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.