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OpenMetadata Specifications

Comprehensive metadata specifications for the modern data ecosystem

OpenMetadata Standards define metadata models across six key areas. Click any card to explore detailed specifications with JSON Schema, RDF, and JSON-LD representations.

Data Assets

10+ asset types across your data ecosystem

Model databases, pipelines, dashboards, ML models, topics, storage, APIs, and search indexes with complete hierarchical structures.

Entities: Table, Pipeline, Topic, Dashboard, Chart, ML Model, Container, API Endpoint, Search Index

Specifications: JSON Schema, RDF/OWL, JSON-LD

Explore Data Assets →

Governance

Business context and data classification

Define business glossaries, classify sensitive data with hierarchical tags, and apply governance policies across all assets.

Entities: Glossary, Glossary Term, Classification, Tag

Features: PII/PHI detection, GDPR compliance, business terminology, hierarchical taxonomies

Explore Governance →

Data Quality

Comprehensive testing and profiling framework

Define quality tests, execute validation, profile data distributions, and track quality metrics over time.

Entities: Test Definition, Test Case, Test Suite

Platforms: OpenMetadata, Great Expectations, dbt, Deequ, Soda

Explore Data Quality →

Lineage

End-to-end data flow tracking

Capture column-level and table-level lineage using W3C PROV-O provenance ontology. Track data from source to dashboard to ML model.

Standards: PROV-O, RDF provenance graphs, column-level lineage

Examples: API → Pipeline → Table → Dashboard

Explore Lineage →

Data Contracts

Formal SLAs for data assets

Define schema requirements, quality expectations, and service level agreements for any data asset - not just tables.

Apply to: Tables, Topics, Dashboards, ML Models, APIs, Storage

Includes: Schema contracts, quality SLAs, freshness guarantees

Explore Data Contracts →

Teams & Users

Organizational structure and ownership

Model users, teams, roles, and hierarchies. Assign ownership and define access policies across all assets.

Entities: User, Team, Role, Persona

Features: Ownership, hierarchies, permissions, collaboration

Explore Teams & Users →

Standards Representation

Every entity in OpenMetadata Standards is expressed in three complementary formats:

JSON Schema

Draft-07 validation schemas

  • 700+ entity schemas
  • Strongly typed definitions
  • Required/optional fields
  • Constraints and patterns
  • IDE autocomplete support

View JSON Schemas →

RDF & OWL

Semantic web ontology

  • W3C OWL ontology
  • RDFS classes & properties
  • Formal semantics
  • SPARQL queryable
  • Reasoning & inference

View RDF Ontology →

JSON-LD

Linked data contexts

  • JSON-LD 1.1 contexts
  • Maps JSON to RDF
  • Semantic annotations
  • Schema.org compatible
  • Web-scale integration

View JSON-LD →

Example: Customer Data Pipeline

See how all concepts work together:

graph LR
    A[PostgreSQL<br/>customers table] -->|Lineage| B[Airflow<br/>customer_etl]
    B -->|Lineage| C[Snowflake<br/>customers_dim]
    C -->|Lineage| D[Tableau<br/>Customer Dashboard]
    C -->|Lineage| E[MLflow<br/>Churn Predictor]

    A -.->|Tags| F[PII.Sensitive.Email<br/>Compliance.GDPR]
    A -.->|Glossary| G[Customer Term]
    A -.->|Quality Tests| H[Uniqueness<br/>Completeness]
    A -.->|Owner| I[Data Engineering]
    A -.->|Contract| J[SLA: 99% complete<br/>Update: 1hr]

    style A fill:#667eea,color:#fff
    style D fill:#f5576c,color:#fff
    style E fill:#4facfe,color:#fff
    style F fill:#fa709a,color:#fff
    style G fill:#43e97b,color:#fff
    style H fill:#00f2fe,color:#fff

Data Assets: Table (PostgreSQL), Pipeline (Airflow), Table (Snowflake), Dashboard (Tableau), ML Model (MLflow)

Governance: Email column tagged as PII.Sensitive.Email and Compliance.GDPR, table linked to "Customer" glossary term

Data Quality: Tests for uniqueness of customer_id, completeness of email

Lineage: Source table → ETL → Warehouse → Dashboard & ML Model

Ownership: Data Engineering team owns pipeline and tables

Data Contract: Table must be 99% complete, updated within 1 hour

Getting Started

1. Understand the Specifications

Browse the sections above to explore each metadata concept

2. Read Entity Specifications

Each entity has comprehensive documentation with:

  • Overview and use cases
  • Complete field reference
  • JSON Schema, RDF, and JSON-LD specifications
  • Real-world examples

3. See Standards in Action

Check out the Examples for complete use cases

4. Integrate with Your Tools

Use the API Reference to integrate OpenMetadata Standards

Why OpenMetadata Standards?

Key Benefits

  • Comprehensive - Covers all data assets, governance, quality, lineage, and contracts
  • Open Source - Freely available, community-driven, transparent
  • Semantic - RDF ontologies enable reasoning and knowledge graphs
  • Interoperable - JSON-LD enables integration with semantic web
  • Extensible - Custom properties for your specific needs
  • Battle-Tested - Used in production managing petabytes of data

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