OpenMetadata Development

Guide for using OpenMetadata Python/Java SDKs and REST APIs to build integrations, connectors, and automations.

When to Use This Skill

• Querying and updating metadata via SDK/API
• Building custom ingestion connectors
• Creating and managing custom properties
• Automating governance workflows
• Integrating OpenMetadata with external systems
• Managing lineage programmatically

This Skill Does NOT Cover

• Implementing new language SDKs (see

  openmetadata-sdk-dev

  )
• Administering OpenMetadata (bots, users, security) (see

  openmetadata-ops

  )
• Deploying or operating OpenMetadata infrastructure

SDK Setup

Python SDK Installation

pip install openmetadata-ingestion

Initialize Client

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

# Configure connection
server_config = OpenMetadataConnection(
    hostPort="http://localhost:8585/api",
    authProvider=AuthProvider.openmetadata,
    securityConfig=OpenMetadataJWTClientConfig(
        jwtToken="<your-jwt-token>",
    ),
)

# Create client
metadata = OpenMetadata(server_config)

# Verify connection
assert metadata.health_check()

Java SDK Setup

<dependency>
    <groupId>org.open-metadata</groupId>
    <artifactId>openmetadata-java-client</artifactId>
    <version>1.3.0</version>
</dependency>

OpenMetadataConnection server = new OpenMetadataConnection();
server.setHostPort("http://localhost:8585/api");
server.setAuthProvider(AuthProvider.OPENMETADATA);
server.setSecurityConfig(new OpenMetadataJWTClientConfig().withJwtToken("<token>"));

OpenMetadata client = new OpenMetadata(server);

Querying Metadata

Get Entity by Name

from metadata.generated.schema.entity.data.table import Table

# Get table by fully qualified name
table = metadata.get_by_name(
    entity=Table,
    fqn="prod.sales.public.orders",
    fields=["columns", "owner", "tags"],  # Optional: include related data
)

if table:
    print(f"Table: {table.name}")
    print(f"Columns: {[col.name for col in table.columns]}")
    print(f"Owner: {table.owner.name if table.owner else 'None'}")

Get Entity by ID

from uuid import UUID

table = metadata.get_by_id(
    entity=Table,
    entity_id=UUID("12345678-1234-1234-1234-123456789abc"),
    fields=["columns"],
)

List Entities with Pagination

from metadata.generated.schema.entity.data.table import Table

# List tables with pagination
tables = metadata.list_entities(
    entity=Table,
    limit=100,
    fields=["owner", "database"],
)

for table in tables.entities:
    print(f"{table.fullyQualifiedName}")

# Get next page
if tables.paging.after:
    next_page = metadata.list_entities(
        entity=Table,
        limit=100,
        after=tables.paging.after,
    )

Search Entities

# Search using Elasticsearch query
results = metadata.es_search_from_fqn(
    entity_type=Table,
    fqn_search_string="*orders*",
    size=50,
)

for hit in results:
    print(hit["_source"]["fullyQualifiedName"])

Creating and Updating Entities

Create Table

from metadata.generated.schema.api.data.createTable import CreateTableRequest
from metadata.generated.schema.entity.data.table import Column, DataType

create_request = CreateTableRequest(
    name="new_orders",
    databaseSchema="prod.sales.public",
    columns=[
        Column(name="id", dataType=DataType.BIGINT),
        Column(name="customer_id", dataType=DataType.BIGINT),
        Column(name="total", dataType=DataType.DECIMAL),
        Column(name="created_at", dataType=DataType.TIMESTAMP),
    ],
    description="Order transactions",
)

table = metadata.create_or_update(create_request)
print(f"Created table: {table.fullyQualifiedName}")

Update Entity Description

from metadata.generated.schema.type.tagLabel import TagLabel

# Update table description
metadata.patch_description(
    entity=Table,
    source=table,
    description="Updated description for orders table",
)

Add Tags to Entity

from metadata.generated.schema.type.tagLabel import (
    TagLabel,
    TagSource,
    LabelType,
    State,
)

tag = TagLabel(
    tagFQN="PII.Sensitive",
    source=TagSource.Classification,
    labelType=LabelType.Manual,
    state=State.Confirmed,
)

metadata.patch_tag(
    entity=Table,
    source=table,
    tag_label=tag,
)

Set Owner

from metadata.generated.schema.type.entityReference import EntityReference

# Get user reference
user = metadata.get_by_name(entity=User, fqn="john.doe")

# Set owner
metadata.patch_owner(
    entity=Table,
    source=table,
    owner=EntityReference(id=user.id, type="user"),
)

Custom Properties

Create Custom Property Type

from metadata.generated.schema.api.data.createCustomProperty import (
    CreateCustomPropertyRequest,
)
from metadata.generated.schema.type.customProperty import PropertyType

# Create custom property on Table entity
metadata.create_or_update_custom_property(
    ometa_custom_property=CreateCustomPropertyRequest(
        name="costCenter",
        description="Cost center for billing",
        propertyType=PropertyType(
            id=metadata.get_property_type("string").id,
            type="type",
        ),
    ),
    entity_type=Table,
)

Set Custom Property Value

# Set custom property value using extension
table = metadata.get_by_name(entity=Table, fqn="prod.sales.orders")

# Patch extension with custom property
metadata.patch(
    entity=Table,
    source=table,
    destination=table.copy(
        update={"extension": {"costCenter": "SALES-001"}}
    ),
)

Read Custom Property Value

table = metadata.get_by_name(entity=Table, fqn="prod.sales.orders")

if table.extension:
    cost_center = table.extension.get("costCenter")
    print(f"Cost Center: {cost_center}")

Supported Property Types

string

"SALES-001"

integer

42

number

3.14

markdown

"# Header\nContent"

enum

"option1"

date

"2024-01-15"

dateTime

"2024-01-15T10:30:00Z"

time

"10:30:00"

duration

"PT1H30M"

entityReference

{"id": "uuid", "type": "user"}

entityReferenceList

[{"id": "uuid", "type": "user"}]

Lineage Management

Add Lineage Edge

from metadata.generated.schema.api.lineage.addLineage import AddLineage
from metadata.generated.schema.type.entityLineage import EntitiesEdge

# Get source and target tables
source = metadata.get_by_name(entity=Table, fqn="raw.events")
target = metadata.get_by_name(entity=Table, fqn="analytics.user_events")

# Add lineage
metadata.add_lineage(
    AddLineage(
        edge=EntitiesEdge(
            fromEntity=EntityReference(id=source.id, type="table"),
            toEntity=EntityReference(id=target.id, type="table"),
        ),
    )
)

Add Column-Level Lineage

from metadata.generated.schema.type.entityLineage import ColumnLineage

metadata.add_lineage(
    AddLineage(
        edge=EntitiesEdge(
            fromEntity=EntityReference(id=source.id, type="table"),
            toEntity=EntityReference(id=target.id, type="table"),
            lineageDetails=LineageDetails(
                columnsLineage=[
                    ColumnLineage(
                        fromColumns=["raw.events.user_id"],
                        toColumn="analytics.user_events.user_id",
                    ),
                    ColumnLineage(
                        fromColumns=["raw.events.event_type"],
                        toColumn="analytics.user_events.event_type",
                    ),
                ],
            ),
        ),
    )
)

Query Lineage

lineage = metadata.get_lineage_by_name(
    entity=Table,
    fqn="analytics.user_events",
    up_depth=3,    # Upstream hops
    down_depth=3,  # Downstream hops
)

print("Upstream tables:")
for node in lineage.upstreamEdges:
    print(f"  - {node.fromEntity.name}")

print("Downstream tables:")
for node in lineage.downstreamEdges:
    print(f"  - {node.toEntity.name}")

Building Custom Connectors

Connector Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Workflow                                │
├─────────────────────────────────────────────────────────────┤
│  Source → Processor → Processor → Sink                      │
│    ↓          ↓           ↓         ↓                        │
│  Extract   Transform   Enrich    Load to                     │
│  Records    Data       Data      OpenMetadata                │
└─────────────────────────────────────────────────────────────┘

Source Implementation

from abc import ABC, abstractmethod
from typing import Iterable, Optional
from metadata.ingestion.api.models import Either
from metadata.ingestion.api.steps import Source
from metadata.ingestion.ometa.ometa_api import OpenMetadata

class MyCustomSource(Source):
    """Custom source for extracting metadata."""

    def __init__(self):
        super().__init__()
        self.config = None
        self.metadata = None

    @classmethod
    def create(
        cls,
        config_dict: dict,
        metadata: OpenMetadata,
    ) -> "MyCustomSource":
        instance = cls()
        instance.config = MySourceConfig.parse_obj(config_dict)
        instance.metadata = metadata
        return instance

    def prepare(self):
        """Initialize connections before extraction."""
        self.client = MyApiClient(self.config.api_url)

    def _iter(self) -> Iterable[Either]:
        """Yield records to downstream steps."""
        for item in self.client.list_items():
            yield Either(right=self._convert_to_entity(item))

    def _convert_to_entity(self, item) -> CreateTableRequest:
        """Convert API response to OpenMetadata entity."""
        return CreateTableRequest(
            name=item["name"],
            databaseSchema=self.config.database_schema,
            columns=[
                Column(name=col["name"], dataType=self._map_type(col["type"]))
                for col in item["columns"]
            ],
        )

    def close(self):
        """Cleanup resources."""
        if self.client:
            self.client.close()

    def test_connection(self) -> None:
        """Verify connectivity to source system."""
        self.client.health_check()

Processor Implementation

from metadata.ingestion.api.steps import Processor

class EnrichmentProcessor(Processor):
    """Add additional metadata to records."""

    @classmethod
    def create(cls, config_dict: dict, metadata: OpenMetadata):
        instance = cls()
        instance.config = EnrichmentConfig.parse_obj(config_dict)
        instance.metadata = metadata
        return instance

    def _run(self, record: CreateTableRequest) -> Either:
        """Process each record."""
        # Add custom enrichment
        record.description = self._generate_description(record)
        record.tags = self._auto_classify(record)
        return Either(right=record)

    def close(self):
        pass

Sink Implementation

from metadata.ingestion.api.steps import Sink

class OpenMetadataSink(Sink):
    """Write records to OpenMetadata."""

    @classmethod
    def create(cls, config_dict: dict, metadata: OpenMetadata):
        instance = cls()
        instance.metadata = metadata
        return instance

    def _run(self, record: CreateTableRequest) -> Either:
        """Write record to OpenMetadata."""
        try:
            entity = self.metadata.create_or_update(record)
            return Either(right=entity)
        except Exception as e:
            return Either(left=StackTraceError(str(e)))

    def close(self):
        pass

Workflow Configuration

# connector.yaml
source:
  type: MyCustomSource
  serviceName: my-source
  serviceConnection:
    config:
      api_url: https://api.example.com
      database_schema: prod.my_db.public

processor:
  type: EnrichmentProcessor
  config:
    auto_classify: true

sink:
  type: metadata-rest
  config: {}

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: openmetadata
    securityConfig:
      jwtToken: ${OM_JWT_TOKEN}

Run Workflow

from metadata.workflow.metadata import MetadataWorkflow

config = yaml.safe_load(open("connector.yaml"))
workflow = MetadataWorkflow.create(config)

workflow.execute()
workflow.print_status()
workflow.stop()

Automation Patterns

Bulk Tagging

def bulk_tag_tables(metadata: OpenMetadata, pattern: str, tag_fqn: str):
    """Apply tag to all tables matching pattern."""
    tables = metadata.es_search_from_fqn(
        entity_type=Table,
        fqn_search_string=pattern,
    )

    tag = TagLabel(
        tagFQN=tag_fqn,
        source=TagSource.Classification,
        labelType=LabelType.Automated,
        state=State.Confirmed,
    )

    for hit in tables:
        table = metadata.get_by_id(
            entity=Table,
            entity_id=UUID(hit["_source"]["id"]),
        )
        metadata.patch_tag(entity=Table, source=table, tag_label=tag)
        print(f"Tagged: {table.fullyQualifiedName}")

# Tag all PII tables
bulk_tag_tables(metadata, "*customer*", "PII.Sensitive")

Auto-Assign Owners

def auto_assign_owners(metadata: OpenMetadata, rules: dict):
    """Assign owners based on schema/database patterns."""
    for pattern, owner_fqn in rules.items():
        owner = metadata.get_by_name(entity=User, fqn=owner_fqn)
        owner_ref = EntityReference(id=owner.id, type="user")

        tables = metadata.es_search_from_fqn(
            entity_type=Table,
            fqn_search_string=pattern,
        )

        for hit in tables:
            table = metadata.get_by_id(
                entity=Table,
                entity_id=UUID(hit["_source"]["id"]),
            )
            if table.owner is None:
                metadata.patch_owner(entity=Table, source=table, owner=owner_ref)
                print(f"Assigned {owner_fqn} to {table.fullyQualifiedName}")

# Define ownership rules
rules = {
    "*.sales.*": "sales-team-lead",
    "*.analytics.*": "analytics-team-lead",
    "*.finance.*": "finance-team-lead",
}
auto_assign_owners(metadata, rules)

Data Quality Automation

from metadata.generated.schema.tests.testCase import TestCase
from metadata.generated.schema.tests.testDefinition import TestDefinition

def add_null_check_tests(metadata: OpenMetadata, table_fqn: str):
    """Add null check tests to all required columns."""
    table = metadata.get_by_name(
        entity=Table,
        fqn=table_fqn,
        fields=["columns"],
    )

    null_test = metadata.get_by_name(
        entity=TestDefinition,
        fqn="columnValuesToBeNotNull",
    )

    for column in table.columns:
        if column.constraint == "NOT NULL":
            test_case = TestCase(
                name=f"{column.name}_not_null",
                testDefinition=EntityReference(id=null_test.id, type="testDefinition"),
                entityLink=f"<#E::table::{table_fqn}::columns::{column.name}>",
                parameterValues=[],
            )
            metadata.create_or_update(test_case)
            print(f"Added null check for {column.name}")

Lineage Propagation

def propagate_tags_downstream(
    metadata: OpenMetadata,
    source_fqn: str,
    tag_fqn: str,
    max_depth: int = 3,
):
    """Propagate tags through lineage."""
    source = metadata.get_by_name(entity=Table, fqn=source_fqn)

    lineage = metadata.get_lineage_by_name(
        entity=Table,
        fqn=source_fqn,
        down_depth=max_depth,
    )

    tag = TagLabel(
        tagFQN=tag_fqn,
        source=TagSource.Classification,
        labelType=LabelType.Propagated,
        state=State.Confirmed,
    )

    for edge in lineage.downstreamEdges:
        downstream = metadata.get_by_id(
            entity=Table,
            entity_id=edge.toEntity.id,
        )
        metadata.patch_tag(entity=Table, source=downstream, tag_label=tag)
        print(f"Propagated {tag_fqn} to {downstream.fullyQualifiedName}")

# Propagate PII tag through lineage
propagate_tags_downstream(metadata, "raw.customers", "PII.Sensitive")

REST API Direct Usage

Authentication

# Get JWT token
curl -X POST "http://localhost:8585/api/v1/users/login" \
  -H "Content-Type: application/json" \
  -d '{"email": "admin@openmetadata.org", "password": "admin"}'

Common Endpoints

/api/v1/tables

/api/v1/tables/{id}

/api/v1/tables/name/{fqn}

/api/v1/tables

/api/v1/tables/{id}

/api/v1/tables/{id}

/api/v1/search/query?q={query}

/api/v1/lineage/{type}/{fqn}

Example: Create Table via REST

curl -X PUT "http://localhost:8585/api/v1/tables" \
  -H "Authorization: Bearer ${JWT_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "new_table",
    "databaseSchema": "prod.db.schema",
    "columns": [
      {"name": "id", "dataType": "BIGINT"},
      {"name": "name", "dataType": "VARCHAR"}
    ]
  }'

Error Handling

Common Exceptions

from metadata.ingestion.ometa.client import APIError

try:
    table = metadata.get_by_name(entity=Table, fqn="nonexistent.table")
except APIError as e:
    if e.status_code == 404:
        print("Table not found")
    elif e.status_code == 403:
        print("Permission denied")
    else:
        raise

Retry Pattern

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=1, max=10),
)
def resilient_create(metadata: OpenMetadata, entity):
    return metadata.create_or_update(entity)

Best Practices

Connection Management

# Use context manager pattern
class OpenMetadataSession:
    def __init__(self, config: OpenMetadataConnection):
        self.config = config
        self.client = None

    def __enter__(self) -> OpenMetadata:
        self.client = OpenMetadata(self.config)
        self.client.health_check()
        return self.client

    def __exit__(self, exc_type, exc_val, exc_tb):
        # Client cleanup if needed
        pass

# Usage
with OpenMetadataSession(config) as metadata:
    table = metadata.get_by_name(entity=Table, fqn="prod.sales.orders")

Batch Operations

def batch_update(metadata: OpenMetadata, entities: list, batch_size: int = 50):
    """Update entities in batches to avoid rate limits."""
    for i in range(0, len(entities), batch_size):
        batch = entities[i:i + batch_size]
        for entity in batch:
            metadata.create_or_update(entity)
        time.sleep(0.5)  # Rate limit protection

Idempotent Operations

def ensure_table_exists(metadata: OpenMetadata, create_request: CreateTableRequest):
    """Create table if not exists, otherwise return existing."""
    existing = metadata.get_by_name(
        entity=Table,
        fqn=f"{create_request.databaseSchema}.{create_request.name}",
    )
    if existing:
        return existing
    return metadata.create_or_update(create_request)

MCP Server Integration

OpenMetadata provides a Model Context Protocol (MCP) server that enables AI assistants (like Claude and ChatGPT) to interact with your metadata catalog using natural language.

What is MCP?

MCP (Model Context Protocol) is an open standard that allows AI systems to interact with external tools and data sources in a uniform, secure way. OpenMetadata's MCP server exposes its metadata knowledge graph to AI tools.

Use Cases

• Natural language queries about data assets
• "What tables contain customer data?"
• "Who owns the orders table?"
• "Show me the lineage for the sales dashboard"
• AI-powered data discovery
• Conversational data governance

Setting Up MCP

1. Install MCP Application

1. Navigate to Settings → Applications → Marketplace
2. Find the MCP application
3. Click Install
4. Configure the application settings

2. Generate Personal Access Token

1. Go to Profile → Access Token
2. Click Generate New Token
3. Set appropriate expiration
4. Copy token (shown only once)

3. Configure MCP Client

For Claude Desktop:

Add to your

claude_desktop_config.json

{
  "mcpServers": {
    "openmetadata": {
      "url": "http://localhost:8585/api/v1/mcp",
      "headers": {
        "Authorization": "Bearer <your-token>"
      }
    }
  }
}

For API Integration:

import requests

MCP_ENDPOINT = "http://localhost:8585/api/v1/mcp"
TOKEN = "<your-token>"

def query_mcp(prompt: str) -> dict:
    """Send natural language query to OpenMetadata MCP."""
    response = requests.post(
        f"{MCP_ENDPOINT}/query",
        headers={
            "Authorization": f"Bearer {TOKEN}",
            "Content-Type": "application/json",
        },
        json={"prompt": prompt},
    )
    return response.json()

# Example queries
result = query_mcp("What tables are in the sales database?")
result = query_mcp("Show me the owner of the customers table")
result = query_mcp("What is the lineage for the revenue dashboard?")

Available MCP Tools

The MCP server exposes tools for:

Security Considerations

1. Use dedicated tokens - Don't share personal tokens
2. Set appropriate permissions - MCP uses token's permissions
3. Rotate tokens regularly - Follow security policies
4. Audit usage - Monitor MCP queries in logs

References

• OpenMetadata Python SDK
• OpenMetadata Java SDK
• OpenMetadata API Reference
• Building Connectors
• Data Governance Automation
• MCP Server Guide

• openmetadata-sdk-dev

  - Implementing SDKs for new languages

• openmetadata-ops

  - Administering OpenMetadata

• openmetadata-user

  - UI navigation and discovery

• openmetadata-dq

  - Data quality and observability