Why Slim CI with state comparison in dbt? - Purpose & Use Cases

The Big Idea

What if you could cut your data build time by running only what really changed?

The Scenario

Imagine you have a big data project with many models. Every time you make a small change, you run all tests and builds from scratch, even if most models didn't change.

This means waiting a long time for results and using lots of computer power.

The Problem

Running everything every time is slow and wastes resources.

It's easy to miss which parts really need updating, causing confusion and errors.

Manual checks take too long and slow down your team's work.

The Solution

Slim CI with state comparison only runs tests and builds on models that actually changed.

It compares the current state with the previous one to find differences.

This saves time, reduces errors, and speeds up feedback.

Before vs After

✗ Before

dbt run
# runs all models every time

✓ After

dbt run --state path/to/previous_state --select state:modified
# runs only changed models

What It Enables

You can get faster, smarter updates that focus only on what really matters.

Real Life Example

A data team working on a large analytics project uses slim CI to test only updated models, cutting build time from hours to minutes.

Key Takeaways

Manual full runs waste time and resources.

State comparison finds exactly what changed.

Slim CI runs only needed tests and builds, speeding up work.