OpenMetadata

// Overview

OpenMetadata is an open-source platform for cataloguing data and the context around it. It connects technical metadata — databases, schemas, tables, columns, topics, dashboards, pipelines, ML models — to the things that decide whether that data can be trusted or used: ownership, usage, column-level lineage, quality tests and freshness checks, glossaries, classifications, domains, policies, data contracts and data products. The result is a single metadata knowledge graph rather than a list of tables.

The AI angle is what the project leads with now. A raw warehouse connector tells an assistant what columns exist; it does not say what a metric means, whether a dataset is certified, who owns it, which policy applies or what breaks downstream if a column changes. OpenMetadata exposes exactly that through an MCP server at `/mcp`, semantic search that matches by meaning rather than exact names, and AI SDKs for Python, TypeScript and Java — so an agent queries governed context instead of guessing from schema names.

It ships 130+ connectors for warehouses, BI tools, pipeline systems, quality tools and lineage sources, supports open standards including DCAT, DPROD, PROV-O, OpenLineage, ODCS and RDF/OWL, and keeps a layer of organisational memory — conversations, decisions, assumptions, runbooks and remediation notes attached to assets. The project is released under Apache 2.0 and runs self-hosted; a public sandbox is available for a first look.

// What it does

• 130+ connectors across databases, warehouses, BI tools, pipelines, quality tools and lineage sources
• Table-level and column-level lineage with downstream impact analysis
• Data quality and trust signals: test cases and suites, freshness, volume, null, uniqueness and distribution checks, profiling history
• Business semantics — glossaries, metrics, classifications, domains, policies, data products and data contracts
• MCP server plus semantic search and AI SDKs (Python, TypeScript, Java) so agents get governed context
• Open standards support: DCAT, DPROD, PROV-O, OpenLineage, ODCS, RDF/OWL, JSON-LD, SHACL, JSON Schema

// Getting started

Start on the hosted sandbox to see the model, then self-host and point it at your first source. All packages and snippets below come from the project README.

https://sandbox.open-metadata.org

https://docs.open-metadata.org/latest/quick-start

pip install "openmetadata-ingestion"
pip install data-ai-sdk

from metadata.generated.schema.entity.data.table import Table
from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
    OpenMetadataConnection, AuthProvider,
)
from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
    OpenMetadataJWTClientConfig,
)
from metadata.ingestion.ometa.ometa_api import OpenMetadata

metadata = OpenMetadata(OpenMetadataConnection(
    hostPort="http://localhost:8585/api",
    authProvider=AuthProvider.openmetadata,
    securityConfig=OpenMetadataJWTClientConfig(jwtToken="<your-token>"),
))
assert metadata.health_check()

table = metadata.get_by_name(entity=Table, fqn="sample_data.ecommerce_db.shopify.raw_product_catalog")
print(table.description, [c.name.root for c in table.columns])

https://docs.open-metadata.org/latest/how-to-guides/mcp

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Reach for it when an AI assistant needs to know what a dataset means and whether it can be trusted, not just its schema
• Reach for it to answer impact questions — which dashboards, pipelines or models break if this column changes
• Reach for it when governance needs owners, policies, classifications and data contracts in one place
• Reach for it to give agents governed, policy-aware access to enterprise data context through MCP

// How OpenMetadata compares

OpenMetadata alongside other open-source governance & compliance tools AI/TLDR tracks, ranked by GitHub stars.