ontologies for business intelligence
Tableau
Timbr integrates seamlessly with Tableau, providing a centralized semantic layer that replaces complex data prep and duplicated logic.
Instead of modeling calculations and relationships inside Tableau workbooks or data sources, users connect to Timbr ontologies, leveraging governed metrics, hierarchies, and cubes for consistent, high-performance analytics.
Key Capabilities
Use Live Query
or Extract
Connect via live query or extract to a governed semantic model
Consistent Metric
Definitions
Centralize business definitions, measures and hierarchies and reuse across all dashboards.
Controlled
Access
Enforce row-level and role-based access controls directly within the semantic layer.
Reduce
Complexity
Eliminate repetitive logic in Tableau Prep or calculated fields
Access Virtualized
Data
Support data virtualization across multiple sources with one connection
Accelerate
Dashboards
Accelerate performance with auto-aggregations and a 4-tier caching engine
Timbr vs. Tableau Data Model: What’s the Difference?
While Tableau’s data model allows users to define joins, relationships, and calculated fields, it lacks the centralized governance and reusability that Timbr provides.
| Feature / Need | Tableau Data Model | Timbr Ontology Based SL |
|---|---|---|
| Modeling Approach | Per workbook or data source | StaCentralized, SQL-native, reusable across tools |
| Metric consistency | No, defined manually in each workbook | Define once, reuse anywhere |
| Hierarchies | Basic drill paths | Ontology driven rich hierarchies that apply to measures |
| Caching & auto-aggregations | Manual extracts or logic | 4-tier cache with usage-based auto-aggregates |
| Data source virtualization | One source at a time (via joins/unions) | Federate across databases via a unified model |
| Security (RLS, CLS) | Workbook-level filters or Row-Level Security | Centralized RBAC, RLS, CLS in the semantic model |
When to Use Timbr with Tableau:
- You want to standardize metrics across multiple dashboards or departments.
- You need to connect Tableau to multiple data sources without flattening or duplicating data.
- You’re hitting complexity limits in Tableau Prep or calculated fields.
- You want to scale governed analytics across BI tools, not just within Tableau.
Live vs. Extract Mode:
Using Timbr with Tableau
Timbr supports both Live and Extract connection modes in Tableau, depending on your performance, freshness, and cost requirements:
| Mode | How it Works with Timbr | Best for |
|---|---|---|
| Live |
Tableau sends queries directly to Timbr’s semantic layer; Timbr pushes SQL to the database or serves from cache |
Real-time data, governed access, fast response via semantic cache |
| Extract | Tableau extracts data returned from a Timbr query into a workbook snapshot | Scheduled reporting, disconnected access, lighter Tableau server loads |
With Timbr’s semantic caching, even Live mode can achieve near-extract performance, without sacrificing freshness or central control.
Tip:
Use Live mode for most dashboards, backed by Timbr’s auto-aggregations.
Use Extracts selectively for static summaries or offline distribution.
OLAP Capabilities
Tableau is powerful for visualizing multidimensional data, but it lacks a built-in semantic or OLAP modeling engine. Timbr fills this gap by providing reusable cubes, hierarchies, and measures, defined in SQL and queryable across tools. With Timbr, Tableau gains OLAP-style structure and performance, without manual joins, redundant logic, or cube maintenance.
Think: cube-like flexibility, governed metrics, and data virtualization. all delivered through SQL to Tableau in real time or extract mode.
Here’s how Timbr offers OLAP-like functionality:
- Define cubes, dimensions, and measures using standard SQL.
- Create semantic hierarchies for drill-down and roll-up operations.
- Use semantic auto-aggregations and a four-tier caching engine to serve queries from pre-aggregated projections.
- Support MDX endpoints for Excel and legacy pivoting tools.
- Push compute and aggregations down to your data warehouse for real-time performance.
While Timbr does not deliver “traditional” OLAP cubes, it achieves the same goals, governed, multidimensional analysis at scale, through virtual models and semantic intelligence. That’s why we describe Timbr as “OLAP-like”: it’s OLAP, reimagined for the cloud and SQL-first data stacks.
How it Works
Tableau connects to Timbr through a standard ODBC or SQL connector. Instead of importing flat tables or building logic inside workbooks, users query a semantic model where business concepts, metrics, and relationships are already defined in SQL. Timbr rewrites Tableau queries for optimal execution, either pushing logic to the underlying database or serving results from a semantic cache, delivering governed insights faster and at scale.
