PR review workflows for dbt changes

Introduction

PR review workflows help teams check and improve dbt changes before adding them to the main project. This keeps data models accurate and reliable.

When a team member wants to add or update a dbt model or test.

Before merging changes that affect data transformations or sources.

To catch errors or inconsistencies early in the data pipeline.

When collaborating on dbt projects with multiple contributors.

To ensure code quality and maintain documentation for dbt models.

Syntax

dbt

1. Create a feature branch for your dbt changes.
2. Make your changes to models, tests, or documentation.
3. Push the branch to the remote repository.
4. Open a Pull Request (PR) against the main branch.
5. Run automated dbt tests and checks in the PR pipeline.
6. Review code and test results with your team.
7. Address feedback by updating the PR.
8. Once approved, merge the PR to main.

This workflow uses Git and a code hosting platform like GitHub or GitLab.

Automated tests often include running dbt deps, dbt run, and dbt test commands.

Examples

Create and push a new branch with your dbt model changes.

dbt

git checkout -b feature/add_new_model
# edit model files
git add models/new_model.sql
git commit -m "Add new sales model"
git push origin feature/add_new_model

Automated steps to install dependencies, load seeds, run models, and test them.

dbt

# In the PR pipeline
run: dbt deps
run: dbt seed
run: dbt run
run: dbt test

Team reviews the PR for quality and completeness.

dbt

Review comments:
- Check model logic for correctness.
- Verify tests cover new cases.
- Confirm documentation is updated.

Sample Program

This code simulates the automated steps in a PR pipeline for dbt changes. It runs dependencies, seeds, models, and tests, printing the output for review.

dbt

# Simulated PR review steps for dbt changes
import subprocess

# Step 1: Install dependencies
subprocess.run(['dbt', 'deps'], check=True)

# Step 2: Load seed data
subprocess.run(['dbt', 'seed', '--select', 'seed_data'], check=True)

# Step 3: Run models
result_run = subprocess.run(['dbt', 'run'], capture_output=True, text=True)
print(result_run.stdout)

# Step 4: Run tests
result_test = subprocess.run(['dbt', 'test'], capture_output=True, text=True)
print(result_test.stdout)

Important Notes

Always run dbt test to catch data issues early.

Use descriptive commit messages to explain your dbt changes.

Automate PR checks with CI tools like GitHub Actions or GitLab CI.

Summary

PR review workflows help keep dbt projects clean and reliable.

Use branches, automated tests, and team reviews before merging.

This process reduces errors and improves collaboration on data models.

Practice

1. What is the main purpose of using a PR (Pull Request) review workflow in dbt projects?

easy

A. To ensure changes are reviewed and tested before merging

B. To speed up merging without checks

C. To delete old branches automatically

D. To create new dbt models without review

Solution

1. Step 1: Understand PR review workflow purpose

   PR review workflows are designed to check code changes carefully before they become part of the main project.

2. Step 2: Identify the correct purpose in dbt context

   In dbt, this means reviewing and testing data models to avoid errors and keep the project reliable.

3. Final Answer:

   To ensure changes are reviewed and tested before merging -> Option A

4. Quick Check:

   PR review = review and test before merge [OK]

Hint: PR reviews catch errors before merging [OK]

Common Mistakes:

• Thinking PRs speed up merging without checks
• Confusing PR reviews with branch deletion
• Assuming PRs create models automatically

2. Which of the following is the correct syntax to create a new branch for a dbt PR workflow using Git?

easy

A. git branch new-feature

B. git merge new-feature

C. git push new-feature

D. git checkout -b new-feature

Solution

1. Step 1: Understand Git branch creation commands

   To create and switch to a new branch in one step, use git checkout -b branch-name.

2. Step 2: Identify the correct command for new branch creation

   git checkout -b new-feature uses git checkout -b new-feature, which creates and switches to the branch.

3. Final Answer:

   git checkout -b new-feature -> Option D

4. Quick Check:

   New branch creation = git checkout -b [OK]

Hint: Use git checkout -b to create and switch branch [OK]

Common Mistakes:

• Using git branch without switching
• Confusing merge with branch creation
• Trying to push before creating branch

3. Given this simplified dbt test command in a PR workflow:

dbt test --models my_model

What will happen if the model my_model has a failing test?

medium

A. The command deletes the model automatically

B. The command completes successfully with no error

C. The command fails and returns a non-zero exit code

D. The command ignores the test and continues

Solution

1. Step 1: Understand dbt test behavior

   When a dbt test fails, the command returns a non-zero exit code indicating failure.

2. Step 2: Identify the effect of a failing test in PR workflow

   This failure stops the PR from merging until the issue is fixed.

3. Final Answer:

   The command fails and returns a non-zero exit code -> Option C

4. Quick Check:

   Failing test = command fails with error code [OK]

Hint: Failing dbt tests cause command to fail [OK]

Common Mistakes:

• Assuming tests pass silently
• Thinking dbt deletes models on failure
• Believing tests are ignored automatically

4. You wrote a dbt model and created a PR, but the automated tests fail. Which of the following is the best way to debug the issue?

medium

A. Ignore the test failures and merge the PR

B. Check the test logs and fix the model SQL accordingly

C. Delete the model file and close the PR

D. Merge the PR and fix errors later in production

Solution

1. Step 1: Understand the role of test logs in debugging

   Test logs show which tests failed and why, helping identify SQL errors or data issues.

2. Step 2: Choose the correct debugging approach

   Fixing the model SQL based on logs ensures the PR passes tests before merging.

3. Final Answer:

   Check the test logs and fix the model SQL accordingly -> Option B

4. Quick Check:

   Debug by reviewing logs and fixing code [OK]

Hint: Use test logs to find and fix errors [OK]

Common Mistakes:

• Ignoring test failures
• Deleting models unnecessarily
• Merging broken code into main branch

5. In a team using dbt with PR review workflows, which combination of practices best ensures data model quality before merging?

hard

A. Create feature branches, run automated tests, and require team approval

B. Directly commit to main branch and run tests after merging

C. Skip tests and rely on manual checks only

D. Merge PRs without reviews to speed up deployment

Solution

1. Step 1: Identify best practices in PR workflows

   Using feature branches isolates changes, automated tests catch errors early, and team approval ensures quality review.

2. Step 2: Compare options to find the best combination

   Create feature branches, run automated tests, and require team approval includes all these practices, while others skip important steps or risk errors.

3. Final Answer:

   Create feature branches, run automated tests, and require team approval -> Option A

4. Quick Check:

   Branch + tests + review = quality assurance [OK]

Hint: Combine branches, tests, and reviews for quality [OK]

Common Mistakes:

• Committing directly to main branch
• Skipping automated tests
• Merging without team review