timbr core
Data Modeling with SQL Ontologies
Timbr enables agile data modeling using SQL ontologies that align with real-world meaning.
Users can build models bottom-up from existing schemas, top-down using SQL DDL, or automatically via AI-assisted generation, catalog conversion, or OWL imports.
This flexible approach supports both no-code modeling and advanced, pipeline-integrated development in notebooks or through APIs.
Building Big Data's Brain
The Paths to SQL Ontologies
Start from raw data sources, import existing ERDs/OWL models, or define business concepts first. All modeling approaches create the same SQL-native ontologies with relationships, measures, business rules, and hierarchies, no matter which path you choose.
Automatic / AI-Assisted Modeling
Accelerate modeling with auto-generated concepts, properties, and mappings in bulk from database schemas. AI can infer relationships and suggest structure, which you refine.
Top-Down Modeling
Model top-level business domains first, then refine into related sub-concepts with inherited properties and relationships.
Example: Customer → VIP Customer.
Bottom-Up Modeling
Use the data schema to create concepts, relationships and measures. The parent concept unifies child concepts with shared properties.
Example: Facebook Ads + Google Ads → Ads.
ERD / OWL
Import
Jump-start modeling by importing existing ERDs or OWL ontologies, reusing prior investments in semantic definitions.
Visual
Modeling
Use Timbr’s Ontology Explorer to create concepts, hierarchies, relationships, and business rules in a no-code environment.
DDL Statements
Use Timbr’s SQL Lab to define concepts, hierarchies, relationships, measures and business rules using familiar SQL statements.
Ontology Modeling Approaches Comparison
| Approach | Best For | How Timbr Models It | What It Gives the Customer | Example Use Case |
|---|---|---|---|---|
| Automatic Modeling (AI) | Accelerating model creation from existing schemas | Generate concepts, properties, and mappings in bulk from database schemas, with suggested structure that users can review and refine | Fast model creation while keeping human control over the final ontology | Bulk-generate ontology from schema |
| Top-Down | "Blank slate" conceptual modeling of data, defining business domains first, then refining into sub-concepts | Define a parent concept first, then create sub-concepts that inherit properties, relationships, mappings, and apply business filters | A clean business hierarchy with reusable logic, consistent definitions, and less duplicated modeling work | Customer → VIP Customer, Premium Customer |
| Bottom-Up | Creating concepts from existing schemas and tables | Map source-specific concepts first, then create a parent concept that unifies their shared properties and relationships | A single semantic view over fragmented data, without users writing JOINs and UNIONs logic | Facebook Ads + Google Ads → Ads |
| ERD Import | Modernizing existing relational models or database designs | Convert existing tables, keys, and relationships into semantic concepts, properties, mappings, and relationships | Reuse existing data architecture while turning it into a governed semantic model | Turn a legacy CRM schema into Customer, Account, Contact, and Opportunity concepts |
| OWL Import | Reusing existing semantic standards or enterprise ontologies | Bring existing ontology definitions into Timbr’s SQL-native ontology model | Preserve prior semantic modeling work while making it usable through SQL, BI, and AI workflows | Reuse an industry ontology or enterprise knowledge model |
| SQL DDL Modeling | Data engineers and platform teams that want code-based, versionable modeling | Define concepts, relationships, measures, mappings, and rules using SQL statements | Supports advanced development, automation, governance, and CI/CD-style workflows | Create ontology objects in SQL Lab or through deployment scripts |
Zero Data Movement
Ontology modeling in Timbr lets you query live data where it resides. Concepts act as virtual tables, while semantic relationships automatically combine data across sources. Models evolve without restructuring, and no ingestion or ETL pipelines are required.
- Query live data where it lives
- No ingestion or ETL required
- Models evolve without restructuring data
- Mappings connect concepts directly to tables and columns
No matter where your data lives, Timbr exposes it through the ontology-based model that makes distributed querying intuitive and efficient.
Multiple Schema Views
Timbr automatically generates five virtual schemas that let you query the ontology in different ways:
timbr schema (Intrinsic) – Base concepts and their properties mapped directly to your tables.
etimbr schema (Exhaustive) – Adds all inherited and derived properties from parent and sub-concepts.
dtimbr schema (Dereferenced) – Lets you traverse relationships as if they were properties, replacing joins with graph-style navigation.
vtimbr schema (Views) – Exposes saved views created on top of the ontology.
gtimbr schema (Graph) – Enables graph algorithms on concepts (licensed add-on).
These Schemas are auto-generated, letting you query ontologies relationally, with inheritance, or as a graph.
The Timbr Advantage: Ontologies Made Simple
From modeling to consumption:
- Choose your modeling approach: Start bottom-up from data, top-down from business concepts, or import existing ERDs/OWL ontologies
- Create semantic concepts: Define business entities as virtual tables with properties, relationships, and inheritance
- Map to live data: Connect concepts directly to your source tables without moving or copying data
- Query with context: Use SQL dot notation to traverse relationships instead of writing complex joins.
- Export and govern: Move models between environments and integrate with BI tools like Power BI
Benefits at a Glance
For Data Teams
Model in hours or days instead of weeks or months. No ETL pipelines required, query live data where it resides. Semantic relationships replace joins, while inheritance ensures consistent properties across concepts.
For Business Users
Work with governed business concepts instead of raw tables. Use familiar SQL and BI tools without learning graph languages. Explore data visually and benefit from AI-ready models for copilots and assistants.
For the Enterprise
Unlock the ROI of ERDs, Catalogs and OWL investments with one governed semantic model across BI, AI, and analytics. Ensure consistent definitions, accelerate AI adoption with structured context, and scale models seamlessly across dev → staging → prod.
