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In dbt Talk, dbt webinar

dbt Fusion Explained: The Next Step in dbt’s Evolution

dbt Fusion Engine

As data teams continue to scale and the demand for faster, more reliable analytics grows, the tools we depend on must evolve. Enter dbt Fusion, the latest high‑performance execution engine from dbt Labs that promises to take your dbt workflows to unprecedented speeds. In this post, we’ll dive deep into what dbt Fusion is, explore its key features, discuss supported platforms and migration paths, and help you decide if—and when—you should upgrade. Let’s get started!

In this article:



Why a New Engine?

dbt (data build tool) has revolutionized how analytics engineers transform and test data directly within the data warehouse. Until now, both dbt Core and dbt Cloud have relied on a Python-based execution engine. While powerful, Python parsing and compilation can become a bottleneck as projects grow to thousands of models. Recognizing this, dbt Labs has developed dbt Fusion from the ground up in Rust, a language known for its speed and memory safety.

Key Benefit: Lightning‑Fast Parsing

One of dbt Fusion’s marquee improvements is its parsing speed. Traditional dbt projects—especially those with tens of thousands of models—could take minutes to parse. With Fusion’s Rust implementation, parsing times drop dramatically, often by up to 30× faster, bringing multi‑minute delays down to mere seconds (or even milliseconds). Faster parsing means quicker iterations, faster CI checks, and more responsive development workflows.

Ahead‑of‑Time Cycle Compilation

Typically, dbt compilation happens right before execution, which means syntax errors or schema mismatches only surface during run time. dbt Fusion introduces ahead‑of‑time cycle compilation, enabling the engine to analyze your SQL and model dependencies intelligently before executing any queries against your warehouse. This pre‑flight check catches errors early, saving compute costs and developer time by preventing failed runs on the warehouse.

Column‑Level Lineage & Data Type Validation

Data governance is becoming ever more critical. With dbt Fusion, you gain column‑level lineage and built‑in data type validation. This fine‑grained visibility ensures that every downstream model inherits accurate metadata. For instance, if you tag a column as “PII” or “Personal Information” at the source model, Fusion will automatically propagate that tag to any downstream models referencing the same column—streamlining compliance and auditability.

Smarter Orchestration & Cost Savings

dbt Cloud users already benefit from intelligent job scheduling, but Fusion takes orchestration to the next level. It can detect unchanged models and skip them, dramatically reducing unnecessary computation. In practice, this means your daily or hourly runs only re‑execute models that truly need it, leading to significant savings on warehousing costs.

Enhanced Developer Experience in VS Code

To complement the core engine improvements, dbt Labs has released an updated VS Code extension tailored for Fusion. Highlights include:

  • Autocomplete for model names, macros, and config blocks
  • Inline SQL preview so you see your compiled SQL before executing
  • Live feedback on syntax or type errors as you code

These enhancements further shrink the feedback loop, allowing analytics engineers to develop with confidence and speed.

Supported Platforms & Future Connectors

At launch (beta stage), dbt Fusion supports:

  • Snowflake
  • Databricks

dbt Labs has confirmed that additional connectors—such as BigQuery and Redshift—are on the roadmap. To stay up to date, subscribe to the official dbt community forums or follow the dbt Twitter account for announcement alerts.

Beta to GA: What to Expect

dbt Fusion is currently in beta, but the pace of innovation is rapid. dbt Labs aims to reach general availability soon. During the beta, you can:

  1. Experiment with your most complex projects to quantify performance gains.
  2. Report issues and help refine features via GitHub or the dbt community channels.
  3. Understand limitations—such as unsupported adapters or edge‑case macros—before rolling out to production.

Migration Paths for dbt Cloud & Core Users

If you’re on dbt Cloud, you don’t need to lift a finger: Fusion will become the default execution engine automatically once GA is reached. Your existing jobs and orchestrations will seamlessly target Fusion under the hood.

For dbt Core users, upgrading is straightforward:

  1. Install the latest dbt-fusion package alongside dbt-core.
  2. Follow the step‑by‑step migration guide on the dbt Labs documentation site.
  3. Run your test suite locally to confirm compatibility.

License & Pricing Considerations

dbt Fusion introduces a new tiered licensing model:

  • Local Development (dbt Core users): Source‑available, free, and fully functional for local builds (with some advanced features behind a paywall).
  • dbt Cloud customers: Fusion is included in paid tiers, unlocking all premium capabilities—such as enterprise connectors, deeper metadata lineage, and priority support.

Review the official pricing page to see which features align with your team’s needs.

Is dbt Fusion Right for You?

If your team regularly works on large-scale dbt projects or you’re chasing every millisecond of performance, dbt Fusion is a game‑changer. Early adopters report 10×–30× faster parsing, near‑instant validation feedback, and lower cloud compute bills thanks to smarter orchestration.

That said, if your project is small or you’re comfortable with existing runtimes, you may choose to wait until GA and additional adapters ship. Either way, Fusion is the future of dbt, and understanding its capabilities now will help you plan your analytics roadmap.

Next Steps

  • Read the dbt Fusion docs to explore detailed benchmarks and feature matrices.
  • Join the beta: enable Fusion in your dev environment and share feedback.
  • Monitor connector announcements to align Fusion with your warehouse of choice.

Watch the Video

In dbt Talk, dbt webinar

dbt Fusion Erklärt: Der nächste Schritt in der Entwicklung des dbt

dbt Fusion Engine

As data teams continue to scale and the demand for faster, more reliable analytics grows, the tools we depend on must evolve. Enter dbt Fusion, the latest high‑performance execution engine from dbt Labs that promises to take your dbt workflows to unprecedented speeds. In this post, we’ll dive deep into what dbt Fusion is, explore its key features, discuss supported platforms and migration paths, and help you decide if—and when—you should upgrade. Let’s get started!

In this article:



Why a New Engine?

dbt (data build tool) has revolutionized how analytics engineers transform and test data directly within the data warehouse. Until now, both dbt Core and dbt Cloud have relied on a Python-based execution engine. While powerful, Python parsing and compilation can become a bottleneck as projects grow to thousands of models. Recognizing this, dbt Labs has developed dbt Fusion from the ground up in Rust, a language known for its speed and memory safety.

Key Benefit: Lightning‑Fast Parsing

One of dbt Fusion’s marquee improvements is its parsing speed. Traditional dbt projects—especially those with tens of thousands of models—could take minutes to parse. With Fusion’s Rust implementation, parsing times drop dramatically, often by up to 30× faster, bringing multi‑minute delays down to mere seconds (or even milliseconds). Faster parsing means quicker iterations, faster CI checks, and more responsive development workflows.

Ahead‑of‑Time Cycle Compilation

Typically, dbt compilation happens right before execution, which means syntax errors or schema mismatches only surface during run time. dbt Fusion introduces ahead‑of‑time cycle compilation, enabling the engine to analyze your SQL and model dependencies intelligently before executing any queries against your warehouse. This pre‑flight check catches errors early, saving compute costs and developer time by preventing failed runs on the warehouse.

Column‑Level Lineage & Data Type Validation

Data governance is becoming ever more critical. With dbt Fusion, you gain column‑level lineage and built‑in data type validation. This fine‑grained visibility ensures that every downstream model inherits accurate metadata. For instance, if you tag a column as “PII” or “Personal Information” at the source model, Fusion will automatically propagate that tag to any downstream models referencing the same column—streamlining compliance and auditability.

Smarter Orchestration & Cost Savings

dbt Cloud users already benefit from intelligent job scheduling, but Fusion takes orchestration to the next level. It can detect unchanged models and skip them, dramatically reducing unnecessary computation. In practice, this means your daily or hourly runs only re‑execute models that truly need it, leading to significant savings on warehousing costs.

Enhanced Developer Experience in VS Code

To complement the core engine improvements, dbt Labs has released an updated VS Code extension tailored for Fusion. Highlights include:

  • Autocomplete for model names, macros, and config blocks
  • Inline SQL preview so you see your compiled SQL before executing
  • Live feedback on syntax or type errors as you code

These enhancements further shrink the feedback loop, allowing analytics engineers to develop with confidence and speed.

Supported Platforms & Future Connectors

At launch (beta stage), dbt Fusion supports:

  • Snowflake
  • Databricks

dbt Labs has confirmed that additional connectors—such as BigQuery and Redshift—are on the roadmap. To stay up to date, subscribe to the official dbt community forums or follow the dbt Twitter account for announcement alerts.

Beta to GA: What to Expect

dbt Fusion is currently in beta, but the pace of innovation is rapid. dbt Labs aims to reach general availability soon. During the beta, you can:

  1. Experiment with your most complex projects to quantify performance gains.
  2. Report issues and help refine features via GitHub or the dbt community channels.
  3. Understand limitations—such as unsupported adapters or edge‑case macros—before rolling out to production.

Migration Paths for dbt Cloud & Core Users

If you’re on dbt Cloud, you don’t need to lift a finger: Fusion will become the default execution engine automatically once GA is reached. Your existing jobs and orchestrations will seamlessly target Fusion under the hood.

For dbt Core users, upgrading is straightforward:

  1. Install the latest dbt-fusion package alongside dbt-core.
  2. Follow the step‑by‑step migration guide on the dbt Labs documentation site.
  3. Run your test suite locally to confirm compatibility.

License & Pricing Considerations

dbt Fusion introduces a new tiered licensing model:

  • Local Development (dbt Core users): Source‑available, free, and fully functional for local builds (with some advanced features behind a paywall).
  • dbt Cloud customers: Fusion is included in paid tiers, unlocking all premium capabilities—such as enterprise connectors, deeper metadata lineage, and priority support.

Review the official pricing page to see which features align with your team’s needs.

Is dbt Fusion Right for You?

If your team regularly works on large-scale dbt projects or you’re chasing every millisecond of performance, dbt Fusion is a game‑changer. Early adopters report 10×–30× faster parsing, near‑instant validation feedback, and lower cloud compute bills thanks to smarter orchestration.

That said, if your project is small or you’re comfortable with existing runtimes, you may choose to wait until GA and additional adapters ship. Either way, Fusion is the future of dbt, and understanding its capabilities now will help you plan your analytics roadmap.

Next Steps

  • Read the dbt Fusion docs to explore detailed benchmarks and feature matrices.
  • Join the beta: enable Fusion in your dev environment and share feedback.
  • Monitor connector announcements to align Fusion with your warehouse of choice.

Watch the Video

In Data Tools, dbt webinar, Intermediate

Scale Up your Data Vault Project – with dbt Mesh

dbt Mesh - data mesh solution

dbt Mesh

Learn how dbt Mesh enhances Data Vault projects within dbt Cloud by facilitating a more efficient data mesh architecture. The larger a data warehouse project grows, the more people begin to rely and work with the data provided. This work could be consuming the data, applying business rules, modeling facts and dimensions, or other typical tasks in a data environment. In a large organization, all these users might be scattered across different divisions, and the data they are working with might belong to different business domains. At some point, the entire organization faces the challenge of data sharing and governance guidelines, which might prohibit users of the sales department from accessing data from the finance department. A data mesh offers a solution that helps organizations to deal with these challenges. If you want to learn more about the data mesh, check our recent blog article about Data Vault and data mesh here!

We also have a webinar on exactly this specific subject. Don’t miss it and watch the recording for free!

Data Mesh Support bei dbt Cloud

Many organizations struggle with introducing a Data Mesh approach into the Data Vault landscape. In this webinar, we will dive into dbt Mesh, and how to leverage it in a Data Vault project.

Watch Webinar Recording

In this article:

What is dbt Mesh?

Dbt Mesh is a recently added feature that makes dbt Cloud work more efficiently with a data mesh approach. The already familiar {{ ref() }} function is no longer limited to models within one dbt project, instead it can refer to models of other dbt projects.

Why would I want to refer to other dbt projects?

Imagine a big organization that uses dbt Cloud for their Data Vault implementation. The project might have 400 sources defined, 2000 models implemented, and is used actively by 30 developers. Out of these 30 developers, there might be 5 people specifically working on the Business Data Vault and Information Mart layer for finance-related objects. Another 5 developers are working on the same layers but for sales-related objects.

At some point, you might want to avoid finance people messing around with the sales-related dbt models, so a data mesh architecture is to be implemented. This would allow the organization to define policies regarding data sharing, data ownership, and other governance measures.

With dbt Mesh, both the Sales and the Finance team would get their own dbt project. Since both should be based on the same Raw Data Vault, an additional foundational dbt project is created exclusively for staging and Raw Data Vault objects. Both domain-specific dbt projects, sales and finance, can now refer to Raw Vault objects inside the foundational dbt project, avoiding actually physically replicating the data.

dbt Mesh - data mesh solution

How can I leverage dbt Mesh in a Data Vault powered Data Mesh?

Define Data Contracts

Dbt models, or groups of models, can now be configured to have data contracts. Inside the already familiar .yml files, models can now be set to be publicly available (within an organization), data owners can be enforced, and table schemas can be locked.

Create a Foundational dbt project

In a Data Mesh architecture, the most common way to implement Data Vault 2.0, is to have a commonly shared Raw Vault as a foundation, and both Business Vault and Information Marts are divided by business domains. In dbt Mesh, this would reflect in a foundational dbt project, that includes all staging and Raw Data Vault objects. Only the Raw Data Vault objects would be configured to be accessible by other dbt projects, since the staging models should not be used outside of Raw Data Vault models.

Add domain-level dbt projects

Based on the foundational Raw Vault dbt project, each domain team can now work in their own dbt project. They access the Raw Data Vault via the (extended) {{ ref() }} function and don’t have to worry about maintaining these Raw Vault objects. Additionally, they can define which of their artifacts might be useful for other domains, these can be shared via their own data contracts.

Distribute Responsibilities

Typically, a power user does not create Hubs, Links, and Satellites. And it’s not their responsibility to ensure a reliable Raw Data Vault to build transformations on. Therefore, it is important to define responsibilities within each dbt project. Especially objects that are shared outside of one project should always have data contracts and defined owners. This ensures that users of these shared objects can rely on it.

Conclusion

All in all, dbt Mesh offers a fantastic way to properly implement a true data mesh approach. It is especially relevant, when different business domains of one organization are working together in dbt to create trustable deliverables. In most scenarios, it makes sense to already start using dbt Mesh, although your project might not be too big yet. Having clear responsibilities and data contracts always helps maintain trust and transparency for your data!

– Tim Kirschke (Scalefree)

In Data Tools, dbt webinar, Intermediate

Scale Up your Data Vault Project – with dbt Mesh

dbt Mesh - data mesh solution

dbt Mesh

Learn how dbt Mesh enhances Data Vault projects within dbt Cloud by facilitating a more efficient data mesh architecture. The larger a data warehouse project grows, the more people begin to rely and work with the data provided. This work could be consuming the data, applying business rules, modeling facts and dimensions, or other typical tasks in a data environment. In a large organization, all these users might be scattered across different divisions, and the data they are working with might belong to different business domains. At some point, the entire organization faces the challenge of data sharing and governance guidelines, which might prohibit users of the sales department from accessing data from the finance department. A data mesh offers a solution that helps organizations to deal with these challenges. If you want to learn more about the data mesh, check our recent blog article about Data Vault and data mesh here!

We also have a webinar on exactly this specific subject. Don’t miss it and watch the recording for free!

Data Mesh Support bei dbt Cloud

Many organizations struggle with introducing a Data Mesh approach into the Data Vault landscape. In this webinar, we will dive into dbt Mesh, and how to leverage it in a Data Vault project.

Watch Webinar Recording

In this article:

What is dbt Mesh?

Dbt Mesh is a recently added feature that makes dbt Cloud work more efficiently with a data mesh approach. The already familiar {{ ref() }} function is no longer limited to models within one dbt project, instead it can refer to models of other dbt projects.

Why would I want to refer to other dbt projects?

Imagine a big organization that uses dbt Cloud for their Data Vault implementation. The project might have 400 sources defined, 2000 models implemented, and is used actively by 30 developers. Out of these 30 developers, there might be 5 people specifically working on the Business Data Vault and Information Mart layer for finance-related objects. Another 5 developers are working on the same layers but for sales-related objects.

At some point, you might want to avoid finance people messing around with the sales-related dbt models, so a data mesh architecture is to be implemented. This would allow the organization to define policies regarding data sharing, data ownership, and other governance measures.

With dbt Mesh, both the Sales and the Finance team would get their own dbt project. Since both should be based on the same Raw Data Vault, an additional foundational dbt project is created exclusively for staging and Raw Data Vault objects. Both domain-specific dbt projects, sales and finance, can now refer to Raw Vault objects inside the foundational dbt project, avoiding actually physically replicating the data.

dbt Mesh - data mesh solution

How can I leverage dbt Mesh in a Data Vault powered Data Mesh?

Define Data Contracts

Dbt models, or groups of models, can now be configured to have data contracts. Inside the already familiar .yml files, models can now be set to be publicly available (within an organization), data owners can be enforced, and table schemas can be locked.

Create a Foundational dbt project

In a Data Mesh architecture, the most common way to implement Data Vault 2.0, is to have a commonly shared Raw Vault as a foundation, and both Business Vault and Information Marts are divided by business domains. In dbt Mesh, this would reflect in a foundational dbt project, that includes all staging and Raw Data Vault objects. Only the Raw Data Vault objects would be configured to be accessible by other dbt projects, since the staging models should not be used outside of Raw Data Vault models.

Add domain-level dbt projects

Based on the foundational Raw Vault dbt project, each domain team can now work in their own dbt project. They access the Raw Data Vault via the (extended) {{ ref() }} function and don’t have to worry about maintaining these Raw Vault objects. Additionally, they can define which of their artifacts might be useful for other domains, these can be shared via their own data contracts.

Distribute Responsibilities

Typically, a power user does not create Hubs, Links, and Satellites. And it’s not their responsibility to ensure a reliable Raw Data Vault to build transformations on. Therefore, it is important to define responsibilities within each dbt project. Especially objects that are shared outside of one project should always have data contracts and defined owners. This ensures that users of these shared objects can rely on it.

Conclusion

All in all, dbt Mesh offers a fantastic way to properly implement a true data mesh approach. It is especially relevant, when different business domains of one organization are working together in dbt to create trustable deliverables. In most scenarios, it makes sense to already start using dbt Mesh, although your project might not be too big yet. Having clear responsibilities and data contracts always helps maintain trust and transparency for your data!

– Tim Kirschke (Scalefree)

In dbt webinar, Intermediate

Enterprise Data Transformations with Turbovault and dbt Cloud

ENTERPRISE DATA TRANSFORMATIONS WITH TURBOVAULT AND DBT CLOUD FEBRUARY 27TH dbt Labs SCALEFREE
Watch Webinar Recording

Webinar Summary

Data Vault is vital for businesses due to its adaptability and scalability in managing dynamic data environments. Its hub-and-spoke architecture ensures traceability and agility, enabling quick adaptation to changing requirements and diverse data sources.

Come and join our upcoming webinar and learn about how to use dbt Cloud with Turbovault and a data modeling tool to implement data vault in your organization.

In this webinar you will

  • Receive a detailed 90-minute “show-and-tell” walkthrough of an end-to-end Data Vault implementation using cutting-edge tools
  • Explore the seamless integration of Ellie.ai, Turbovault4dbt, and Datavault4dbt for enhanced data modeling and automation
  • Understand the practical aspects of implementing a Data Vault without the need for a pre-configured demo environment.

Webinar Details

  • Date: 27th February
  • Time: 14:00 – 15:45 PM CET

Webinar Agenda

  1. Introduction to the Power Trio: dbt Cloud, Turbovault, and Data Modeling Tools
  2. 90-Minute “Show-and-Tell” Walkthrough of an End-to-End Data Vault Implementation
    • Using Ellie.ai for ER Model, Turbovault4dbt for dbt Automation, and Datavault4dbt for DV Model Generation
  3. Insights into Data Vault Implementation in Medium and Large Sized Companies
  4. Q&A Session with Industry Experts

In Partnership With

dbt labs
DataVault4dbt Powered by Scalefree

Speakers

Profile picture of Tim Kirschke

Tim Kirschke
Senior Consultant

Tim has a Bachelor’s degree in Applied Mathematics and has been working as a BI consultant for Scalefree since the beginning of 2021. He’s an expert in the design and implementation of BI solutions, with focus on the Data Vault 2.0 methodology. His main areas of expertise are dbt, Coalesce, and BigQuery.

Sean McIntyre

Sean McIntyre
Senior Solutions Architect

Sean is a Senior Solutions Architect at dbt Labs who works with organizations across Europe, helping them get started with and scaling dbt Cloud with Data Vault. In his last role, he was a Principal Data Engineer who implemented the modern data stack. Originally from Canada, his homebase is now Vienna.

In dbt webinar, Intermediate

Enterprise Data Transformations with Turbovault and dbt Cloud

ENTERPRISE DATA TRANSFORMATIONS WITH TURBOVAULT AND DBT CLOUD FEBRUARY 27TH dbt Labs SCALEFREE
Watch Webinar Recording

Webinar Summary

Data Vault is vital for businesses due to its adaptability and scalability in managing dynamic data environments. Its hub-and-spoke architecture ensures traceability and agility, enabling quick adaptation to changing requirements and diverse data sources.

Come and join our upcoming webinar and learn about how to use dbt Cloud with Turbovault and a data modeling tool to implement data vault in your organization.

In this webinar you will

  • Receive a detailed 90-minute “show-and-tell” walkthrough of an end-to-end Data Vault implementation using cutting-edge tools
  • Explore the seamless integration of Ellie.ai, Turbovault4dbt, and Datavault4dbt for enhanced data modeling and automation
  • Understand the practical aspects of implementing a Data Vault without the need for a pre-configured demo environment.

Webinar Details

  • Date: 27th February
  • Time: 14:00 – 15:45 PM CET

Webinar Agenda

  1. Introduction to the Power Trio: dbt Cloud, Turbovault, and Data Modeling Tools
  2. 90-Minute “Show-and-Tell” Walkthrough of an End-to-End Data Vault Implementation
    • Using Ellie.ai for ER Model, Turbovault4dbt for dbt Automation, and Datavault4dbt for DV Model Generation
  3. Insights into Data Vault Implementation in Medium and Large Sized Companies
  4. Q&A Session with Industry Experts

In Partnership With

dbt labs
DataVault4dbt Powered by Scalefree

Speakers

Profile picture of Tim Kirschke

Tim Kirschke
Senior Consultant

Tim has a Bachelor’s degree in Applied Mathematics and has been working as a BI consultant for Scalefree since the beginning of 2021. He’s an expert in the design and implementation of BI solutions, with focus on the Data Vault 2.0 methodology. His main areas of expertise are dbt, Coalesce, and BigQuery.

Sean McIntyre

Sean McIntyre
Senior Solutions Architect

Sean is a Senior Solutions Architect at dbt Labs who works with organizations across Europe, helping them get started with and scaling dbt Cloud with Data Vault. In his last role, he was a Principal Data Engineer who implemented the modern data stack. Originally from Canada, his homebase is now Vienna.

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