OpenMetadata for Semantic Layers: Open Source Metadata Integration

OpenMetadata is an open-source unified metadata platform that has gained significant adoption for data discovery, lineage, quality, and governance. As an open-source solution, it offers transparency, community contributions, and freedom from vendor lock-in - making it attractive for organizations building modern data stacks with semantic layer capabilities.

Integrating OpenMetadata with semantic layers combines open-source metadata management with AI-powered analytics, creating context-aware systems that understand both business meaning and data reality.

OpenMetadata Capabilities

Data Discovery

OpenMetadata catalogs data assets across the stack:

Database Assets: Tables, views, schemas from databases and warehouses Dashboard Assets: Reports and visualizations from BI tools Pipeline Assets: ETL jobs and transformations ML Assets: Models and features for machine learning Storage Assets: Files and datasets in data lakes

Comprehensive discovery provides the inventory for semantic layer source mapping.

Business Glossary

Define and manage business terminology:

Terms and Definitions: Standardized business vocabulary Hierarchies: Parent-child relationships between terms Related Terms: Associations between concepts Asset Linking: Connecting terms to physical data

Glossary terms can seed semantic layer metric definitions.

Data Lineage

Trace data flow through systems:

Automated Extraction: Parse SQL and connector metadata Table Lineage: Dataset-level dependencies Column Lineage: Field-level transformation tracking Cross-Platform: Lineage across databases, ETL, and BI

Lineage enables semantic layer metrics to trace back to sources.

Data Quality

Assess and monitor data fitness:

Test Definitions: Configurable quality checks Test Execution: Scheduled quality validation Quality Scores: Aggregated quality ratings Incident Management: Issue tracking and resolution

Quality metadata informs trust decisions in analytics.

Ownership and Governance

Assign accountability and policies:

Owners and Experts: Responsibility assignments Tags and Classifications: Organizational metadata Tiers: Importance and trust levels Teams: Collaborative ownership

Governance metadata flows through to analytics access and trust display.

OpenMetadata Architecture

Core Components

Metadata Store: MySQL or PostgreSQL for persistent metadata Search Engine: Elasticsearch for discovery and querying Workflow Engine: Airflow for metadata processing and quality tests API Server: REST and GraphQL interfaces for integration

Connectors

OpenMetadata connects to data platforms:

Databases: Snowflake, BigQuery, Redshift, PostgreSQL, MySQL, and many more Transformation: dbt, Airflow, Fivetran, Airbyte BI Tools: Tableau, Looker, Metabase, Superset Storage: S3, GCS, Azure Blob

Connectors extract metadata automatically, keeping the catalog current.

APIs

Integration-friendly interfaces:

REST API: Standard HTTP endpoints for all metadata operations GraphQL: Flexible queries for complex metadata traversal Python SDK: Programmatic access for automation Webhooks: Event notifications for reactive integration

Codd AI Integrations connect to OpenMetadata APIs to extract and operationalize metadata.

Integrating with Semantic Layers

Glossary to Metrics

Map OpenMetadata glossary terms to semantic layer definitions:

1. Extract glossary terms tagged as metrics or KPIs
2. Retrieve term definitions, descriptions, and related terms
3. Map to semantic layer metric structure
4. Link referenced data assets to source tables
5. Synchronize updates as glossary evolves

# OpenMetadata glossary term
term:
  name: "Monthly Active Users"
  definition: "Count of unique users with activity in trailing 30 days"
  related_terms: ["Daily Active Users", "User Engagement"]
  linked_assets: ["analytics.fact_user_activity"]

# Translated to semantic layer
metric:
  name: "monthly_active_users"
  description: "Count of unique users with activity in trailing 30 days"
  source: "analytics.fact_user_activity"
  calculation: "count_distinct(user_id) where activity_date >= current_date - 30"

Asset-Based Source Mapping

Use OpenMetadata assets for semantic layer source configuration:

• Database and schema information for connection details
• Table metadata for source table definitions
• Column metadata for field mappings
• Tag information for access control policies

Lineage-Powered Documentation

Enhance semantic layer with OpenMetadata lineage:

• Show metric data origins in documentation
• Enable impact analysis from semantic layer perspective
• Link to OpenMetadata for detailed lineage exploration
• Alert when upstream changes affect metrics

Quality-Aware Metrics

Surface OpenMetadata quality in analytics:

• Import quality scores for metrics and dimensions
• Display quality indicators in dashboards
• Warn users about low-quality data usage
• Filter queries by minimum quality thresholds

Implementation Approach

Setup and Connection

Establish OpenMetadata integration:

1. Deploy OpenMetadata or connect to existing instance
2. Run connectors to populate metadata from sources
3. Enrich with glossary terms and documentation
4. Configure API access credentials
5. Test connectivity and permissions

Metadata Extraction

Pull relevant metadata for semantic layer:

# Example: Extracting glossary terms via API
import requests

response = requests.get(
    "https://openmetadata.company.com/api/v1/glossaryTerms",
    headers={"Authorization": f"Bearer {token}"}
)

for term in response.json()['data']:
    if 'metric' in term.get('tags', []):
        semantic_metric = {
            'name': term['name'],
            'description': term['description'],
            'sources': [asset['name'] for asset in term.get('relatedAssets', [])]
        }
        import_to_semantic_layer(semantic_metric)

Synchronization Strategy

Keep semantic layer current with OpenMetadata:

Scheduled Sync: Periodic extraction for batch updates Webhook-Driven: React to metadata changes in real-time Hybrid: Critical metadata via webhooks, comprehensive sync scheduled

Bidirectional Enrichment

Feed analytics insights back to OpenMetadata:

• Usage statistics from query patterns
• Popularity rankings for assets and terms
• Quality issues discovered during analytics
• User feedback on definitions

dbt Integration Synergy

OpenMetadata's dbt integration creates powerful synergy:

dbt to OpenMetadata

• Model documentation flows to catalog
• Tests inform quality metadata
• Lineage from ref() and source() relationships
• Tags and meta properties transfer

OpenMetadata to Semantic Layer

• Enriched documentation powers metrics
• Quality metadata informs trust
• Lineage enables provenance

Organizations using dbt for transformations get seamless metadata flow from source through modeling to analytics.

Advantages of Open Source Integration

No Vendor Lock-In

OpenMetadata avoids proprietary metadata formats:

• Standard data models
• API-first architecture
• Exportable metadata
• Community-driven standards

Integration with semantic layers remains portable.

Community Innovation

Active development community:

• Rapid feature development
• Community connectors for diverse sources
• Shared best practices
• Responsive issue resolution

Cost Optimization

Open-source economics:

• No licensing costs for metadata platform
• Infrastructure costs controlled by self-hosting
• Investment in integration rather than licensing
• Scalable without per-seat pricing

Transparency

Open development model:

• Visible roadmap and priorities
• Community input on direction
• Code inspection for security review
• Clear understanding of capabilities

Challenges and Considerations

Self-Management

Open-source requires operational investment:

• Infrastructure provisioning and management
• Upgrades and patch management
• Monitoring and troubleshooting
• Capacity planning and scaling

Organizations must staff appropriately or consider managed offerings.

Enterprise Features

Some enterprise capabilities may be developing:

• SSO and advanced authentication
• Audit logging completeness
• Multi-tenancy maturity
• Enterprise support SLAs

Evaluate against specific requirements.

Community Dependency

Community projects have different dynamics:

• Governance and direction may shift
• Contributor availability varies
• Long-term sustainability considerations
• Enterprise confidence building

Building Open Metadata-Powered Analytics

Codd AI Integrations provide native connectivity to OpenMetadata, enabling organizations to leverage open-source metadata management while building AI-powered semantic layers. This combination delivers modern analytics capabilities without proprietary lock-in, creating flexible architectures that evolve with organizational needs.

OpenMetadata integration brings community-driven metadata management to context-aware analytics - proving that open-source solutions can power enterprise-grade semantic layers.