PR review workflows for dbt changes - Deep Dive

Overview - PR review workflows for dbt changes

What is it?

PR review workflows for dbt changes are structured processes to check and approve changes made to dbt projects before merging them into the main codebase. They help teams collaborate safely by reviewing SQL models, tests, and documentation changes. This ensures that data transformations are correct, consistent, and do not break existing reports or pipelines.

Why it matters

Without PR review workflows, errors in data models or transformations can go unnoticed, causing wrong data insights and business decisions. These workflows prevent bugs, improve code quality, and maintain trust in data. They also help teams share knowledge and catch mistakes early, saving time and effort in fixing issues later.

Where it fits

Learners should first understand dbt basics like models, tests, and version control with Git. After mastering PR review workflows, they can explore advanced CI/CD automation, data quality monitoring, and deployment strategies for dbt projects.

Mental Model

Core Idea

A PR review workflow for dbt changes is a team checkpoint that verifies data transformation code before it becomes part of the main project, ensuring quality and reliability.

Think of it like...

It's like having a safety inspector check every new part added to a car before it leaves the factory, making sure nothing will cause a breakdown later.

┌───────────────┐      ┌───────────────┐      ┌───────────────┐
│ Developer     │─────▶│ Pull Request  │─────▶│ Reviewers     │
│ makes changes │      │ created       │      │ check changes │
└───────────────┘      └───────────────┘      └───────────────┘
                                │                      │
                                ▼                      ▼
                        ┌───────────────┐      ┌───────────────┐
                        │ Automated     │      │ Feedback &    │
                        │ Tests run     │      │ Approval      │
                        └───────────────┘      └───────────────┘
                                │                      │
                                └───────────────┬──────┘
                                                ▼
                                       ┌───────────────┐
                                       │ Merge to main │
                                       │ branch        │
                                       └───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding dbt Project Structure

Concept: Learn what files and folders make up a dbt project and their roles.

A dbt project contains SQL models, tests, macros, and documentation files organized in folders. Models define data transformations. Tests check data quality. Macros are reusable SQL snippets. Documentation explains models. Knowing this helps you understand what changes need review.

Result

You can identify which parts of a dbt project are affected by a change.

Understanding the project structure is essential to know what reviewers should focus on during PR reviews.

2

Foundation

Basics of Git and Pull Requests

Concept: Learn how Git tracks changes and how pull requests propose code updates.

Git records changes to files over time. Developers create branches to work on features or fixes. A pull request (PR) is a request to merge these changes into the main branch. PRs allow others to review and discuss changes before merging.

Result

You can create and submit a PR with your dbt changes for review.

Knowing Git and PR basics is necessary to participate in collaborative dbt development.

3

Intermediate

Setting Up Automated Tests for dbt PRs

🤔Before reading on: do you think automated tests run before or after manual review? Commit to your answer.

Concept: Introduce automated testing in PR workflows to catch errors early.

Automated tests run dbt commands like 'dbt run' and 'dbt test' on the PR branch using CI tools. This checks if models compile and tests pass without manual effort. If tests fail, the PR is blocked from merging until fixed.

Result

PRs with broken models or failing tests are automatically flagged, preventing bad code from merging.

Automated tests save time and reduce human error by catching issues before manual review.

4

Intermediate

Reviewing SQL and Test Changes in PRs

🤔Before reading on: do you think reviewers should check only SQL code or also tests and docs? Commit to your answer.

Concept: Reviewers check not just SQL models but also tests and documentation changes.

Reviewers read the SQL code for logic errors, check if tests cover new cases, and verify documentation updates. They comment on unclear or risky changes. This holistic review ensures data quality and maintainability.

Result

PRs get feedback that improves code correctness and clarity before merging.

Reviewing all related files prevents gaps that could cause data issues later.

5

Intermediate

Using dbt Artifacts in PR Reviews

Concept: Leverage dbt-generated files like manifest.json and run_results.json to aid reviews.

dbt creates artifacts during runs that describe model dependencies and test results. Reviewers and CI tools can use these to understand impact scope and test outcomes. This helps prioritize review focus and verify correctness.

Result

Reviewers gain deeper insight into changes and their effects without running dbt locally.

Using artifacts makes reviews more efficient and informed.

6

Advanced

Integrating PR Workflows with Data Quality Monitoring

🤔Before reading on: do you think PR reviews alone guarantee data quality in production? Commit to your answer.

Concept: Combine PR reviews with ongoing data quality monitoring for robust pipelines.

PR reviews catch issues before merging, but data quality monitoring tools track data freshness and anomalies in production. Integrating alerts with PR workflows helps catch problems early and link them to recent changes.

Result

Teams can quickly identify and fix data issues related to recent dbt changes.

Understanding that PR reviews are one part of a larger quality system prevents overreliance on code checks alone.

7

Expert

Handling Complex Dependency and Merge Conflicts in dbt PRs

🤔Before reading on: do you think dbt model dependencies can cause tricky merge conflicts? Commit to your answer.

Concept: Advanced strategies to manage dependency chains and resolve conflicts in collaborative dbt projects.

dbt models depend on each other, so changes in one model can affect many downstream models. When multiple PRs change related models, merge conflicts or test failures can occur. Experts use feature branches, rebase workflows, and dependency graphs to plan merges and avoid conflicts.

Result

Teams maintain stable main branches and reduce integration headaches.

Knowing how to manage dependencies and conflicts is key to scaling dbt projects in teams.

Under the Hood

When a PR is created, CI systems check out the PR branch and run dbt commands to compile models and execute tests. dbt parses SQL files, builds a dependency graph, and runs models in order. Test results and logs are collected as artifacts. Reviewers use these outputs plus code diffs to assess changes. Merging updates the main branch, triggering downstream deployments.

Why designed this way?

This workflow balances automation and human judgment. Automated tests catch obvious errors fast, while human reviewers catch logic, style, and business rule issues. Using Git and PRs leverages existing developer tools and collaboration patterns. The design evolved to reduce data errors and improve team productivity.

┌───────────────┐      ┌───────────────┐      ┌───────────────┐
│ Developer     │─────▶│ GitHub/GitLab │─────▶│ CI System     │
│ pushes branch │      │ creates PR    │      │ runs dbt      │
└───────────────┘      └───────────────┘      └───────────────┘
                                │                      │
                                ▼                      ▼
                        ┌───────────────┐      ┌───────────────┐
                        │ Reviewers     │◀────│ Test Results  │
                        │ review code   │      │ and Artifacts │
                        └───────────────┘      └───────────────┘
                                │                      │
                                └───────────────┬──────┘
                                                ▼
                                       ┌───────────────┐
                                       │ Merge to main │
                                       │ branch        │
                                       └───────────────┘

Myth Busters - 3 Common Misconceptions

Quick: Do you think automated tests in PRs catch all data quality issues? Commit yes or no.

Common Belief: Automated tests in PRs guarantee perfect data quality in production.

Reality: Automated tests check only what is defined in dbt tests and models; they cannot catch all data anomalies or external data source issues.

Why it matters: Relying solely on PR tests can lead to undetected data problems in production, causing wrong business decisions.

Quick: Do you think reviewers only need to check SQL code, ignoring tests and docs? Commit yes or no.

Common Belief: Reviewers only need to focus on SQL code changes; tests and docs are less important.

Reality: Tests and documentation are critical parts of dbt projects and must be reviewed to ensure data quality and maintainability.

Why it matters: Ignoring tests or docs can cause missing coverage or outdated explanations, leading to confusion and errors.

Quick: Do you think merge conflicts in dbt PRs are always simple to resolve? Commit yes or no.

Common Belief: Merge conflicts in dbt projects are straightforward and rare.

Reality: Due to model dependencies and multiple contributors, conflicts can be complex and require careful resolution strategies.

Why it matters: Underestimating conflict complexity can cause broken pipelines and delays in deployment.

Expert Zone

1

Reviewing dbt PRs requires understanding the model dependency graph to assess the impact of changes beyond just the modified files.

2

Automated tests should include schema, data, and custom tests to cover different failure modes, not just basic compilation checks.

3

Effective PR workflows integrate with deployment pipelines to enable safe, incremental releases and rollback capabilities.

When NOT to use

PR review workflows are less effective for very small teams or solo projects where informal reviews or direct commits may suffice. In such cases, lightweight testing and monitoring might replace formal PR processes.

Production Patterns

In production, teams use branch protection rules to enforce passing tests and approvals before merging. They integrate PR workflows with CI/CD pipelines that deploy dbt models to production environments automatically after merge.

Connections

Continuous Integration (CI)

PR review workflows build on CI principles by automating tests and checks on proposed changes.

Understanding CI helps grasp how automated testing fits into PR workflows to improve code quality.

Software Code Review

PR review workflows for dbt are a specialized form of code review focused on data transformation code.

Knowing general code review practices helps apply best practices to dbt projects.

Quality Control in Manufacturing

PR reviews act like quality control checkpoints ensuring each change meets standards before release.

Seeing PR reviews as quality control highlights their role in preventing defects and maintaining trust.

Common Pitfalls

#1 Skipping automated tests and relying only on manual review.

Wrong approach:

Merge PRs after manual approval without running 'dbt test' in CI.

Correct approach:

Configure CI to run 'dbt run' and 'dbt test' on every PR before allowing merge.

Root cause:

Underestimating the value of automated checks leads to missed errors and unstable data pipelines.

#2 Reviewing only changed SQL files and ignoring test or documentation updates.

Wrong approach:

Approve PRs without checking changes in tests or docs folders.

Correct approach:

Review all changed files including tests and documentation to ensure completeness.

Root cause:

Misunderstanding that tests and docs are integral parts of dbt projects causes incomplete reviews.

#3 Ignoring merge conflicts or resolving them without understanding model dependencies.

Wrong approach:

Force merge conflicting PRs without checking downstream model impacts.

Correct approach:

Use dbt's dependency graph and rebase workflows to carefully resolve conflicts and test thoroughly before merging.

Root cause:

Lack of awareness of dbt model dependencies leads to broken pipelines after merges.

Key Takeaways

PR review workflows for dbt changes combine automated testing and human review to ensure data transformation quality.

Understanding dbt project structure and Git basics is essential before implementing PR workflows.

Automated tests catch many errors early, but reviewers must also check tests and documentation for full coverage.

Managing model dependencies and merge conflicts is critical for smooth collaboration in dbt projects.

PR workflows are part of a larger data quality system including monitoring and deployment automation.