Simplify Complexity
Deliver Insights Faster
The SQL ontology-based semantic layer integrates data with meaning,
relationships, and metrics.
Build smarter, enterprise-ready AI
with SQL Ontologies
Build smarter, enterprise-ready AI with SQL Ontologies
Enable LLMs – Large Language Models and autonomous agents to interact seamlessly with structured, governed, and contextualized data, by leveraging semantic modeling, relationships, SQL measures, and virtualization.
- Unify structured data into a semantic graph for precise context.
- Empower LLMs with accurate, governed, real-time answers.
- Accelerate development with SDKs for seamless integration into apps, copilots, and workflows.
Boost Productivity of Data Teams
Simplify your Journey
from Data to Knowledge
- Model data using business terms .
- Define reusable semantic metrics.
- Replace complex JOINs with semantic relationships.
- Benefit from hierarchies and classifications.
- Apply business rules using logic and math operators.
Semantic Representation
of Data
Semantic Relationships
Hierarchies and Classifications
Business Logic
Semantic Reasoning (Inheritance)
Integrate Data
- Automatically map data to the semantic model.
- Join multiple data sources to query data at scale.
- Consume data as a connected semantic graph.
- Benefit from advanced query optimizations.
- Boost performance and save compute costs with Timbr's intelligent cache engine.
Connect Any Data Source
- Connect to your data where it lives.
- Work seamlessly with your data.
- Timbr doesn't store data.
- When running a query, Timbr optimizes the query and pushes it down to the backend.
Leverage Universal Consumption of Data
Power Web Apps with Smart REST
- Draw from multiple data sources.
- Fetch only the fields needed.
- Fetch nested data structures.
- Self-document with semantic Swagger (open API).
- Draw complex relationships in data.
- Reduce network payload.
- Role-based and row-level access control.
Accelerate Analytics and Insights
Benefit From Short SQL Queries
With JOINs
22 lines of SQL:
With Relationships:
6 lines of SQL
Visually Explore Data as a Graph
- Traverse your relational data across tables and sources.
- Generate explorations with SQL or NL queries.
- Understand data connections and gain insights.
Decouple BI from Data
- Reusable common metrics across tools.
- Replacing static views with dynamic concepts and data mappings.
- Update the data structure without disrupting workflows.
- Push down compute from BI to CDW.
- Avoid lock-in of data stores and consumption tools.
- Accelerate cloud migration.
- Build easier to maintain systems.
Easy and Fast
Implementation
- Fully virtual, no ETLs required
- Low code UI or familiar SQL
-
JDBC/ODBC compliant, accessible
to any BI and Data Science tool
Choice of
Deployment Options
- SaaS, cloud or on-premises
- Available on Azure, GCP and AWS
- Containerized in Kubernetes
Tackle your data challenges
smarter and faster
1 GARTNER Cool Vendors in Data Management, June 01, 2021. ID: G00746797. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. The GARTNER COOL VENDOR badge and GARTNER are registered trademarks and services marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
