Scale dbt without scaling waste: How Fusion cuts redundant model runs

As dbt projects scale, small changes can trigger full model and test re-runs. That redundant work inflates warehouse spend and pipeline runtimes without improving data quality.

Fusion changes the equation. By running only what’s changed and optimizing test execution, teams are seeing 29%+ reductions in warehouse usage while maintaining fast, reliable pipelines.

In this on-demand event, you’ll learn:

• How teams running large dbt projects use state awareness to execute only changed models and their downstream dependencies
• How selective test execution reduces unnecessary warehouse compute while preserving data quality guarantees
• How eliminating redundant model runs enables teams to reduce warehouse usage by 29% or more

You’ll also hear from Obie Insurance and Analytics8 on how they’re modernizing with Fusion, including:

• How Obie Insurance is achieving approximately 30% model reuse through state-aware orchestration, resulting in meaningful compute savings
• How both teams are streamlining development workflows and reducing redundant work across large dbt environments
• How they’re evaluating Fusion to lower platform costs and simplify data operations

Meet the speakers:

Patrick Vinton

CTO

Analytics8

Tyson Dobernek

Senior data engineer

Obie Insurance

Matt Karan

Senior data engineer

Obie Insurance

Reuben McCreanor

Product manager

dbt Labs