Orchestrating dbt with Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Airflow DAG class.

dbt

from airflow.[1] import DAG

Drag options to blanks, or click blank then click option'

Aoperators

Bmodels

Cdags

Dtasks

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DAG from airflow.operators instead of airflow.models

Using incorrect module names like airflow.dags

The DAG class is imported from airflow.models to define workflows.

2fill in blank

medium

Complete the code to create a dbt run task using BashOperator.

dbt

dbt_run = BashOperator(task_id='dbt_run', bash_command='dbt [1]')

Drag options to blanks, or click blank then click option'

Arun

Bcompile

Ctest

Dseed

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dbt test' instead of 'dbt run' for model execution

Using 'dbt compile' which only compiles SQL without running

The 'dbt run' command executes the models in the project.

3fill in blank

hard

Fix the error in the DAG definition by completing the default_args dictionary key for retries.

dbt

default_args = {
    'owner': 'airflow',
    'start_date': days_ago(1),
    'retries': [1]
}

Drag options to blanks, or click blank then click option'

A'3'

BNone

CTrue

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Putting retries as a string like '3' causing type errors

Using boolean or None instead of an integer

The 'retries' value must be an integer, not a string.

4fill in blank

hard

Fill both blanks to set the schedule interval to daily and enable catchup.

dbt

dag = DAG('dbt_dag', default_args=default_args, schedule_interval=[1], catchup=[2])

Drag options to blanks, or click blank then click option'

A'@daily'

BFalse

CTrue

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using None for schedule_interval which disables scheduling

Setting catchup to True causing unexpected backfills

Setting schedule_interval to '@daily' runs the DAG daily. Setting catchup to False disables backfilling.

5fill in blank

hard

Fill all three blanks to define task dependencies: dbt_run runs before dbt_test, which runs before dbt_docs.

dbt

dbt_run [1] dbt_test
 dbt_test [2] dbt_docs
 dag = DAG('dbt_dag', default_args=default_args, schedule_interval='@daily')
 dbt_docs = BashOperator(task_id='dbt_docs', bash_command='dbt docs generate')

Drag options to blanks, or click blank then click option'

A>>

D<<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<<' which reverses the order

Not setting dependencies causing tasks to run in parallel

The '>>' operator sets the order of tasks in Airflow DAGs, meaning the left task runs before the right task.

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