Data teams often spend countless hours refreshing data models that haven’t changed, leading to wasted time and increased costs. The dbt Fusion engine addresses this inefficiency by achieving over 29% savings in data warehouse usage. It accomplishes this by executing only the necessary processes based on changes, optimizing test runs, and employing configurations that prevent unnecessary model executions. This approach reduces compute usage without sacrificing data freshness or reliability. During a recent virtual event, experts shared insights on how organizations are leveraging Fusion to streamline their data operations. Companies are seeing significant reductions in warehouse costs by eliminating redundant tasks and creating more efficient pipelines. The centralized orchestration provided by Fusion leads to more reliable data delivery and enhances the developer experience through faster iterations and improved debugging capabilities. Companies like Obie Insurance are achieving about 30% model reuse through state-aware orchestration, which contributes to substantial compute savings. Speakers from Analytics8 and Obie Insurance discussed their experiences with Fusion, highlighting how they are optimizing their operations. Both teams are focused on reducing redundant work and exploring ways to lower platform costs. The advancements offered by Fusion are seen as a way to scale operations effectively while supporting business growth.