Orchestrating dbt with Airflow

Introduction

We use Airflow to run dbt tasks automatically and in order. This helps keep data fresh and organized without doing it by hand.

You want to update your data models every day without manual work.

You need to run tests on your data after it changes.

You want to run multiple dbt commands in a specific order.

You want to get alerts if something goes wrong in your data pipeline.

You want to combine dbt with other data tasks like loading or cleaning.

Syntax

dbt

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG('dbt_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    run_dbt = BashOperator(
        task_id='run_dbt',
        bash_command='dbt run --profiles-dir /path/to/profiles'
    )

Use BashOperator to run dbt commands as shell commands.

Set schedule_interval to control how often dbt runs.

Examples

Runs all dbt models once.

dbt

run_dbt = BashOperator(
    task_id='dbt_run',
    bash_command='dbt run'
)

Runs tests on your dbt models to check data quality.

dbt

test_dbt = BashOperator(
    task_id='dbt_test',
    bash_command='dbt test'
)

This makes sure tests run only after models finish running.

dbt

run_dbt >> test_dbt

Sample Program

This Airflow DAG runs dbt models first, then runs tests to check data quality. It runs every day automatically.

dbt

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG('dbt_workflow', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    dbt_run = BashOperator(
        task_id='dbt_run',
        bash_command='dbt run --profiles-dir /home/user/.dbt'
    )

    dbt_test = BashOperator(
        task_id='dbt_test',
        bash_command='dbt test --profiles-dir /home/user/.dbt'
    )

    dbt_run >> dbt_test

Important Notes

Make sure Airflow can find your dbt profiles by setting --profiles-dir correctly.

You can add more tasks like dbt seed or dbt snapshot as needed.

Use Airflow's UI to monitor your dbt runs and see logs if something fails.

Summary

Airflow helps run dbt commands automatically and in order.

Use BashOperator to run dbt commands inside Airflow tasks.

Set task dependencies so dbt tests run after models finish.

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