How a global investment firm reduced runtimes by 30–40% with the dbt Fusion engine
This matters because reliable transformation is becoming a strategic layer in analytics delivery, improving trust, reuse, and the quality of business-facing data products.
How a global investment firm reduced runtimes by 30–40% with the dbt Fusion engine
NBIM cut runtimes 30–40% in 3 months with the dbt Fusion engine and State-Aware Orchestration—without heavy optimization.
Editorial Analysis
The 30-40% runtime improvement NBIM achieved signals a maturation in dbt's execution layer that addresses a real pain point: most teams optimize transforms manually, burning cycles on query tuning when the platform itself could handle orchestration smarter. State-Aware Orchestration is the key insight here—skipping redundant computations based on upstream state changes reduces wasted CPU, which compounds across large asset graphs. For data engineering teams, this suggests we're moving past the 'dbt as a modeling tool' phase into 'dbt as an execution engine.' The implication is operational: you no longer need heavy-handed solutions like incremental models as a band-aid for performance. You can focus on correctness and modularity, letting the platform optimize. This also strengthens the case for consolidating your transformation layer—fewer tools, less glue code. My recommendation: if your DAGs exceed 500+ models or runtime exceeds 2+ hours, audit your current orchestration assumptions. You're likely already paying for optimization you shouldn't need to build yourself.