MOUNTAIN VIEW, Calif. — Meta Integration Know-how, Inc. (MITI) at present introduced common availability of MetaKarta v12, together with Semantic Hub that compiles ruled enterprise definitions into the databases, BI instruments, and AI brokers that depend upon them. A definition authored as soon as in MetaKarta lands as a local artifact inside Snowflake, Databricks, Energy BI, and Tableau, and reaches AI brokers as compiled context drawn from the identical repository, so a finance dashboard and an AI agent answering the identical query return the identical quantity. MetaKarta v12 is constructed to supply three outcomes from one system: trusted BI, dependable AI, and defensible governance.
MITI has constructed OEM metadata infrastructure for practically 30 years, and its know-how ships at present inside merchandise from Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend. MetaKarta v12 brings that depth and breadth of metadata engineering to enterprises straight, with 400+ native connectors and parser-based, column-level lineage computed from precise code.
Now in v12, organizations personal the semantic artifacts compiled and deployed in their very own databases and BI instruments. Definitions keep transportable by a migration, for instance from SAP BusinessObjects to Energy BI, or Oracle to Snowflake
or Databricks.
The compiled definitions are already in place earlier than any question runs, so no MetaKarta proprietary execution engine sits within the question path. Those self same definitions are delivered as compiled context to AI brokers.
A shift to MetadataOps
MetaKarta v12 additionally displays a shift MITI calls MetadataOps: metadata administration shifting from periodic initiatives to a steady enterprise operation. As information, purposes, definitions, and AI programs change, the metadata connecting them has to vary with them. MetaKarta gives the shared basis for conserving that work steady throughout information lineage, information catalog, information governance, and semantics.
“For many of our historical past we constructed OEM elements, and that labored whereas the issue lived inside one platform,” stated Christian Bremeau, Founder and CEO of Meta Integration Know-how. “AI moved it. Most enterprises now level brokers at metadata fragmented throughout a lineage instrument, a catalog, a governance instrument, and a number of semantic layers, so the agent picks whichever definition sits nearest the info and acts on it at machine velocity, with no analyst catching the discrepancy.”
“That’s why we expect metadata administration now has to function as MetadataOps, a steady self-discipline moderately than a collection of disconnected initiatives,” Bremeau continued. “We constructed v12 so the enterprise that means beneath an agent is reliable earlier than it ever acts. Definitions must be transportable, vendor-neutral, and owned by the enterprise that depends upon them. When stories, dashboards, and AI brokers all begin from unified metadata, folks and brokers get the identical reply wherever the query comes from.”
What’s inside MetaKarta v12
MetaKarta v12 expands the metadata administration platform to incorporate Semantic Hub, platform-wide AI, and a totally redesigned consumer expertise.
- Semantic Hub: Reverse-engineer present semantic belongings from Energy BI, Tableau, Looker, MicroStrategy, SAP BusinessObjects, IBM Cognos, and Oracle OBIEE, govern them centrally, then compile them into native artifacts: Snowflake Semantic Views, Databricks Metric Views, Oracle Analytic Views, Energy BI semantic fashions, Tableau logical fashions, and LookML.
- Semantic Hub Language (SHL): A YAML-based language for outlining semantic fashions.
- MetaKarta MCP for Agentic AI: Metadata Administration Instruments present entry to ruled metadata, whereas Semantic Hub Instruments ship compiled context and deterministic SQL to AI brokers from the shared metadata repository. Per-user entry tokens imply an agent sees precisely what the individual behind it’s licensed to see.
- Context Sandbox: Validate AI grounding context earlier than deployment, tracing a solution from supply to output.
- Ontology Modeling: Outline enterprise entities, relationships, and hierarchies in MetaKarta Enterprise Glossary as a ruled ontology, certain to the semantic fashions and bodily columns that implement them.
- Ask MetaKarta: Pure-language entry to ruled metadata for enterprise and technical customers.
What MetaKarta adjustments
- Trusted BI. When “income” is coded individually in Energy BI and in Tableau, finance and gross sales reconcile the distinction earlier than anybody can act on the quantity. One ruled definition compiles into each.
- Dependable AI. An agent querying uncooked schema at run-time infers what a metric means from column names, and infers it in another way each time. Compiled context provides the canonical definition and the provenance behind it, alongside structured AI consumption inside Snowflake, Databricks, and Oracle.
- Defensible Governance. Auditors ask what a area meant on a selected date, the way it was calculated, and who authorised the change. Model-controlled definitions, lineage, and audit-ready metadata flip that request into a question.
Full particulars can be found at www.metakarta.com.
The MetadataOps Equipment is offered at www.metakarta.com/assets/metadataops, masking the apply, working life cycle, metadata high quality scoring mannequin, a paste-ready job description and expertise matrix for the MetadataOps Engineer function, and extra.
SD Occasions Q&A
What’s MetaKarta v12 and what does it do?
MetaKarta v12 is an enterprise metadata administration platform from Meta Integration Know-how (MITI) that unifies information lineage, information catalog, information governance, and semantic layer administration right into a single system. Its Semantic Hub function lets groups writer a enterprise definition as soon as and compile it natively into instruments like Snowflake, Databricks, Energy BI, and Tableau. The platform additionally delivers ruled metadata context to AI brokers by way of an MCP integration.
How does MetaKarta Semantic Hub push definitions into Snowflake and Databricks?
Semantic Hub reverse-engineers present semantic belongings from BI instruments (Energy BI, Tableau, Looker, MicroStrategy, SAP BusinessObjects, IBM Cognos, Oracle OBIEE) and governs them centrally. It then compiles these definitions into native artifacts — Snowflake Semantic Views, Databricks Metric Views, Oracle Analytic Views, Energy BI semantic fashions, Tableau logical fashions, and LookML — so the compiled definition exists earlier than any question runs, with no proprietary execution engine within the question path.
What’s MetadataOps and the way is it totally different from conventional metadata administration?
MetadataOps, a time period coined by MITI, frames metadata administration as a steady operational self-discipline moderately than a collection of periodic initiatives. Conventional metadata administration typically entails one-off information catalog or governance initiatives; MetadataOps treats metadata as a residing system that updates robotically as information, purposes, and AI programs change. MetaKarta v12 is designed to function the shared operational basis for this ongoing apply.
How does MetaKarta v12 present ruled context to AI brokers?
MetaKarta v12 features a Mannequin Context Protocol (MCP) integration that exposes two classes of instruments to AI brokers: Metadata Administration Instruments for accessing ruled metadata, and Semantic Hub Instruments that ship compiled context and deterministic SQL drawn from the central metadata repository. Per-user entry tokens guarantee an agent can solely entry information the licensed consumer behind it’s permitted to see.
Does MetaKarta v12 assist column-level information lineage?
Sure. MetaKarta v12 presents parser-based, column-level lineage computed from precise code moderately than inferred at runtime. With 400+ native connectors, it may possibly hint lineage throughout a variety of knowledge platforms, BI instruments, and ETL programs, which helps fulfill audit necessities round how a selected area was calculated and who authorised adjustments.
