MetaKarta's Data Catalog harvests metadata automatically from 400+ sources. The catalog reflects your actual, live data estate. New sources appear without manual intervention. Changes apply across the metamodel when they happen.
Passive catalog tools wait for humans to document data. MetaKarta actively harvests metadata from every connected source, stitches it into a unified picture of your estate, and keeps it current. No manual tagging campaigns. No quarterly cleanup sprints. The catalog reflects what's actually there.
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MetaKarta connects to 400+ source systems and pulls metadata automatically. Databases, ETL pipelines, BI tools, cloud platforms. If it's in your estate, it's in the catalog.
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Data sampling and profiling runs at scale. Column statistics, data quality indicators, relationship discovery, and semantic classification surface automatically.
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PII detection and sensitive data tagging run without manual review. Findings trigger governance workflows and policy enforcement immediately.
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Every catalog entry links to the same lineage, governance policies, and semantic definitions on the shared metadata repository. The catalog isn't a silo. It's connected to the infrastructure that governs it.
Automatically pulls metadata from 400+ sources. New assets appear without manual registration. Changes are applied uniformly to the unified metadata.
Column statistics, null rates, cardinality, and data quality indicators computed automatically across the full estate.
Detects and tags sensitive data without manual review. Findings trigger stewardship workflows and masking enforcement.
Automatically maps relationships between tables, fields, and business terms. Building a business glossary from scratch gets a running start.
Data stewards annotate, rate, and certify datasets. Trusted data products get surfaced to analysts who need them.
Every catalog entry carries full lineage context and links to the governance policies that apply to it. The catalog is operational, not decorative.
Accurate on day one, stale by month six.
Documentation-first catalogs depend on humans to keep them current. Data stewards register assets, tag fields, and write descriptions. When the underlying data changes, someone has to notice and update the catalog manually.
Drawbacks
Catalog accuracy degrades when the data estate changes faster than humans can document it
PII detection depends on manual tagging, creating compliance gaps wherever coverage slips
Catalog entries carry no lineage context. You can find a dataset but can't trace where it came from or what depends on it
Governance policies live in the catalog UI, separate from the systems that actually implement them
Harvested automatically, connected to the infrastructure.
MetaKarta's catalog harvests metadata continuously from 400+ sources. It doesn't wait for a human to register an asset. The catalog reflects the live estate. PII is classified automatically. Every entry connects directly to lineage, governance policies, and semantic definitions within the shared metadata repository.
Advantages
Active harvesting keeps your catalog current and connected without the manual maintenance overhead
Automated PII classification scales compliance across the full estate, with no tagging gaps
Every catalog entry carries column-level lineage context: where the data came from, what transformation touched it, what depends on it
No integration needed between lineage, catalog, governance, and semantics
Federated data discovery
Analysts search a single catalog that reflects the full estate: cloud warehouses, on-prem databases, BI tools, and data lakes.
Certified data products
Data stewards certify trusted datasets and surface them prominently. Analysts spend less time second-guessing data and more time using it.
Connected business glossary
Business terms link directly to technical fields. Semantic lineage traces how definitions flow from source through transformation to report.
Automated PII detection (GDPR, CCPA, HIPAA)
PII and sensitive data classification runs automatically across the full estate. No manual tagging campaigns.
Data access governance
Catalog entries connect directly to access control policies. Who can see what is enforced, with a complete audit trail.
Audit readiness on demand
Every metadata change is versioned, attributed, and timestamped. Auditors ask for documentation. You produce it.
Compiled context for AI agents
AI agents and text-to-SQL tools query against catalog-connected definitions with full provenance, not raw schema.
Data quality visibility before AI consumption
Profiling results surface data quality indicators before any dataset feeds an AI pipeline.
MCP-delivered catalog context
MetaKarta MCP Metadata Management Tools access delivers catalog data, lineage context, and governance status to AI consumers across the full shared metadata 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.
Active harvesting. MetaKarta connects to 400+ sources and pulls metadata continuously, so new assets appear and changes propagate without anyone registering or re-tagging them. The catalog tracks the estate, and drift between the two has no place to accumulate.
Connection. Every MetaKarta catalog entry carries column-level lineage and links to the governance policies that apply to it, because catalog, lineage, governance, and semantic definitions all read from one shared metadata repository. A standalone catalog documents assets; a connected one lets you trace and defend them.
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.
Automated classification detects and tags PII and sensitive data across the full estate, and findings immediately trigger stewardship workflows and masking enforcement. That's how GDPR, CCPA, and HIPAA coverage scales past what a manual tagging program can sustain.
Yes. Semantic and relationship discovery maps tables and fields to business terms automatically, stewards certify trusted datasets, and the connected business glossary shows what each term means and which technical fields implement it.
AI agents and text-to-SQL tools work against catalog-connected definitions that carry lineage and governance status, so every answer traces to governed meaning. Profiling results also flag data quality issues before a dataset ever feeds a pipeline.