Introduction
dbt Labs has recently acquired SDF, a company known for its cutting-edge SQL comprehension technology. This acquisition brings several key benefits, including:
- Faster dbt project compilation
- Improved developer experience
- High-fidelity data lineage tracking
But what does this mean for dbt users? Let’s explore.
In this article:
Background & Context
dbt simplifies the creation of data models using SQL SELECT statements, making data engineering accessible to anyone familiar with SQL. Historically, however, dbt has treated SQL as mere strings, limiting its ability to understand the deeper meaning behind queries.
The Acquisition Explained
In January 2025, dbt Labs announced the acquisition of SDF. According to dbt Labs’ founder and CEO Tristan Handy:
“We are acquiring SDF to bring SQL comprehension into dbt and usher in a new era of ‘what’s possible’ for analytics: supercharging developer productivity and heightening data quality, all while optimizing data platform costs.”
What is SDF Technology?
Unlike dbt’s traditional approach of treating SQL as text, SDF understands the deeper structure of SQL, recognizing objects, types, syntax, and semantics. It emulates the SQL compilers of various data platforms (e.g., Snowflake, Redshift, BigQuery), allowing developers to:
- Validate SQL queries before execution
- Catch breaking changes early
- Ensure platform compatibility
- Perform real-time impact analysis
- Reduce computational costs
What Does “SQL Comprehension” Mean?
SQL comprehension enables dbt to:
- Identify query components
- Generate structured artifacts
- Validate SQL syntax and semantics
- Predict query outcomes, including column creation and datatype assignments
- Execute queries efficiently
Impact on dbt
1. Validate: Will My SQL Really Work?
With SDF, dbt can ensure that SQL queries are correct before execution, reducing errors and debugging time.
2. Analyze: Precise Column-Level Lineage
SQL comprehension improves data lineage tracking, leading to:
- Better debugging workflows
- Optimized CI builds (only rebuilding models affected by changes)
- Enhanced metadata propagation (e.g., PII tagging, test descriptions)
3. Optimize: Right Query, Right Place
With a better understanding of SQL, dbt can:
- Optimize queries for specific engines
- Identify performance bottlenecks before execution
- Support query pruning (scanning only relevant data subsets)
- Optimize DAG execution across multiple platforms
Future Implications
The integration of SDF into dbt is in progress. While it won’t be part of the Apache 2.0 code base, dbt Labs plans to make key SDF capabilities available to all users—both in dbt Core and dbt Cloud.
With SDF, dbt users can look forward to:
- Faster performance
- Optimized data platform costs
- Improved data lineage tracking
All without needing to change existing dbt projects.
Conclusion
The acquisition of SDF marks a significant milestone for dbt, bringing true SQL comprehension into the platform. Developers will benefit from enhanced query validation, real-time impact analysis, and optimized execution strategies. As dbt evolves, its users can expect more powerful, efficient, and cost-effective data transformations.
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Meet the Speaker
Hernan Revale
Senior BI Consultant
Hernan Revale is working in Business Intelligence supporting Scalefree International since 2022 as a BI Consultant. Prior to Scalefree, he had over three years of experience as an independent consultant in the areas of business intelligence, strategic planning, and analytics; and was the General Manager of the Research and Technology Transfer area of a National University in Argentina. Hernan has an MSc with Distinction in Business Analytics from Imperial College London and is a Certified Data Vault 2.0 Practitioner. He is also a university professor and researcher, with multiple presentations in conferences and indexed journals.
