This blog post is Human-Centered Content: Written by humans for humans.
Recently, my colleagues and I attended Data Council in Oakland, California, where discussions about data infrastructure, AI integration and seminal data engineering patterns dominated the technical talks. Among the most impactful sessions was Elias DeFaria’s talk on dbt labs’ acquisition and integration of SDF Labs (yes, there will be a lot of acronyms in this blog) and its transformative implications for the data engineering landscape.
As a daily user of dbt’s core product and strong advocate on their approach to data engineering, I wanted to share key insights from my discussion with the dbt team and explain what these changes mean for teams with active investments in the product, or those who may be considering a shift to a new data transformation framework.
This blog will contextualize these new capabilities in the three main overlays that resonated throughout this talk: cost control and innovation with AI-assisted development.
Another TLA in the ETL Space: WTF is SDF?
In his talk, SDF founder Elias DeFaria provided the rationale of dbt’s decision to acquire SDF Labs. Simply put, there was a need to address what Elias described as “the 20% innovation gap” that dbt couldn’t support with their monumental growth across mid-market and enterprises. This is not an uncommon strategy for maturing organizations. However, the implications of this marrying of a transformative framework with a universal engine has the potential to benefit any organization, regardless of their data platform.
Data Council exhibited how organizations are increasingly concerned with optimizing their existing data infrastructure costs while simultaneously preparing for further integrations with LLMs and AI-assistance for their developers, rather than replacements. SDF’s novel approach to understanding the idiosyncrasies and semantic sprawl of various SQL dialects addresses both concerns head-on. While dbt historically treated SQL code as string-types, SDF can introspect data objects, data types, platform-specific syntax and metadata semantics, creating a near-universal SQL compiler that emulates the full utility of platform-specific compilers.
During his talk, Elias emphasized how SDF has put analytics engineering on “hyperdrive” through two key technological advantages that align perfectly with the core themes of Data Council:
- Local impersonation of cloud execution: SDF’s tech allows developers to validate SQL code locally without sending queries to the warehouse — a direct response to the cost control overlay that dominated many Data Council sessions. This is a major win for any power users of dbt who understand how easy it is to generate phantom queries when running tests in dbt.
- Language-server protocol: This enables powerful IDE integrations, specifically with VScode, that dramatically improve developer efficiency — another key theme that resonated across the conference. dbt even announced their own VScode integration with the speediness of SDF built in, which is still in a private beta.
Putting the Analytics Engineering Workflow on Hyperdrive
A recurring theme at Data Council this year was the growing need to maximize developer productivity with the power of LLMs, keeping a human in the loop. Elias’s talk directly addressed how the new dbt engine transforms the developer experience with several key enhancements:
- Linting: Real-time feedback on SQL syntax and best practices that are in-context for your platform.
- Parsing: Understanding SQL structure without execution and compilation of your dbt project.
- Autocomplete: Intelligent suggestions based on schema awareness, tailored and trained upon the idiosyncrasies of your dbt codebase.
- Compute retargeting: This allows data models to be executed across different compute platforms with minimal changes, increasing flexibility and supporting heterogeneous data environments.
- Polyfills: SDF technology enables compatibility layers that smooth over differences between SQL dialects, reducing the complexity of managing multi-platform data environments.
What makes these capabilities particularly valuable is their integration directly into the dbt developer experience, and building with full parity across dbt’s cloud IDE and VSCode. Rather than relying on third party tools or manual processes, dbt users can now enjoy the quality of life experience anywhere.
The Next Generation: Building the “Brain” of the Data Team
The third major theme running throughout Data Council was AI integration, and Elias made several compelling connections between the SDF acquisition and the future of LLM-assisted workflows for data practitioners.
The most pertinent aspect is how SDF’s deep understanding of SQL semantics provides a foundation for LLM-assisted development. By knowing both the structure and the meaning of the SQL being written, the platform can generate better suggestions, identify code optimization opportunities and even automatically transform code between dialects.
Elias gave a glimpse into the future roadmap, suggesting that dbt’s enhanced engine will eventually enable:
- AI-generated transformations: Automatically creating optimized SQL based on natural language descriptions.
- Intelligent testing: Suggesting tests based on data patterns and usage.
- Automated documentation: Using AI to generate comprehensive documentation of complex data transformations.
These capabilities align perfectly with the broader industry focus on AI augmentation that was evident throughout Data Council 2025. While other vendors were showcasing point solutions, the dbt approach offers the potential for AI-powered features integrated directly into the core workflow that data teams already use.
What This Means for dbt Users Today
For organizations using dbt, the integration of SDF will appear to deliver immediate benefits across the three key overlays highlighted at Data Council:
- Cost control: Significantly reduced warehouse compute costs through local validation rather than execution — a direct response to CFO-level concerns about escalating data infrastructure expenses.
- Developer efficiency: Compilation speeds that are an order of magnitude faster than before, combined with LLM-enabled code assistance, will dramatically reduce development time and improve the data team’s output.
- AI readiness: Enhanced metadata and semantic understanding of your team’s work in dbt will continue to pave the foundation for future AI capabilities and agent understanding of the work data professionals are dong. Essentially, we are building the brain that holds the context and technical know-how that will benefit our successors and collaborators in the future.
As we move further into the ever-changing data landscape, organizations that effectively leverage these new capabilities and embrace mature transformation frameworks like dbt will likely see significant competitive advantages through faster, more cost-effective dataOps, creating exciting possibilities for the data team through time and cost savings.
If you’re wanting to learn more about dbt, or you’re curious about how these new changes can impact you, please reach out to us and we can get you in touch with one of our dbt subject mater experts.
