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Introduction to OpenMetadata Standards

Overview

OpenMetadata Standards is a comprehensive specification for metadata management that unifies how we describe, govern, and utilize metadata across the entire data ecosystem. It provides a standard vocabulary and structure for representing all aspects of your data landscape.

What Problem Does It Solve?

The Metadata Fragmentation Challenge

Modern data platforms consist of diverse technologies:

  • Databases (PostgreSQL, MySQL, Oracle, MongoDB)
  • Data warehouses (Snowflake, BigQuery, Redshift)
  • Data lakes (S3, ADLS, GCS)
  • Message queues (Kafka, Pulsar, RabbitMQ)
  • BI tools (Tableau, Looker, PowerBI)
  • ML platforms (MLflow, SageMaker, Databricks)
  • Pipeline orchestrators (Airflow, Prefect, Dagster)

Each tool has its own way of representing metadata. This fragmentation leads to:

  • Silos: Metadata trapped in individual tools
  • Duplication: Same metadata defined multiple times
  • Inconsistency: Conflicting definitions across systems
  • Limited Visibility: No unified view of the data landscape
  • Integration Challenges: Difficult to build cross-platform features

The OpenMetadata Solution

OpenMetadata Standards provides:

  1. Unified Schema: A single, comprehensive schema covering all metadata types
  2. Extensibility: Customizable to meet specific organizational needs
  3. Interoperability: Based on open standards (JSON Schema, RDF, JSON-LD)
  4. Versioning: Proper schema evolution and backward compatibility
  5. Validation: Built-in constraints and validation rules

Architecture

OpenMetadata Standards consists of three main layers:

1. JSON Schema Layer

The foundation is a comprehensive set of JSON Schemas that define:

  • Entity Schemas: Data assets, services, teams, policies
  • Type System: Reusable types and custom properties
  • API Schemas: REST API request/response formats
  • Event Schemas: Change events and notifications
  • Configuration Schemas: System configuration options

Key Benefits:

  • Machine-readable and validatable
  • Language-agnostic
  • Excellent tooling support (IDE completion, validation)
  • Easy to generate code from schemas

2. RDF/OWL Ontology Layer

A semantic layer that provides:

  • Ontology: Formal definitions of concepts and relationships
  • Provenance: W3C PROV-O for lineage tracking
  • SHACL Shapes: Validation constraints
  • JSON-LD Contexts: Semantic mapping for JSON data

Key Benefits:

  • Semantic reasoning and inference
  • Integration with knowledge graphs
  • SPARQL query capabilities
  • Linked data and URI-based references

3. Standards Compliance Layer

Ensures compatibility with:

  • Industry standards (ISO, W3C)
  • Data governance frameworks
  • Regulatory requirements
  • Best practices

Core Principles

1. Comprehensiveness

Cover all aspects of metadata management:

  • Technical Metadata: Schemas, columns, data types
  • Business Metadata: Glossary terms, descriptions, ownership
  • Operational Metadata: Usage, performance, SLAs
  • Governance Metadata: Policies, tags, classifications
  • Lineage Metadata: Data flows and transformations
  • Quality Metadata: Tests, profiling, assertions

2. Flexibility

Support diverse use cases through:

  • Custom properties on any entity
  • Extensible type system
  • Plugin architecture for connectors
  • Configurable workflows

3. Evolution

Enable schema evolution while maintaining compatibility:

  • Semantic versioning
  • Backward compatibility guarantees
  • Deprecation policies
  • Migration guides

4. Openness

Built on open standards:

  • Open source (Apache 2.0 license)
  • Community-driven development
  • Vendor-neutral
  • Well-documented

Who Should Use This?

Data Engineers

  • Design data pipelines with proper metadata
  • Track lineage across transformations
  • Monitor data quality

Data Analysts

  • Discover datasets through rich metadata
  • Understand data context and meaning
  • Ensure data quality before analysis

Data Governance Teams

  • Define and enforce policies
  • Classify sensitive data
  • Ensure compliance
  • Manage access controls

Platform Engineers

  • Build metadata-driven tools
  • Integrate metadata across systems
  • Implement governance automation

ML Engineers

  • Track model training data
  • Document feature engineering
  • Monitor model performance
  • Ensure reproducibility

Key Concepts

Entities

Core metadata objects:

  • Data Assets: Tables, topics, dashboards, ML models
  • Services: Connections to external systems
  • Teams & Users: Organizations, people, roles
  • Governance: Glossaries, tags, policies
  • Observability: Tests, metrics, incidents

Relationships

Connections between entities:

  • Contains: Database contains tables
  • Owns: Team owns dashboard
  • Uses: Pipeline uses table
  • Produces: Query produces table
  • DerivedFrom: Table derived from another table

Properties

Attributes of entities:

  • Core Properties: Always present (name, type, id)
  • Optional Properties: May be present based on entity type
  • Custom Properties: User-defined extensions
  • Computed Properties: Derived from other properties

Events

Changes to metadata:

  • Entity Events: Created, updated, deleted
  • Change Events: Specific field changes
  • System Events: Ingestion, indexing
  • Custom Events: Application-specific

Standards Ecosystem

OpenMetadata Standards integrates with:

  • JSON Schema: Schema definition and validation
  • OpenAPI: API specifications
  • RDF/OWL: Semantic web standards
  • SHACL: Shape validation
  • JSON-LD: Linked data
  • PROV-O: Provenance tracking
  • DCAT: Data catalog vocabulary
  • Dublin Core: Metadata element set

Next Steps