collibra-connector 1.1.2

An UNOFFICIAL standard Python connector for the Collibra Data Governance Center API with full type safety, async support, and enterprise features.

Collibra Python Connector

An UNOFFICIAL comprehensive Python connector for the Collibra Data Governance Center API.

Features

• Complete API Coverage: Asset, Community, Domain, User, Responsibility, Workflow, Metadata, Comment, Relation, and Output Module management
• Automatic Retry Logic: Built-in retry mechanism with exponential backoff for transient errors
• Context Manager Support: Use with statement for automatic session management and connection pooling
• Pagination Helpers: Easy iteration over large datasets without memory issues
• Batch Processing: Process bulk operations efficiently with rate limiting
• Metadata Caching: Thread-safe cache for frequently accessed metadata (UUIDs, types, statuses)
• Input Validation: Comprehensive parameter validation with clear error messages
• Type Hints: Full type annotations for better IDE support and static analysis
• Custom Exceptions: Structured exception hierarchy for precise error handling

Installation

pip install collibra-connector

For development with testing tools:

pip install collibra-connector[dev]

Quick Start

Basic Usage

from collibra_connector import CollibraConnector

# Initialize the connector
connector = CollibraConnector(
    api="https://your-collibra-instance.com",
    username="your-username",
    password="your-password",
    timeout=30,           # Request timeout in seconds
    max_retries=3,        # Number of retry attempts
    retry_delay=1.0       # Base delay between retries
)

# Test connection
if connector.test_connection():
    print("Connected successfully!")

# Find communities
communities = connector.community.find_communities(limit=10)
for community in communities.get("results", []):
    print(f"Community: {community['name']}")

Using Context Manager (Recommended)

The context manager provides automatic session management and connection pooling:

from collibra_connector import CollibraConnector

with CollibraConnector(
    api="https://your-collibra-instance.com",
    username="your-username",
    password="your-password"
) as connector:
    # Session is automatically managed
    communities = connector.community.find_communities()
    assets = connector.asset.find_assets(limit=100)

# Session is automatically closed

Environment Variables Pattern

import os
from collibra_connector import CollibraConnector

connector = CollibraConnector(
    api=os.environ["COLLIBRA_URL"],
    username=os.environ["COLLIBRA_USERNAME"],
    password=os.environ["COLLIBRA_PASSWORD"]
)

API Reference

Assets

# Get an asset by ID
asset = connector.asset.get_asset("asset-uuid")

# Create an asset
new_asset = connector.asset.add_asset(
    name="My New Asset",
    domain_id="domain-uuid",
    display_name="My Asset Display Name",
    type_id="asset-type-uuid",
    status_id="status-uuid"  # Optional
)

# Update an asset
updated = connector.asset.change_asset(
    asset_id="asset-uuid",
    name="Updated Name",
    display_name="Updated Display Name"
)

# Find assets with filters
assets = connector.asset.find_assets(
    community_id="community-uuid",
    domain_id="domain-uuid",
    asset_type_ids=["type-uuid-1", "type-uuid-2"],
    limit=1000
)

# Set asset attributes
connector.asset.set_asset_attributes(
    asset_id="asset-uuid",
    type_id="attribute-type-uuid",
    values=["value1", "value2"]
)

# Set asset relations
connector.asset.set_asset_relations(
    asset_id="asset-uuid",
    related_asset_ids=["related-uuid-1", "related-uuid-2"],
    relation_direction="TO_TARGET",
    type_id="relation-type-uuid"
)

# Remove an asset
connector.asset.remove_asset("asset-uuid")

Communities

# Get a community
community = connector.community.get_community("community-uuid")

# Find communities with filters
communities = connector.community.find_communities(
    name="Search Term",
    name_match_mode="ANYWHERE",  # START, END, ANYWHERE, EXACT
    parent_id="parent-community-uuid",
    sort_field="NAME",
    sort_order="ASC",
    limit=100
)

# Create a community
new_community = connector.community.add_community(
    name="My New Community",
    description="Community description",
    parent_id="parent-community-uuid"  # Optional
)

# Update a community
connector.community.change_community(
    community_id="community-uuid",
    name="Updated Name",
    description="Updated description"
)

# Remove a community
connector.community.remove_community("community-uuid")

Domains

# Get a domain
domain = connector.domain.get_domain("domain-uuid")

# Find domains
domains = connector.domain.find_domains(
    community_id="community-uuid",
    name="Search Term",
    type_id="domain-type-uuid",
    include_sub_communities=True,
    limit=100
)

# Create a domain
new_domain = connector.domain.add_domain(
    name="My New Domain",
    community_id="community-uuid",
    type_id="domain-type-uuid",
    description="Domain description"
)

# Update a domain
connector.domain.change_domain(
    domain_id="domain-uuid",
    name="Updated Name"
)

# Remove a domain
connector.domain.remove_domain("domain-uuid")

Users

# Get user by ID
user = connector.user.get_user("user-uuid")

# Get user by username (convenience method)
user_id = connector.user.get_user_by_username("john.doe")

# Find users
users = connector.user.find_users(
    name="John",
    name_search_fields=["USERNAME", "FIRSTNAME", "LASTNAME"],
    include_disabled=False,
    sort_field="USERNAME",
    limit=50
)

# Create a user
new_user = connector.user.create_user(
    username="newuser",
    email_address="[e-mail]"
)

Workflows

# Start a workflow
workflow = connector.workflow.start_workflow(
    workflow_definition_id="workflow-def-uuid",
    asset_ids=["asset-uuid-1", "asset-uuid-2"]
)

Helper Utilities

Pagination

Efficiently iterate over large datasets:

from collibra_connector import CollibraConnector, Paginator

connector = CollibraConnector(...)

# Iterate over all assets (memory-efficient)
paginator = Paginator(
    connector.asset.find_assets,
    limit=100,          # Items per page
    max_items=10000     # Optional: stop after N items
)

# Iterate item by item
for asset in paginator.items():
    print(asset['name'])

# Or iterate page by page
for page in paginator.pages():
    print(f"Processing {len(page)} assets")
    for asset in page:
        process(asset)

# Collect all items (loads into memory)
all_assets = paginator.collect()

Batch Processing

Process bulk operations with rate limiting:

from collibra_connector import CollibraConnector, BatchProcessor

connector = CollibraConnector(...)

# Prepare data
assets_to_create = [
    {"name": "Asset 1", "domain_id": "domain-uuid"},
    {"name": "Asset 2", "domain_id": "domain-uuid"},
    # ... more assets
]

# Process in batches
processor = BatchProcessor(
    batch_size=50,      # Items per batch
    delay=0.1,          # Delay between batches (seconds)
    on_error="continue" # "continue", "stop", or "collect"
)

result = processor.process(
    items=assets_to_create,
    operation=connector.asset.add_asset,
    item_mapper=lambda a: {"name": a["name"], "domain_id": a["domain_id"]},
    progress_callback=lambda done, total: print(f"Progress: {done}/{total}")
)

print(f"Success: {result.success_count}, Errors: {result.error_count}")
print(f"Success rate: {result.success_rate:.1f}%")

Metadata Caching

Cache frequently accessed metadata to reduce API calls:

from collibra_connector import CollibraConnector, CachedMetadata

connector = CollibraConnector(...)

# Create cache with 1-hour TTL
cache = CachedMetadata(connector, ttl=3600)

# Get UUIDs by name (cached after first call)
asset_type_id = cache.get_asset_type_id("Business Term")
status_id = cache.get_status_id("Approved")
attribute_type_id = cache.get_attribute_type_id("Description")
domain_type_id = cache.get_domain_type_id("Physical Data Dictionary")
role_id = cache.get_role_id("Owner")

# Force refresh if needed
cache.refresh_all()

# Clear cache
cache.clear()

Data Transformation

Utilities for transforming API responses:

from collibra_connector import DataTransformer

# Extract IDs from results
assets = connector.asset.find_assets()
asset_ids = DataTransformer.extract_ids(assets["results"])

# Create name->ID mapping
communities = connector.community.find_communities()
name_to_id = DataTransformer.to_name_id_map(communities["results"])
community_id = name_to_id.get("My Community")

# Group items by a field
grouped = DataTransformer.group_by(assets["results"], "type.name")
for type_name, items in grouped.items():
    print(f"{type_name}: {len(items)} assets")

# Flatten nested asset structure
flat_asset = DataTransformer.flatten_asset(asset)
print(flat_asset["type.name"])  # Access nested values with dot notation

Error Handling

The library provides a custom exception hierarchy:

from collibra_connector import (
    CollibraConnector,
    CollibraAPIError,
    UnauthorizedError,
    ForbiddenError,
    NotFoundError,
    ServerError
)

connector = CollibraConnector(...)

try:
    asset = connector.asset.get_asset("invalid-uuid")
except UnauthorizedError:
    print("Invalid credentials")
except ForbiddenError:
    print("Insufficient permissions")
except NotFoundError:
    print("Asset not found")
except ServerError:
    print("Server error - try again later")
except CollibraAPIError as e:
    print(f"API error: {e}")

Retry Logic

The connector automatically retries on transient failures:

• Connection errors: Network issues, DNS failures
• Timeouts: Request timeouts
• Server errors: 500, 502, 503, 504 status codes
• Rate limiting: 429 status code

Configure retry behavior:

connector = CollibraConnector(
    api="https://your-instance.com",
    username="user",
    password="pass",
    max_retries=5,      # Number of retry attempts
    retry_delay=2.0     # Base delay (uses exponential backoff)
)

Configuration

Timeouts

# Set custom timeout (seconds)
connector = CollibraConnector(
    api="...",
    username="...",
    password="...",
    timeout=60  # 60 seconds
)

Auto-load UUIDs

Load all metadata UUIDs on initialization:

connector = CollibraConnector(
    api="...",
    username="...",
    password="...",
    uuids=True  # Fetch all UUIDs on init
)

# Access cached UUIDs
asset_type_id = connector.uuids["AssetType"]["Business Term"]
status_id = connector.uuids["Status"]["Approved"]

Requirements

• Python 3.8+
• requests >= 2.20.0

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest tests/ -v

Run type checking:

mypy collibra_connector/

License

MIT License - see LICENSE for details.

Disclaimer

This is an UNOFFICIAL connector and is not affiliated with, endorsed by, or supported by Collibra. Use at your own risk.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.