From on-prem monolith to AI-ready: 37% cost savings with dbt and Fusion

25% cost savings. 41% improvement in asset build reuse. 41% time savings. Those are the tangible results from a two-phase modernization of AllianceBernstein's Private Alternatives data platform, and this session covers how we achieved these results with Fusion.

We started by migrating off an on-premises orchestration stack built on Airflow, Python, and SQL, moving to cloud-native analytics engineering with dbt and Snowflake. That migration standardized our transformation logic, made the tech stack agile, and gave us the foundation for what came next: upgrading to the dbt Fusion engine and leveraging dbt State to scale performance, governance, and AI-readiness while optimizing compute costs.

You'll leave with honest lessons from both phases: what the migration actually involved, how Fusion improved day-to-day development ergonomics, and how our new architecture optimized iteration speed, model dependencies, and costs at scale.