PR review workflows for dbt changes - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of a PR review workflow in dbt projects?

To ensure that changes to dbt models, tests, and documentation are correct, follow best practices, and do not break existing data pipelines before merging into the main branch.

beginner

Which dbt command is commonly run during PR reviews to validate changes?

The dbt run command to build models and dbt test to run tests, ensuring the new changes work as expected.

intermediate

Why is it important to run dbt docs generate in a PR workflow?

It updates the project documentation to reflect any changes in models or sources, helping reviewers understand the impact and maintain clear data lineage.

intermediate

What role do automated CI/CD pipelines play in PR review workflows for dbt?

They automatically run dbt commands like dbt run, dbt test, and dbt docs generate on PRs to catch errors early and speed up the review process.

beginner

Name one best practice for managing dbt PR reviews to keep the data pipeline stable.

Keep PRs small and focused on a single change, run all tests, and review model dependencies carefully to avoid unintended side effects.

What is the first step in a typical dbt PR review workflow?

A Run <code>dbt docs generate</code>

B Merge the PR immediately

C Run <code>dbt run</code> to build models

D Delete the branch

Running dbt run builds the models and checks if the changes work before merging.

Which command checks if your dbt models meet quality standards during PR review?

A <code>dbt test</code>

B <code>dbt run</code>

C <code>dbt seed</code>

D <code>dbt snapshot</code>

dbt test runs tests defined in your project to validate data quality.

Why should documentation be updated in a dbt PR workflow?

A Documentation is not important

B To confuse reviewers

C To slow down the review process

D To reflect changes and maintain clear data lineage

Updated documentation helps everyone understand the changes and their impact.

What is a benefit of using automated CI/CD pipelines in dbt PR reviews?

A They automatically run tests and catch errors early

B They make the process slower

C They replace human reviewers

D They delete branches automatically

Automation speeds up reviews and reduces human error by running tests automatically.

Which practice helps keep dbt PR reviews effective?

A Making very large PRs with many changes

B Keeping PRs small and focused

C Ignoring test failures

D Skipping documentation updates

Small, focused PRs are easier to review and less likely to cause issues.

Explain the key steps in a PR review workflow for dbt changes.

Think about how you check your work and share updates with your team.

Describe why automated testing and documentation are important in dbt PR reviews.

Consider how automation and clear info help teamwork.

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