Why PR review workflows for dbt changes? - Purpose & Use Cases

The Big Idea

What if every dbt change could be safely checked without endless emails or confusion?

The Scenario

Imagine you and your team are working on a big data project using dbt. Every time someone changes a model or a test, you have to manually check the code, run it locally, and then ask others to review it by sending files or messages. This back-and-forth can get confusing and slow.

The Problem

Doing reviews manually means you might miss errors, forget to test some changes, or accidentally overwrite someone else's work. It takes a lot of time to coordinate, and mistakes can cause broken data models or wrong reports.

The Solution

PR review workflows automate this process. When you make a change in dbt, it creates a pull request that others can easily review online. Tests run automatically, and everyone can comment or approve changes in one place. This keeps the project safe and speeds up teamwork.

Before vs After

✗ Before

Run dbt models locally
Email team for review
Wait for feedback
Merge manually

✓ After

Create PR with dbt changes
Automated tests run
Team reviews in PR
Merge after approval

What It Enables

It enables smooth, safe collaboration where every dbt change is checked and approved before becoming part of the project.

Real Life Example

A data team updating sales models can quickly spot errors in new calculations through PR reviews, preventing wrong numbers from reaching dashboards.

Key Takeaways

Manual reviews are slow and risky for dbt projects.

PR workflows automate testing and feedback.

Teams collaborate safely and efficiently on data changes.

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