Every BI platform keeps business logic in its own format: DAX measures in Power BI, calculated fields in Tableau, LookML in Looker. So the same metric gets rebuilt in each tool, a little differently each time.

That gets expensive in three places:

• Reconciliation: A fixed share of every sprint goes to explaining why two dashboards disagree.
• Migration: A Tableau to Microsoft Fabric move puts years of semantic models up for a rebuild.
• AI: Text-to-SQL pilots read raw schema, so the agent's revenue matches neither dashboard.

What changes with MetaKarta

Your existing models become the starting point. Semantic Hub reverse-engineers semantic models from Power BI and Tableau, from legacy tools including MicroStrategy, SAP BusinessObjects, IBM Cognos, and Oracle OBIEE, and from Snowflake, Databricks, Oracle, and SAP HANA. Years of modeling work arrive as the governed baseline.

Duplicate definitions reconciled once. Compare every version of revenue side by side, deduplicate them, and merge them into one governed definition with an owner. Model testing checks the result before it ships.

Write once, compile anywhere. One governed definition compiles natively into Power BI semantic models, Tableau logical models, LookML, Snowflake Semantic Views, Databricks Metric Views, and Oracle Analytic Views. Remove us tomorrow. Your definitions stay.

Agents read the same definition. Snowflake Cortex Analyst and Databricks Genie read the compiled artifacts natively, and MetaKarta MCP serves the same definition to other agents with deterministic SQL. Same answer, AI and BI. Proven accuracy. Deterministic.

How it works

MetaKarta runs on one shared metadata repository, so every compiled definition carries its lineage and governance with it.

• Semantic Hub reverse-engineering: Existing Power BI, Tableau, MicroStrategy, SAP BusinessObjects, IBM Cognos, and Oracle OBIEE models imported as the governed baseline.
• Reconciliation and model testing: Duplicate metric definitions compared, deduplicated, merged, and tested before they ship.
• Multi-target compilation: One definition compiled to six native targets, with security policies compiled to native controls.
• Semantic Hub Language (SHL): YAML-based models you branch, review in a pull request, and ship through Git and CI/CD.
• Parser-based lineage: Every BI measure traced back to its source column across 400+ native connectors.
• Ontology Modeling: Business vocabulary terms live-linked to the metric that implements them.

See it on your own estate

Three checks make a useful evaluation of any metadata platform, including ours:

1. The reconciliation check. Pick the two dashboards that disagree on revenue. Ask the vendor to import both models, show where the definitions diverge, and merge them into one.
2. The migration check. Take one production Tableau model and ask for it compiled into a Power BI semantic model, with the lineage behind every measure.
3. The exit check. Ask what happens to your definitions if you cancel the contract. Compiled artifacts stay in your tools; definitions served at run-time through a vendor's engine leave with the vendor.

Built on nearly 30 years of metadata engineering

MetaKarta's metadata technology runs inside the products of Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend. MetaKarta brings that engine to your analytics team directly, with definitions that stay portable as native artifacts in the tools you already own.

When one governed definition compiles into every tool, the metric that moves is the share of each sprint spent on reconciliation, and the number of models your next migration has to rebuild by hand.

Get in touch to learn more about how Semantic Hub carries your BI tools models and metrics through a migration.