Last updated 11-07-2025
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SDF
SDF is a powerful SQL compiler and build system that enhances data workflows by providing deep static analysis of SQL code across large data warehouses. Since joining forces with dbt Labs, SDF's advanced SQL comprehension technology is being integrated directly into dbt, improving developer efficiency and enabling faster, more reliable data transformations. This collaboration brings true column-level lineage and richer metadata capabilities to dbt users, helping teams manage data complexity at scale.
SDF’s core strength lies in its ability to perform compile-time code checks that detect vulnerabilities and privacy risks, supporting organizations focused on data governance and quality. Its cloud-native environment offers features like a data catalog, semantic search, interactive data maps, and detailed reports, giving users a comprehensive view of their SQL ecosystem.
The integration with dbt means SDF’s technology now powers a next-generation dbt engine that is faster and more cost-efficient, unlocking new metadata use cases and improving the analytics development lifecycle. This makes SDF especially valuable for analytics engineers and data teams looking to ship trusted data faster while maintaining agility and quality.
SDF supports custom compile-time checks and automated label propagation, reducing manual annotation to less than 1%. It analyzes SQL both in source form and query logs, making it flexible for various development workflows. The partnership with dbt Labs also means ongoing enhancements and tighter integration with popular data tooling.
Overall, SDF is a key tool for teams aiming to elevate their SQL development with advanced static analysis, improved data privacy, and enhanced data governance, now amplified through its collaboration with dbt Labs.
🔍 Deep SQL code analysis detects errors early
⚙️ Custom compile-time checks protect sensitive data
📊 Interactive data maps visualize dependencies clearly
🚀 Integration with dbt boosts speed and efficiency
🗂️ Automated metadata and column-level lineage tracking
Integrates deeply with dbt to enhance developer workflows
Provides precise column-level lineage for better data tracking
Reduces manual effort with automated label propagation
Supports custom checks to enforce data privacy and governance
Cloud-native platform with rich data catalog and search
Pricing details are not publicly disclosed and require contact
Primarily targeted at teams using dbt or large SQL warehouses
How does SDF improve the dbt developer experience?
SDF integrates its SQL comprehension technology into dbt, making data transformations faster, more cost-efficient, and enabling true column-level lineage.
What levels of SQL comprehension does SDF provide?
SDF offers three levels of SQL comprehension that enhance precision in understanding and analyzing SQL queries for better data insights.
Can SDF detect privacy risks in SQL code?
Yes, SDF uses compile-time code checks to identify vulnerabilities and privacy issues, helping teams maintain data governance.
Does SDF support custom checks for SQL code?
SDF allows development of custom compile-time checks to protect sensitive information tailored to your organization's needs.
What new metadata capabilities does SDF bring to dbt users?
SDF unlocks true column-level lineage and richer metadata use cases, improving data traceability and management within dbt.
Is SDF suitable for large-scale data warehouses?
Yes, SDF is designed to analyze SQL across large data warehouses, supporting complex data environments efficiently.
How does SDF reduce manual annotation effort?
With rich classifiers and automated label propagation, SDF requires less than 1% manual annotation for comprehensive coverage.