Industry Validation
Gartner Magic Quadrant returns
"D&A leaders must improve metadata management maturity or risk AI failure."
Why MetaKarta
Definitions that hold in every system you run
MetaKarta compiles governed business logic natively into data warehouses, BI tools, and pipelines, while serving the same to AI agents as compiled context. Now you can trust that what governance says is what systems actually do.
Metadata management is no longer a documentation job
For two decades, metadata management meant writing things down in glossaries, catalogs, policies, and stewardship workflows. They were built to observe, document, and surface. That was enough when the consequences of inconsistency were survivable.
AI changed the stakes
Humans can catch BI discrepancies before they ship. Agents inherit fragmented business context without flagging it, passing the inconsistency straight through. The failure surfaces after damage has been done.
Regulations changed
GDPR, SOX, BCBS 239, and AI governance frameworks don't accept documented intent as proof of compliance. Policies that document intent without showing enforcement are a liability.
400+
Native connectors
~30 years
OEM metadata expertise
Vendor-Neutral Philosophy
The Switzerland of metadata
Enterprises need a single enforced definition of what their data means, across the full stack, so that reporting is trusted, AI is reliable, and governance is defensible.
Meta Integration Technology’s thirty years of OEM metadata expertise, embedded inside Microsoft Purview, Salesforce Informatica, Oracle, IBM, and Qlik Talend, provide the engineering depth and breadth needed for MetaKarta to meet the complexity of diverse enterprise data estates, now and in the future.
Spanning legacy to modern and on-premises to cloud, no source in your estate sits outside of MetaKarta. Plus, no run-time middleware and no proprietary engine means there’s no vendor lock-in.
The Architecture
MetaKarta platform architecture
MetaKarta fits into your existing ecosystem without replacing the tools your teams already rely on.
Enforce
Semantic Compiler
Reverse-Engineer | Designer | Compiler
Govern
Data Governance
Classification | Policy | Stewardship
Understand
Data Catalog
Discovery | Profiling | Social Curation
Understand
Data Lineage
Cross System | Column-Level | Design Time
Harvest
Metadata Management
Shared Repository | Configuration Management | Version Control
Compiles governed definitions
Warehouse
Snowflake | Databricks | Redshift
BI Tools
Power BI | Tableau | Looker
Data Pipelines
Informatica | SSIS | dbt Labs
LLMs & AI Agents
AI context & governance via MetaKarta MCP
Compile business logic for
Flexible Deployment
Designed to fit your data estate
Whether you choose hosted or self-hosted, it’s the same platform. You get feature parity anywhere you deploy it without sacrificing functionality.
Hosted SaaS
Fully managed by MetaKarta
Self-Hosted Cloud
Deploy in your own cloud environment
Self-Hosted On-Premises
Deploy entirely on your own infrastructure
The Architectural Difference
Alternatives only solve part of the problem
Fragmentation compounds with every tool you add. Definitions established in a catalog stop at the catalog. Logic defined in a runtime semantic layer governs a single consumer. Lineage tracked in one tool stays in that tool, disconnected from the governance workflow that depends on it.
Specialized solutions
One view of authority, and manual coordination between the rest
Each specialized tool holds a partial view of your data estate, with no shared repository connecting it to the others. The result is a governance program assembled from tools that share no common foundation, where enforcement runs on manual coordination and consistency depends on people keeping up.
Drawbacks
Catalog tools document definitions but have no authority over the warehouse or pipelines that implement them
Runtime semantic layers standardize metrics for one consumer but sit in the query path, adding latency, and have no lineage into the systems that produce the data
Log-based lineage tools show what ran but miss what was designed to run
Warehouse-native governance covers cloud assets but legacy systems and on-premises infrastructure remain outside it
MetaKarta
Integrated architecture on one shared foundation across all four capabilities
MetaKarta operates lineage, cataloging, governance, and semantic enforcement against a single shared metadata repository. Every governance decision is grounded in the same lineage. Every enforced definition reaches the systems that consume it natively, with no manual coordination in between.
Advantages
Governed definitions compiled into your warehouse, BI tools, and pipelines not documented for someone to act on later
400+ connectors cover your full estate: legacy and modern, on-premises and cloud
Lineage by design, not by observation. Computed from actual code artifacts, not inferred from runtime logs
AI agents reason against governed, canonical definitions, not raw, ungoverned data
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 makes MetaKarta different from other metadata management tools?
Most metadata stacks are assembled from separate lineage, catalog, governance, and semantic tools that each hold a partial view of the estate and need constant synchronization. MetaKarta runs all 4 capabilities on one shared metadata repository compiles governed definitions into downstream systems natively and delivers compiled context to AI agents. Consistency is a property of the architecture, and enforcement happens at build time.
What is compile-time enforcement?
Compile-time enforcement means governed definitions are written into the native format of each downstream system, such as Snowflake Semantic Views, Databricks Metric Views, Power BI semantic models, and Tableau data sources, before any query runs. Definitions arrive baked into the tool itself, so nothing has to intercept queries at runtime to apply them.
How is parser-based lineage different from log-based lineage?
Parser-based lineage is computed from the actual code that moves data: SQL, ETL jobs, and BI semantic models. It's column-level, cross-system, and complete by construction, including designed paths that haven't been executed recently. Log-based lineage infers flows from runtime observation, so it captures what happened to run during the observation window. Lineage by design, not by observation.
How does MetaKarta compare to a run-time semantic layer?
A run-time semantic layer standardizes metrics by sitting in the query path between BI tools and the warehouse, which adds latency and creates a run-time dependency. MetaKarta compiles governed definitions directly into each platform's native format, so queries run exactly as they do today. The compiled artifacts are customer-owned and keep working independently of MetaKarta.
Does MetaKarta add latency to queries?
No. We don't sit in your query path. We've already compiled into your database and tools. Queries execute natively in each platform, with no middleware in between and nothing that has to stay available at runtime.
What does vendor-neutral mean for MetaKarta?
Vendor neutrality is structural: MetaKarta connects to 400+ platforms spanning legacy and modern, on-premises and cloud, and compiles into each one's native format without a proprietary runtime. Meta Integration Technology has supplied OEM metadata technology to competing platform vendors for nearly 30 years, which is why it's been called the Switzerland of Metadata.