI

C Directly editing dbt models in Airflow

D Using a dbt operator or BashOperator to run shell commands

Airflow runs dbt commands by executing shell commands via operators.

What is a key benefit of orchestrating dbt with Airflow?

A Automated scheduling and monitoring of dbt runs

B Manual execution of dbt models

C Replacing dbt with Airflow

D Writing dbt models in Airflow

Airflow automates scheduling and monitoring, making dbt runs reliable.

Which file typically defines an Airflow DAG for dbt tasks?

A YAML file

B JSON file

C Python file

D dbt_project.yml

Airflow DAGs are defined in Python files.

Explain how Airflow helps manage dbt workflows and why this is useful.

Think about how automation saves time and reduces errors.

Describe the role of sensors in Airflow when orchestrating dbt jobs.

Sensors act like gatekeepers before tasks start.