The dbt Fusion engine public beta is now available on BigQuery

Exciting news on the dbt Fusion engine front: teams using BigQuery can now participate in the public beta of Fusion. Since the initial beta launch of Fusion in late May, which included support for Snowflake, we’ve expanded data platform support to include Databricks, and now BigQuery. This is an important milestone in our path towards GA, as BigQuery is one of the most-used adapters across all of dbt, and now these users will be able to experience the power of Fusion.

Fusion isn’t just an incremental improvement to dbt—it’s a fundamental shift in speed, intelligence, and developer experience for anyone building with dbt, and we’re excited to extend these benefits to the BigQuery ecosystem. Read the docs to learn more and get started with Fusion.

The dbt Fusion engine now on BigQuery

The dbt Fusion engine represents the technological foundation for a new era of analytics engineering. Born from a complete ground-up rewrite of dbt in Rust with SQL comprehension built in, Fusion is designed for performance at scale, pairing blazing-fast execution with deep understanding of your analytics code.

For BigQuery users, this means:

• Improved developer experience: Fusion doesn’t just execute your SQL; it understands it. SQL comprehension gives developers real-time feedback and error checking as they write SQL, catching mistakes and providing suggestions instantly, without executing queries against the warehouse. This now precisely understands BigQuery SQL and provides all the same benefits to this ecosystem.

• Significant performance gains: Project parsing and compilation are as much as 30x faster than with dbt Core, dramatically improving velocity and reducing turnaround time from code change to insight.

• Automatic cost optimization: Fusion is state-aware, meaning it has an always up-to-date understanding of what's in the project code or what exists in the database, bringing a new level of intelligence to dbt and the platform experiences we deliver. For example, with state-aware orchestration, dbt now knows what’s changed in your DAG and only runs models with new data. Not only does this improve data velocity and eliminate redundant workloads, it helps organizations reduce cloud compute spending by 10% or more…just by turning it on.

• More detailed governance (coming soon): Precise column-level lineage and richer metadata will soon provide enhanced data governance, making it easier to manage risk, enforce policies, and lay the foundation for safe AI applications downstream.

Fusion is a game-changer: it empowers analytics engineers to move faster, iterate with confidence, and focus more on business logic than debugging or firefighting performance issues.

Who benefits?

• Data engineers and analytics engineers running transformations at scale on BigQuery will see dramatically reduced parse times of dbt projects, lower cloud spend, and richer metadata for downstream consumers.
• Organizations requiring strict governance and/or auditability gain robust lineage, improved cost management, and the freedom to scale beyond a single data warehouse.

This is especially valuable for companies orchestrating complex pipelines, handling sensitive data, or operating in hybrid/multi-cloud environments.

Getting started with Fusion

Fusion is now in public beta for projects on Snowflake, Databricks, and BigQuery. Stay tuned for more upcoming platform support and feature coverage in our path towards GA.

You can get started with Fusion today with the freely available and permissively licensed source code and binary, or try it locally using the dbt VS Code Extension.

If you’re a managed dbt customer and your project(s) are eligible, you can enable Fusion with just a few clicks and unlock premium features like state-aware orchestration, cost dashboards, and enterprise governance.

Ready to dive in? Explore the docs or watch a demo.