data platforms
Bridging Data & Meaningful Insights in Snowflake
Timbr enhances Snowflake with a semantic layer that models data as a connected knowledge graph in SQL, streamlining discovery, enabling reuse, and accelerating AI-ready products, all while preserving Snowflake’s native performance, security, and tool integrations.
Model Data Relationships
Simplify Snowflake joins with declarative relationships embedded in your semantic layer.
Define Once, Use Everywhere
Create reusable logic and metrics across Snowflake workspaces, dashboards, and teams.
AI-Ready Structure and Access
Structure Snowflake views semantically for high-accuracy NL-to-SQL and RAG applications.
Clear Lineage Traceability
Track how Snowflake data is used and transformed at the semantic concept level.
How Timbr Embeds into Snowflake
Timbr integrates into Snowflake’s modeling layer, enabling:
- Unified Semantic Graph: Represent Snowflake data as a connected graph for simplified querying.
- Seamless BI Integration: Expose semantic models and metrics to Tableau, Power BI, and Looker.
- Simplified SQL: Replace complex joins with reusable semantic relationships.
Features in Action
- Semantic Modeling: Define and manage data relationships across Snowflake’s ecosystem.
- Metric Store Integration: Standardize metrics with semantic context for universal access via SQL.
- Cross-Platform Consistency: Ensure reliable insights across teams and platforms.
Impact
Timbr transforms Snowflake into a cohesive, semantic data platform, enabling:
- Faster Insights: Simplify querying and accelerate decision-making.
- Enhanced Collaboration: Share semantic models for aligned analytics.
- Scalable Workflows: Build flexible and meaningful data systems.
