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.

Practice

1. What is the main purpose of using Airflow with dbt in data workflows?

easy

A. To automate and schedule dbt commands in a specific order

B. To replace dbt's SQL models with Python scripts

C. To visualize dbt models as charts

D. To manually run dbt commands from the command line

Solution

1. Step 1: Understand Airflow's role

   Airflow is a tool to automate and schedule tasks, including dbt commands.

2. Step 2: Connect Airflow with dbt

   Using Airflow, you can run dbt commands automatically and in order without manual intervention.

3. Final Answer:

   To automate and schedule dbt commands in a specific order -> Option A

4. Quick Check:

   Airflow automates dbt runs = C [OK]

Hint: Airflow schedules tasks; dbt runs models. Together automate workflows [OK]

Common Mistakes:

• Thinking Airflow replaces dbt's SQL modeling
• Confusing manual and automated runs
• Assuming Airflow visualizes dbt models

2. Which Airflow operator is commonly used to run dbt commands inside a DAG?

easy

A. BashOperator

B. PythonOperator

C. HttpOperator

D. EmailOperator

Solution

1. Step 1: Identify how dbt commands run

   dbt commands run in the shell or terminal, so Airflow needs an operator that runs shell commands.

2. Step 2: Match operator to shell commands

   BashOperator runs bash shell commands, perfect for running dbt CLI commands inside Airflow.

3. Final Answer:

   BashOperator -> Option A

4. Quick Check:

   BashOperator runs shell commands = A [OK]

Hint: Use BashOperator for shell commands like dbt runs [OK]

Common Mistakes:

• Choosing PythonOperator which runs Python code, not shell commands
• Confusing HttpOperator with web requests
• Thinking EmailOperator runs commands

3. Given this Airflow DAG snippet, what is the order of task execution?

dbt_run = BashOperator(task_id='dbt_run', bash_command='dbt run')
dbt_test = BashOperator(task_id='dbt_test', bash_command='dbt test')
dbt_run >> dbt_test

medium

A. dbt_run and dbt_test run in parallel

B. dbt_run runs first, then dbt_test

C. dbt_test runs first, then dbt_run

D. Only dbt_test runs

Solution

1. Step 1: Understand the dependency operator

   The '>>' operator in Airflow means the left task runs before the right task.

2. Step 2: Apply to given tasks

   dbt_run >> dbt_test means dbt_run runs first, then dbt_test runs after it finishes.

3. Final Answer:

   dbt_run runs first, then dbt_test -> Option B

4. Quick Check:

   dbt_run >> dbt_test means run then test = A [OK]

Hint: >> means left task runs before right task [OK]

Common Mistakes:

• Thinking >> means parallel execution
• Reversing the order of tasks
• Assuming only one task runs

4. Identify the error in this Airflow task setup for running dbt tests:

dbt_test = BashOperator(task_id='dbt_test', bash_command='dbt test')
dbt_test.set_upstream('dbt_run')

medium

A. bash_command should be a list, not a string

B. task_id cannot be 'dbt_test'

C. set_upstream expects a task object, not a string

D. BashOperator cannot run dbt commands

Solution

1. Step 1: Check set_upstream usage

   set_upstream expects another task object, not a string task id.

2. Step 2: Identify correct usage

   It should be dbt_test.set_upstream(dbt_run) where dbt_run is the task object, not 'dbt_run' string.

3. Final Answer:

   set_upstream expects a task object, not a string -> Option C

4. Quick Check:

   set_upstream needs task object = B [OK]

Hint: set_upstream needs task object, not string name [OK]

Common Mistakes:

• Passing string instead of task object to set_upstream
• Thinking bash_command must be a list
• Believing task_id has naming restrictions
• Assuming BashOperator can't run dbt

5. You want to create an Airflow DAG that runs dbt run, then dbt test, and finally sends an email notification only if tests pass. Which sequence correctly sets task dependencies?

hard

A. dbt_run >> email_notify >> dbt_test

B. email_notify >> dbt_run >> dbt_test

C. dbt_test >> dbt_run >> email_notify

D. dbt_run >> dbt_test >> email_notify

Solution

1. Step 1: Define correct task order

   First, run models with dbt run, then test them with dbt test, and finally notify if tests pass.

2. Step 2: Set dependencies accordingly

   Use '>>' to chain tasks in order: dbt_run >> dbt_test >> email_notify.

3. Final Answer:

   dbt_run >> dbt_test >> email_notify -> Option D

4. Quick Check:

   Run then test then notify = D [OK]

Hint: Chain tasks in order with >>: run, test, then notify [OK]

Common Mistakes:

• Placing email notification before tests
• Reversing run and test order
• Skipping dependencies causing parallel runs