Events
Gartner C-Level CDAO Executive Summit NYC Dec 16, 2026
Events
Meta Integration Technology is proud to sponsor and present at the Gartner C-Level CDAO Executive Summit NYC.
Metadata Management Platform
Get the same answer
from AI and BI
For enterprise data teams whose AI agents, BI tools, and warehouses disagree, MetaKarta traces each definition to its source, gives it an owner and version history, and compiles it into Snowflake, Databricks, Power BI, Tableau, and more. Your agents get the same definitions as compiled context. You keep your business logic, and nothing sits in your query path.
Vendor neutral
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Parser-based lineage
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Compile-time architecture
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Shared metadata repository
Outcomes
Trusted BI. Reliable AI.
Defensible governance.
Inconsistent reporting, unreliable AI, and governance you can't demonstrate share one root cause: business logic fragmented across the stack. MetaKarta fixes it by enforcing shared definitions across the systems you already use.
Prove every number you publish
Every team works from the same business definitions, so board reports and dashboards agree no matter who built them or where the data came from.
Ground AI in meaning you already govern
AI agents read the governed business definitions your people already use. Without them, an agent works from raw schemas and conflicting definitions, and leaves no audit trail.
Defend any definition on demand
Business definitions keep their owner and version history everywhere they're used, so an audit, a regulatory request, or an internal review is answered from the record.
Capabilities
Engineered together from the start
Most enterprises run several metadata tools, bought at different times for different problems and held together with custom integrations that need constant care. MetaKarta’s Data Lineage, Data Catalog, Data Governance, and Semantic Compiler read from and write to one metamodel, so nothing has to be integrated or synchronized after the fact.
Data Lineage
Build lineage by parsing the code that moves your data: SQL, ETL jobs, BI semantic models, pipeline definitions, and even COBOL copybooks. It is column-level and cross-system, from source to the report or AI output it feeds. Parsed code captures what was designed to run, including paths run-time logs never see. Lineage by design, not by observation.
Overview
Any number traces to source in seconds, at the column level
Lineage runs from source through transformation to AI output
Full version and configuration management supports audit answers
Data Catalog
Document every asset in the estate and make it findable, with profiling, semantic discovery, and social curation built in. Every entry carries its source, its owner, and its lineage, so context arrives with its evidence attached. It reads from the same repository as lineage, governance, and semantics, so it stays current with the systems it documents.
Overview
Definitions are findable with lineage attached
Agents and humans share the same governed context
Ownership and meaning are documented where people look
Data Governance
Define policy once and let the platform carry it. Stewardship workflows assign ownership and accountability, automated classification finds PII and sensitive data at scale, and access controls compile into the systems where the data is actually used. Every governance decision is versioned, so you can answer what a policy said, who changed it, and when.
Overview
Stewardship assigns ownership and accountability
Policy travels with the definition into an AI agent's context
Version history, policy, and audit trail live in one view
Semantic Compiler
Import and model business logic once, then compile and deploy it into native formats, including Snowflake Semantic Views, Databricks Metric Views, Power BI semantic models, and Tableau data sources. The artifacts are native and customer-owned, with no proprietary run-time dependency. AI agents ground on the same compiled definitions your BI tools run, so the same question gets the same answer everywhere.
Overview
Portable, customer-owned native artifacts are deployed to targets
Definitions are native in an AI agent's grounding context
The enforced definition is the audited definition
Featured Client
“Knowing exactly where a number comes from, how it was transformed, and what it means in each system is a hard requirement for a regulated insurance environment. MetaKarta gives our teams traceability across the entire chain, and it works at the design level, not just at runtime. That’s what makes it defensible.”
The Platform
Four capabilities compound on a shared foundation
MetaKarta is built on a single shared metadata repository, with built-in metadata version and configuration management. Lineage, catalog, governance, and semantics all operate from a single foundation, against the same data, with no reconciliation required among them. With 400+ connectors, no source in your estate sits outside it.
See the whole estate
Gain complete visibility into your data estate: where it lives, where it flows, and what depends on it, before a ticket is logged.
Define what data means
Connect business meaning to technical reality, defining what data should mean, who owns it, and how it should be used.
Make definitions hold
Compile governed definitions into the tools that consume data, so that what governance says is what systems actually do.
Heritage
We built the engine
inside the tools you already trust.
MetaKarta is built by Meta Integration Technology, Inc., the company that has provided OEM metadata connectivity to the world's leading data platforms for nearly 30 years. Now, the same engine is available to enterprise data teams directly as a complete platform: 400+ connectors, multi-architecture support, and parser-based column-level lineage proven at petabyte scale in the most demanding enterprise environments on the market.
30 years of OEM metadata infrastructure inside the platforms your enterprise already runs
Solutions
Start with the question you're already being asked
Every data team fields the same hard questions from the business. MetaKarta answers each of them from one set of governed definitions, compiled into the tools where the question comes up.
Fix business logic at the foundation
MetaKarta establishes the shared metadata foundation organizations have been trying to patch together for years: trusted BI, reliable AI, and defensible governance, compiled into every architecture, every cloud, and every tool. No runtime dependency. No vendor lock-in. No greenfield start required.
Frequently asked
questions
What is MetaKarta?
MetaKarta is a Metadata Management Platform from Meta Integration Technology, Inc. It connects data lineage, data catalog, and data governance on one shared metadata repository, and compiles governed business definitions into the native formats of databases, BI tools, and AI agents. The platform is built on nearly 30 years of OEM metadata technology embedded in products from Microsoft Purview, Informatica from Salesforce, Oracle, IBM, and Qlik Talend.
How does MetaKarta fit into an existing data stack?
MetaKarta connects to your existing estate through 400+ connectors covering legacy and modern systems, on-premises and cloud. It harvests metadata from the tools you already run, then compiles governed definitions back into them natively. Nothing sits in your query path and there's no runtime dependency, so current workflows keep working exactly as they do today.
Do we need to replace our current tools?
No. MetaKarta integrates with the databases, pipelines, and BI tools you already have deployed, and value starts with the estate as it stands. Many teams do consolidate point tools for lineage, cataloging, or governance over time, since those functions run on MetaKarta's shared repository, but displacement is optional.
How does MetaKarta support AI initiatives?
MetaKarta delivers governed business definitions, relationships, policies, and provenance to AI systems. Agents query through structured consumption in Snowflake Cortex Analyst and Databricks Genie, or connect via MetaKarta MCP, and each answer comes back with the supporting compiled context behind it. Column-level lineage provides an auditable provenance chain from data source through transformation to output, ready for emerging AI governance mandates.
How is MetaKarta different from a data catalog?
MetaKarta includes a full data catalog and goes further: it compiles governed definitions into the systems that consume data. Documentation describes what should be true. Compilation makes it true. That's the difference between recording a definition and enforcing it.
How long does a typical implementation take?
Most teams start delivering value within weeks. MetaKarta deploys alongside existing systems and harvests metadata through connectors, so initial lineage and catalog coverage lands quickly, with scope growing from there. Full enterprise rollouts vary with the complexity of the estate.
Is MetaKarta available on-premises?
Yes. MetaKarta runs as hosted SaaS, self-hosted in your own cloud, or entirely on-premises within your own infrastructure, providing feature parity across all 3 deployment models.