Finance's revenue figure comes from Power BI. Sales' figure comes from Tableau. They don't match, and teams spend roughly 20% of every sprint reconciling numbers like these.

Semantic Hub is MetaKarta's semantic compiler capability. Define business logic once, then compile it into the native format of every database, BI tool, and AI consumer that needs it. Write once, compile anywhere.

Why definitions fragment

Every BI tool carries its own embedded definition of revenue, churn, and active customers. The same metric starts drifting the moment a second tool arrives, and each migration adds another copy.

Run-time semantic layers route every query through a central engine to enforce one definition. That puts the business logic inside the vendor's runtime, adds a component to every query path, and ties your definitions to that engine staying available.

How Semantic Hub works

Import what you already have. Semantic Hub reverse-engineers semantic models from databases (Oracle, Snowflake, Databricks, SAP HANA), legacy BI tools (MicroStrategy, SAP BusinessObjects, IBM Cognos FM, Oracle OBIEE), and modern BI tools (Power BI, Tableau). Metric expressions convert along with the models, so years of BI modeling become the governed starting point.

Reconcile and model. Compare, deduplicate, and merge conflicting definitions, so a conflict gets resolved once in the model. Teams write models in Semantic Hub Language (SHL), an open-source, YAML-based language, and edit them visually or as code in the Semantic Model Editor.

Test before deploying. Model testing runs a query against the live data source and checks the result against the expected answer before anything compiles.

Compile to native artifacts. Governed models compile into Snowflake Semantic Views, Databricks Metric Views, Oracle Analytic Views, Power BI semantic models, Tableau logical models, and LookML. Security policies compile into native row-level and platform-specific controls, and Git and CI/CD integration handles branching, review, and automated deployment.

Ontology Modeling. Teams define business entities, relationships, and hierarchies as a governed ontology built on the glossary, then bind them to the semantic models and physical assets that implement them. Every concept is bound to a real asset from day 1.

Where the compiled definition runs

We don't sit in your query path. We've already compiled into your database and tools.

Each compiled definition runs inside the target platform's own engine, with no MetaKarta runtime to manage. The artifacts live in your systems.

Remove us tomorrow. Your definitions stay.

The compiled artifact is the proof. Anyone can read the definition in the system that executes it, and it's identical in every tool because every tool got it from the same model.

AI grounded in the same definitions

The definitions that compile into BI tools also ground AI. Structured AI consumption in Snowflake Cortex Analyst and Databricks Genie reads the compiled Snowflake Semantic Views and Databricks Metric Views directly, and MetaKarta MCP's Semantic Hub Tools serve compiled context and deterministic SQL to external agents.

Context Sandbox runs a real business question against the governed ontology and its physical bindings, then compares the answer with an approved reference before anything reaches production.

Same answer, AI and BI. Proven accuracy. Deterministic.

What it delivers

Trusted BI. Reliable AI. Defensible governance.

Trusted BI. One compiled artifact in every database and BI tool, with nothing to sync.

Reliable AI. Definitions are native in the agent's grounding context, and every concept points to the asset that implements it.

Defensible governance. The enforced definition is the audited definition.

One repository under every capability

Semantic Hub reads from and writes to the same shared metadata repository as Data Lineage, Data Catalog, and Data Governance. Every compiled definition carries its lineage from the source columns to the artifact in each target system.

The definitions that deploy are the ones stewards approved in Data Governance, under version control with every change attributed and reversible. Catalog entries for BI sources show the semantic models they hold, ready to import.

Where teams put it to work

BI Metric Consistency. Define the metric once, compile it into every BI tool and warehouse, and version every future change.

Data & BI Platform Migration. Retiring MicroStrategy, SAP BusinessObjects, IBM Cognos, or Oracle OBIEE starts with importing their semantic logic. The governed model then compiles into the new target, and adding or dropping a BI tool later doesn't mean redefining a metric.

AI Context & Governance. Agents query compiled definitions with provenance attached, and Context Sandbox validates their answers before production.

Metadata Tool Consolidation. One governed model compiles into Snowflake, Databricks, and Oracle alongside Power BI, Tableau, and LookML, on the same repository as lineage, catalog, and governance.

Proof points

• Compile-time architecture: definitions run natively in each target, outside any MetaKarta runtime
• Native compilation to Snowflake Semantic Views, Databricks Metric Views, Oracle Analytic Views, Power BI semantic models, Tableau logical models, and LookML
• Reverse-engineering from Oracle, Snowflake, Databricks, SAP HANA, MicroStrategy, SAP BusinessObjects, IBM Cognos FM, Oracle OBIEE, Power BI, and Tableau
• Semantic reconciliation, model testing, and Git and CI/CD integration
• Ontology Modeling bound to physical assets, with Context Sandbox validation for AI grounding
• Compiled context and deterministic SQL for agents through MetaKarta MCP

What compiling the definition changes

When a definition compiles into every tool that uses it, the numbers match because they come from one source.

Semantic Hub runs on the same shared metadata repository as Data Lineage, Data Catalog, and Data Governance. It's built on nearly 30 years of metadata engineering embedded in Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend.

Get in touch to learn more about compiling one governed revenue metric into Power BI, Tableau, and your warehouse at the same time.