The modern data stack is in a constant state of evolution. While the specific technologies within it may shift over time, the core principle remains the same: a great data stack thrives when each layer is composed of tools that excel at their specialized role. Some tools move your data. Others model and shape it. When it comes to combining an execution engine with a transformation framework, Starburst and dbt are a natural match.
These technologies are not just different but complementary, ensuring that the resulting data pipelines are not only more powerful but also more adaptable and resilient.
This article explores why Starburst and dbt belong together. Using Starburst as an engine and dbt as the framework creates a powerful foundation for modern analytics engineering. Together, they help data teams unify access, governance, and scale. All of this is achieved using SQL, a language you already know.
Whether you’re already using dbt, evaluating Starburst, or looking to modernize your transformation layer, considering Starburst and dbt together can bring structure and speed to your stack.
Let’s dig in.
Starburst is the Engine. dbt is the Framework.
Starburst and dbt fit together better than most data technologies. This is the result of both specialization and connectivity. Starburst excels at processing workloads. Meanwhile, dbt excels at orchestration. Both come together to work as a team.
Let’s explore how the two technologies work together to drive optimal results.
Starburst’s role in the stack
Starburst is a query engine built to make data more accessible, no matter where it lives. Whether your data is stored in cloud object storage, data lakes, data warehouses, or data lakehouses, Starburst connects to it directly and lets you query it using SQL. Most importantly of all, you can centralize as much or as little of your data as you like. Our data federation capabilities ensure that the choice remains yours.
By unifying access across your organization, Starburst becomes a foundation for collaboration. It allows analysts, engineers, and data scientists to work from the same dataset, accelerating insights and simplifying data workflows across teams and tools.
At the same time, Starburst does not compromise on governance. With built-in support for role-based access control (RBAC), attribute-based access control (ABAC), and data products, it gives data teams confidence that users can access only the right data in the right way. Starburst brings together speed, flexibility, and control, making it the engine that powers open, secure, and scalable data operations.
dbt and the stack
Compared to Starburst, dbt does something very different, but complementary. It acts as the transformation layer that gives structure and clarity to your SQL-based workflows. This approach helps teams manage complex data transformations by treating them as code, applying software development best practices such as modularity, documentation, testing, and version control.
With dbt, every SQL transformation becomes a reusable, auditable, and easy-to-understand model. This makes it easier for teams to collaborate, review changes, and ensure consistency across the analytics pipeline.
When used alongside Starburst, dbt becomes a powerful way to manage and deploy transformations across distributed data sources. It lets you build reliable pipelines on top of Starburst’s federation capabilities while maintaining full transparency and control over how your data is shaped and used.
The result is a streamlined analytics workflow that scales with your team.
Why Starburst + dbt is a powerful combination
Starburst and dbt are built to solve different problems, but they work together seamlessly. Starburst provides universal data access and processing, while dbt acts as the data orchestration framework, structuring and managing your transformation logic.
Each serves a distinct purpose, but together they offer a unified experience for building and running modern data pipelines.
Unpacking the Starburst + dbt data stack
What does it look like in practice? The following image shows how Starburst and dbt work together in a data stack.
Using this approach, Starburst provides the execution layer that reads from multiple data sources, including cloud object stores, on-premises data, and streaming platforms. Meanwhile, dbt operates as the transformation framework layered above, orchestrating the creation of bronze, silver, and gold tables using SQL logic. This separation of compute and logic enables efficient, governed transformations within the lakehouse while supporting real-time analytics, BI, and AI workloads downstream. The result is a flexible and open data stack that works across clouds and scales with your needs.
Overall, the success of this stack is driven by a separation of concerns. The image below shows how the dbt-Starburst data architecture works in practice.
8 reasons why Starburst + dbt are the ideal datastack
Starburst and dbt belong together. In fact, in most production environments, we find that Starburst Galaxy users also deploy dbt. By using Starburst and dbt together, you achieve a number of benefits. Let’s look at each of those benefits individually.
1) Access to all your data, wherever it lives
Starburst is built to provide universal access to your data, wherever it lives. Our approach eliminates data silos and returns true choice to your data stack. Because insights are only as good as the data that fuels them, Starburst helps drive success in both analytics and AI.
Meanwhile, you can use as much or as little data federation or data centralization as you like. The choice is always yours.
2) Single SQL dialect across sources
Starburst and dbt speak the same language, SQL. This commonality makes them a natural fit to work together. In production environments, this typically takes the following form.
Starburst standardizes SQL across all data sources, whether it’s cloud storage, data lakes, or data lakehouses. dbt builds on that by letting teams define and manage their transformation logic using that same SQL dialect.
Together, they provide data teams with a consistent, unified approach to developing, orchestrating, and deploying analytics logic across the full range of enterprise data, regardless of its location.
3) Make your SQL more modular and powerful
Using Starburst and dbt together allows teams to write modular SQL in dbt and execute it across multiple data sources through Starburst without the need for complex ETL. Starburst ensures high-performance access, while dbt enforces governance, testing, and version control in the transformation layer. The result is a scalable, SQL-native workflow that brings speed, structure, and trust to every step of your analytics stack.
4) Handle data ingestion easily
Starburst simplifies data ingestion across both streaming and batch use cases. Whether you are ingesting real-time data from Kafka or importing files from S3, Starburst provides a no-code experience that reduces pipeline complexity. With automatic hydration into Iceberg tables, your data becomes immediately queryable for analytics, AI, and more.
Once ingested, dbt can be used to build, document, and orchestrate transformation models directly on top of this fresh data, enabling a clean and scalable workflow from raw input to refined output.
5) Use the best tool for the job when building the best data stack
Starburst and dbt excel in their own domains, but they come together in your data stack. Specifically, Starburst delivers powerful, scalable compute across all your data sources.
Meanwhile, dbt manages transformation logic through modular, versioned SQL. Together, they separate concerns cleanly, allowing each tool to focus on what it does best without sacrificing flexibility or performance.
6) Superior data governance and data observability
Starburst and dbt bring together robust governance and deep visibility across your data stack. Once again, they work together to achieve this.
Starburst provides fine-grained access controls, role-based policies, and built-in security, making it easier to manage sensitive data across distributed sources. Meanwhile, dbt complements this with documentation, testing, and lineage tracking, giving teams a clear view of where data comes from and how it’s transformed.
Together, they ensure that your analytics workflows are not only scalable but also transparent and compliant without sacrificing developer velocity. With Starburst and dbt, you get confidence in the data you use and trust in the insights you deliver.
7) Write SQL to any source, even no-SQL data
Starburst gives you the ability to query virtually any data source using standard SQL, including systems that are traditionally difficult to work with, such as NoSQL databases. Whether you’re connecting to MongoDB, Cassandra, or key-value stores, Starburst acts as a translation layer that makes these sources accessible.
This means dbt can continue to define transformations and models as SQL without having to switch languages or tools. Together, Starburst and dbt bring consistency to your stack and unlock data that was previously too complex or incompatible to integrate easily.
8) Why Starburst + dbt is better than Databricks
Is Starburst and dbt better than the alternatives? Yes!
Many organizations rely on Databricks for their data lakehouse architecture, using dbt Cloud or Fivetran as a unified platform. While this data stack delivers great UI and built-in ML tools, the bundled infrastructure can be expensive, especially at scale.
In contrast, Starburst with dbt offers a leaner, SQL-native stack. You pay only for the compute and storage you use, avoid lock-in, and benefit from broader source support. This makes your analytics engineering both more agile and less costly.
Starburst and dbt: A perfect match
Starburst and dbt are a natural fit because they bring complementary strengths to the modern data stack. dbt turns raw SQL into clean, modular, and testable logic. Starburst gives that logic power by running it efficiently across any connected data source using a single SQL interface.
Better together
Together, they form the core of a modern data architecture that is open, flexible, and built for scale. With support for Apache Iceberg, the Icehouse architecture, and advanced ingestion features, Starburst makes it easy to query and manage your data wherever it lives. dbt adds transformation discipline and automation, giving teams a consistent way to build and deploy analytics workflows.
If you are working to unify your data, accelerate your analytics, and support AI-ready pipelines, the Starburst and dbt pairing is one of the most effective ways to get there.
