{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-open-metadata--openmetadata","slug":"open-metadata--openmetadata","name":"OpenMetadata","type":"platform","url":"https://open-metadata.org","page_url":"https://unfragile.ai/open-metadata--openmetadata","categories":["data-pipelines"],"tags":["data-catalog","data-collaboration","data-contracts","data-discovery","data-governance","data-lineage","data-observability","data-profiling","data-quality","data-quality-checks","data-validation","datadiscovery","dataengineering","dataquality","hacktoberfest","mcp","mcp-server","metadata","metadata-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-open-metadata--openmetadata__cap_0","uri":"capability://data.processing.analysis.unified.metadata.repository.with.entity.relationship.modeling","name":"unified metadata repository with entity-relationship modeling","description":"OpenMetadata implements a centralized metadata store using a typed entity model (databases, tables, columns, dashboards, pipelines, etc.) persisted in PostgreSQL/MySQL with REST API access. The Entity Management and Repository Layer provides CRUD operations on metadata entities with version control, lineage tracking, and relationship management through a schema-driven approach that enforces consistency across all ingested metadata sources.","intents":["Store and query metadata from 100+ data sources in a single normalized schema","Track relationships between data assets (table → column → pipeline → dashboard)","Maintain audit history and versioning of metadata changes","Enable programmatic access to metadata via REST APIs"],"best_for":["Enterprise data teams managing heterogeneous data stacks (Snowflake, BigQuery, Redshift, etc.)","Organizations building internal data catalogs with governance requirements","Data platform engineers needing a metadata backbone for lineage and discovery"],"limitations":["Requires external relational database (PostgreSQL 12+ or MySQL 8.0+) — no embedded option","Entity schema is opinionated; custom metadata fields require extension of core entity types","Metadata updates are synchronous; bulk operations on 100k+ entities may cause latency spikes"],"requires":["PostgreSQL 12+ or MySQL 8.0+","Java 11+ runtime","Elasticsearch 7.10+ or OpenSearch 1.0+ for search indexing","Docker or Kubernetes for deployment"],"input_types":["JSON/YAML metadata from connectors","REST API payloads","CSV bulk imports","Lineage graphs from data pipelines"],"output_types":["JSON REST API responses","Structured entity objects","Lineage DAGs","CSV exports"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_1","uri":"capability://data.processing.analysis.column.level.data.lineage.tracking.and.visualization","name":"column-level data lineage tracking and visualization","description":"OpenMetadata tracks data lineage at column granularity by parsing SQL queries, ETL job definitions, and pipeline DAGs to build a directed acyclic graph (DAG) of data transformations. The Lineage and Domain Management system stores lineage edges in the metadata repository and exposes them via REST APIs and UI visualizations, enabling users to trace data provenance from source to sink and identify downstream impact of schema changes.","intents":["Trace which source columns feed into a specific dashboard metric","Identify all downstream tables affected by a source table schema change","Understand data transformation logic without reading code","Validate data quality issues by tracing to their origin"],"best_for":["Data engineers debugging data pipeline failures","Analytics engineers understanding metric dependencies","Data stewards assessing impact of upstream changes","Compliance teams tracing PII and sensitive data flows"],"limitations":["Lineage accuracy depends on connector's ability to parse SQL/DAG definitions — complex dynamic SQL may not be captured","Column-level lineage requires explicit column mapping; implicit transformations (SELECT *) lose granularity","Lineage updates are not real-time; depends on connector execution frequency (typically hourly/daily)","No automatic lineage inference for unstructured transformations (Python scripts, Spark RDDs)"],"requires":["Data connectors configured for the source systems (Snowflake, BigQuery, Airflow, dbt, etc.)","SQL parsing library support (OpenMetadata uses sqlparse for basic parsing)","Lineage ingestion job running on schedule"],"input_types":["SQL query logs","DAG definitions (Airflow, dbt)","ETL job metadata","Data pipeline configurations"],"output_types":["Lineage DAG (JSON/GraphQL)","Lineage UI visualization","Impact analysis reports","Lineage API responses"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_10","uri":"capability://automation.workflow.kubernetes.native.deployment.and.scaling","name":"kubernetes-native deployment and scaling","description":"OpenMetadata provides Kubernetes Operator and Helm charts for cloud-native deployment, enabling declarative infrastructure-as-code management of OpenMetadata instances. The deployment architecture supports horizontal scaling of the OpenMetadata service (stateless), with external PostgreSQL/MySQL and Elasticsearch/OpenSearch backends. The Kubernetes Operator automates upgrades, configuration management, and backup/restore operations, enabling GitOps-based deployment workflows.","intents":["Deploy OpenMetadata on Kubernetes with declarative configuration","Scale OpenMetadata service horizontally for high availability","Automate upgrades and configuration changes using GitOps","Integrate OpenMetadata deployment with existing Kubernetes infrastructure"],"best_for":["Organizations running Kubernetes clusters (EKS, GKE, AKS, on-prem)","Teams practicing GitOps and infrastructure-as-code","Enterprises requiring high availability and disaster recovery"],"limitations":["Requires Kubernetes 1.19+; not suitable for teams without Kubernetes infrastructure","External database and search backend required; adds operational complexity","Operator is relatively new; may have edge cases and limited documentation","Backup/restore requires external tools (Velero, database snapshots); no built-in backup mechanism"],"requires":["Kubernetes 1.19+ cluster","Helm 3.0+","PostgreSQL 12+ or MySQL 8.0+ (external)","Elasticsearch 7.10+ or OpenSearch 1.0+ (external)"],"input_types":["Kubernetes manifests (YAML)","Helm values","ConfigMaps and Secrets"],"output_types":["Kubernetes Pods and Services","Deployment status and logs","Metrics (Prometheus-compatible)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_11","uri":"capability://data.processing.analysis.data.profiler.with.statistical.analysis.and.anomaly.detection","name":"data profiler with statistical analysis and anomaly detection","description":"OpenMetadata's Data Profiler computes statistical profiles for tables and columns (null counts, cardinality, min/max values, distribution histograms, correlation analysis) by executing SQL queries against source systems. Profiles are stored as metadata and tracked over time, enabling trend analysis and detection of statistical anomalies (e.g., sudden increase in null values, unexpected cardinality changes). The profiler integrates with data quality tests to provide context for quality issues.","intents":["Understand data distribution and quality baseline for tables and columns","Detect data quality anomalies by comparing current profiles to historical baselines","Identify columns with high null rates or unexpected cardinality","Correlate data quality issues with upstream changes using lineage"],"best_for":["Data teams implementing data quality monitoring without dedicated tools","Organizations needing lightweight profiling integrated with metadata catalog","Teams building data quality baselines for new data sources"],"limitations":["Profiling is resource-intensive; large tables (100M+ rows) may timeout or impact production systems","Profiles are computed on schedule (hourly/daily); real-time anomaly detection not supported","Limited statistical analysis compared to dedicated tools (Great Expectations); no ML-based anomaly detection","Correlation analysis is basic; no multivariate analysis or causal inference"],"requires":["Direct database connectivity to source systems","Database permissions (SELECT, ANALYZE)","Scheduling system (Airflow) for periodic profiling","Sufficient database resources for profiling queries"],"input_types":["Table/column metadata","Profiling configuration (sampling rate, metrics to compute)","Historical profiles"],"output_types":["Profile statistics (JSON)","Distribution histograms","Anomaly alerts","Trend reports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_2","uri":"capability://data.processing.analysis.multi.source.metadata.ingestion.with.100.connector.framework","name":"multi-source metadata ingestion with 100+ connector framework","description":"OpenMetadata's Metadata Ingestion Framework provides a plugin-based architecture for extracting metadata from diverse sources (databases, data warehouses, BI tools, data lakes, orchestration platforms). Each connector implements a standardized interface to extract entities, relationships, and lineage, transform them into OpenMetadata's entity model, and load them into the central repository. The framework supports both batch ingestion (scheduled jobs) and event-driven ingestion via Airflow, Kafka, or direct API calls.","intents":["Automatically discover and catalog tables, columns, and schemas from Snowflake, BigQuery, Redshift, PostgreSQL, etc.","Extract metadata from BI tools (Tableau, Looker, Power BI) including dashboard definitions and metric lineage","Ingest pipeline metadata from Airflow, dbt, Spark, and other orchestration tools","Sync metadata changes incrementally without full re-ingestion"],"best_for":["Data teams with heterogeneous tech stacks needing unified metadata","Organizations automating metadata discovery to reduce manual catalog maintenance","Teams building data governance workflows that depend on current metadata"],"limitations":["Connector quality varies; some sources (e.g., custom databases) may require custom connector development","Ingestion latency depends on source system size and connector efficiency — large warehouses (100k+ tables) may take hours","No built-in incremental sync for all connectors; some require full re-ingestion","Connector authentication requires storing credentials in OpenMetadata config — requires secure secret management"],"requires":["Python 3.8+ (ingestion framework is Python-based)","Appropriate credentials/API keys for each source system","Network connectivity to source systems","Airflow 2.0+ (optional, for scheduling ingestion jobs)"],"input_types":["Database connection strings","API credentials","SQL queries (for custom metadata extraction)","Configuration YAML files"],"output_types":["Metadata entities (tables, columns, dashboards, pipelines)","Lineage relationships","Data quality metrics","Ingestion logs and error reports"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_3","uri":"capability://data.processing.analysis.data.quality.profiling.and.automated.test.execution","name":"data quality profiling and automated test execution","description":"OpenMetadata's Data Profiler and Quality Validations system automatically computes statistical profiles (null counts, cardinality, distribution, min/max values) for tables and columns on a schedule, and executes user-defined data quality tests (e.g., 'column X should have <5% nulls', 'column Y values must match regex pattern'). Test results are stored as metadata entities linked to tables, enabling trend analysis and alerting on quality degradation. The system integrates with dbt tests, Great Expectations, and custom SQL validators.","intents":["Monitor data quality metrics (null %, cardinality, distribution) over time","Define and execute automated data quality tests on ingestion schedule","Detect data quality regressions and alert data owners","Correlate data quality issues with upstream lineage to identify root causes"],"best_for":["Data teams implementing data quality frameworks without dedicated tools (Great Expectations, dbt)","Organizations needing lightweight quality monitoring integrated with metadata catalog","Teams building data contracts and SLAs for data products"],"limitations":["Profiling requires direct database access and can be resource-intensive on large tables (100M+ rows); may impact production systems","Test execution is synchronous; complex tests may timeout on large datasets","Limited test types compared to dedicated tools (Great Expectations); custom tests require SQL knowledge","No built-in anomaly detection or ML-based quality scoring — only rule-based tests"],"requires":["Direct database connectivity to source systems","Appropriate database permissions (SELECT, ANALYZE)","Scheduling system (Airflow) for periodic profiling jobs","Test definitions in YAML or dbt format"],"input_types":["Table/column metadata","Test definitions (SQL, dbt, YAML)","Profiling configuration","Historical quality data"],"output_types":["Profile statistics (JSON)","Test execution results (pass/fail/error)","Quality trend reports","Alert notifications"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_4","uri":"capability://search.retrieval.semantic.search.and.faceted.discovery.across.metadata","name":"semantic search and faceted discovery across metadata","description":"OpenMetadata indexes all metadata entities (tables, columns, dashboards, pipelines, glossary terms) into Elasticsearch or OpenSearch, enabling full-text search with relevance ranking and faceted filtering by entity type, owner, domain, tags, and custom attributes. The Search and Indexing system uses BM25 scoring for relevance and supports advanced queries (wildcards, boolean operators, field-specific searches). Search results are ranked by relevance and enriched with lineage, ownership, and quality metadata.","intents":["Find tables/columns by natural language search ('customer revenue data', 'user id column')","Discover data assets by owner, domain, or tag without knowing exact names","Build data discovery UIs with faceted filtering and autocomplete","Enable non-technical users to find relevant data assets"],"best_for":["Organizations with 1000+ data assets needing efficient discovery","Data teams building internal data marketplaces","Analytics engineers searching for reusable datasets and metrics"],"limitations":["Search index is eventually consistent; newly ingested metadata may take 5-30 seconds to appear in search results","Relevance ranking is based on BM25; no ML-based personalization or collaborative filtering","Search does not understand semantic relationships (e.g., 'customer_id' and 'cust_id' are treated as different terms without synonyms)","Elasticsearch/OpenSearch cluster requires separate infrastructure and operational overhead"],"requires":["Elasticsearch 7.10+ or OpenSearch 1.0+","Metadata entities indexed and synchronized with search backend","Network connectivity between OpenMetadata service and search cluster"],"input_types":["Metadata entity objects (tables, columns, dashboards)","Search queries (text, facet filters)","Custom metadata attributes"],"output_types":["Ranked search results (JSON)","Facet counts and filters","Autocomplete suggestions","Search UI components"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_5","uri":"capability://safety.moderation.role.based.access.control.and.data.governance.workflows","name":"role-based access control and data governance workflows","description":"OpenMetadata implements fine-grained RBAC through the Authentication and Authorization system, supporting multiple auth providers (OAuth2, SAML, LDAP, custom) and role definitions (Admin, DataSteward, DataConsumer, etc.). Access control is enforced at entity level (who can view/edit specific tables, columns, dashboards) and operation level (who can approve data quality tests, manage glossaries). The system integrates with governance workflows (approval chains, ownership assignment, domain management) to enforce data stewardship policies.","intents":["Restrict metadata visibility based on user roles and team membership","Enforce approval workflows for metadata changes and data quality test definitions","Assign data ownership and stewardship responsibilities","Audit who accessed or modified metadata and when"],"best_for":["Regulated industries (finance, healthcare) requiring strict data governance","Large organizations with complex team structures and data ownership models","Teams implementing data stewardship and accountability frameworks"],"limitations":["RBAC is metadata-level only; does not enforce access control on actual data (requires separate data access controls)","Approval workflows are basic; complex multi-stage approvals require custom development","No attribute-based access control (ABAC) for dynamic policies based on data sensitivity","Audit logs are stored in OpenMetadata database; no integration with external SIEM systems"],"requires":["OAuth2, SAML, or LDAP provider configured","User/group definitions in identity provider","Role definitions in OpenMetadata configuration"],"input_types":["User/group identity from auth provider","Role assignments","Metadata change requests","Approval decisions"],"output_types":["Access control decisions (allow/deny)","Audit logs","Approval workflow status","User activity reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_6","uri":"capability://text.generation.language.collaborative.metadata.enrichment.and.glossary.management","name":"collaborative metadata enrichment and glossary management","description":"OpenMetadata provides collaborative features for teams to enrich metadata with descriptions, tags, glossary terms, and custom attributes. The Glossary and Domain Management UI enables creation of business glossaries with term hierarchies, definitions, and relationships to data assets. The Activity Feed and Rich Text Editor track all metadata changes with user attribution, enabling teams to discuss data assets, ask questions, and resolve ambiguities through inline comments and mentions.","intents":["Create and maintain business glossaries linked to data assets","Enable non-technical stakeholders to document data asset purpose and usage","Track who made metadata changes and when","Facilitate cross-team collaboration on data documentation"],"best_for":["Organizations building data literacy and documentation culture","Teams with distributed ownership of data assets","Regulated industries requiring documented data definitions"],"limitations":["Glossary management is basic; no version control or approval workflows for glossary changes","Comments and activity feed are stored in OpenMetadata; no integration with external communication tools (Slack, Teams)","No built-in workflows for metadata enrichment; relies on manual user input","Rich text editor is limited; no support for embedded media or complex formatting"],"requires":["User authentication configured","Glossary structure defined in OpenMetadata"],"input_types":["Glossary term definitions","Metadata descriptions and tags","User comments and mentions","Custom attribute values"],"output_types":["Enriched metadata entities","Glossary hierarchy (JSON/UI)","Activity feed (JSON/UI)","Metadata change history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_7","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.powered.metadata.access","name":"mcp server integration for ai-powered metadata access","description":"OpenMetadata exposes its metadata repository and capabilities through an MCP (Model Context Protocol) server, enabling AI agents and LLMs to query metadata, execute searches, retrieve lineage, and access data quality information via standardized MCP tools. The MCP Server and Java SDK (implemented in openmetadata-mcp module) provides authentication-enriched context extraction, allowing AI systems to respect OpenMetadata's RBAC policies while accessing metadata. This enables natural language queries over metadata ('show me all tables owned by the analytics team with quality issues') and AI-assisted data discovery.","intents":["Enable LLMs and AI agents to query metadata using natural language","Integrate OpenMetadata metadata into AI-powered data discovery and documentation tools","Allow AI systems to understand data lineage and quality context when generating insights","Build AI assistants that can answer questions about data assets and their relationships"],"best_for":["Teams building AI-powered data discovery and documentation tools","Organizations integrating metadata into LLM-based analytics assistants","Data teams using AI agents for metadata governance and quality monitoring"],"limitations":["MCP server is relatively new; limited tool coverage compared to full REST API","Authentication context extraction adds latency (~100-200ms per request)","No streaming support for large result sets; may timeout on complex queries","Requires MCP-compatible client (Claude, custom agents); not compatible with traditional REST clients"],"requires":["OpenMetadata 1.2+ with MCP server enabled","MCP-compatible client (Claude with MCP support, custom agent framework)","API key or OAuth2 token for authentication"],"input_types":["Natural language queries","MCP tool calls with parameters","Search filters and facets"],"output_types":["Metadata entities (JSON)","Search results","Lineage graphs","Quality metrics"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_8","uri":"capability://automation.workflow.data.contracts.and.sla.management.for.data.products","name":"data contracts and sla management for data products","description":"OpenMetadata supports definition and tracking of data contracts (agreements about data quality, freshness, and availability) and SLAs for data products. Data contracts are defined as metadata entities linked to tables/datasets, specifying expected quality metrics, update frequency, and ownership. The system tracks contract compliance by comparing actual data quality metrics (from profiling) against contract expectations, enabling data teams to validate that data products meet their promised SLAs.","intents":["Define SLAs for data products (e.g., 'customer table updated daily, <1% null values')","Monitor compliance with data contracts in real-time","Alert data owners when data products violate their SLAs","Build accountability for data quality across teams"],"best_for":["Organizations treating data as a product with defined SLAs","Teams implementing data mesh or data product architectures","Regulated industries requiring documented data quality commitments"],"limitations":["Data contracts are metadata-only; enforcement requires external systems (data quality tools, alerting)","SLA compliance tracking depends on regular data profiling; gaps in profiling lead to incomplete compliance visibility","No automatic remediation or rollback when contracts are violated","Limited integration with incident management systems for SLA breach escalation"],"requires":["Data quality profiling configured and running on schedule","Data contract definitions in OpenMetadata","Alerting system configured (email, Slack, etc.)"],"input_types":["Data contract definitions (SLA metrics, owners, expectations)","Data quality profiles","Freshness metrics"],"output_types":["Contract compliance reports","SLA breach alerts","Trend analysis (compliance over time)","Impact analysis (which downstream assets are affected by SLA breach)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-metadata--openmetadata__cap_9","uri":"capability://automation.workflow.event.driven.metadata.updates.and.webhook.notifications","name":"event-driven metadata updates and webhook notifications","description":"OpenMetadata's Event System and Workflows enable real-time metadata updates through event streaming (Kafka, webhook) and trigger-based workflows. When metadata changes occur (table added, quality test fails, ownership changes), events are published to configured webhooks or Kafka topics, enabling downstream systems to react. The system supports custom workflows that can execute actions (send notifications, update external systems, trigger data pipelines) based on metadata events.","intents":["Trigger downstream actions when metadata changes (e.g., notify analytics team when new table is added)","Sync metadata changes to external systems (data catalogs, data governance platforms)","Build real-time data quality alerting based on test failures","Automate metadata-driven workflows (e.g., auto-tag tables based on lineage)"],"best_for":["Organizations with event-driven architectures needing metadata event integration","Teams building metadata-driven automation and workflows","Regulated industries requiring real-time audit trails of metadata changes"],"limitations":["Event delivery is at-least-once; duplicate events possible and require idempotent handling","Webhook delivery is synchronous; slow webhooks can block metadata operations","No built-in event replay or recovery; lost events cannot be recovered","Workflow execution is basic; complex multi-step workflows require custom development"],"requires":["Kafka cluster (optional, for event streaming)","Webhook endpoints configured and accessible","Event subscription configuration in OpenMetadata"],"input_types":["Metadata change events (entity created/updated/deleted)","Data quality test results","Lineage changes"],"output_types":["Webhook payloads (JSON)","Kafka events","Workflow execution logs","Notification messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12+ or MySQL 8.0+","Java 11+ runtime","Elasticsearch 7.10+ or OpenSearch 1.0+ for search indexing","Docker or Kubernetes for deployment","Data connectors configured for the source systems (Snowflake, BigQuery, Airflow, dbt, etc.)","SQL parsing library support (OpenMetadata uses sqlparse for basic parsing)","Lineage ingestion job running on schedule","Kubernetes 1.19+ cluster","Helm 3.0+","PostgreSQL 12+ or MySQL 8.0+ (external)"],"failure_modes":["Requires external relational database (PostgreSQL 12+ or MySQL 8.0+) — no embedded option","Entity schema is opinionated; custom metadata fields require extension of core entity types","Metadata updates are synchronous; bulk operations on 100k+ entities may cause latency spikes","Lineage accuracy depends on connector's ability to parse SQL/DAG definitions — complex dynamic SQL may not be captured","Column-level lineage requires explicit column mapping; implicit transformations (SELECT *) lose granularity","Lineage updates are not real-time; depends on connector execution frequency (typically hourly/daily)","No automatic lineage inference for unstructured transformations (Python scripts, Spark RDDs)","Requires Kubernetes 1.19+; not suitable for teams without Kubernetes infrastructure","External database and search backend required; adds operational complexity","Operator is relatively new; may have edge cases and limited documentation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3649488900035015,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-03T10:12:51Z"},"community":{"stars":13782,"forks":2079,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=open-metadata--openmetadata","compare_url":"https://unfragile.ai/compare?artifact=open-metadata--openmetadata"}},"signature":"s+unLBHJr+Z+vZpRDbPEE8UfHkEA6NkK4cuthDT/FOZwPm5NCsGMvEM3+5TIw+ZRMPZjUbyalCOKgxvhs+LZAg==","signedAt":"2026-06-22T13:06:21.613Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open-metadata--openmetadata","artifact":"https://unfragile.ai/open-metadata--openmetadata","verify":"https://unfragile.ai/api/v1/verify?slug=open-metadata--openmetadata","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}