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Data Science
Governance
Data Catalog
Centralized metadata repository for discovering, understanding, and governing data assets.
Intent & Description
📋 Context
As data ecosystems grow, discovering and understanding data assets becomes challenging. Data catalogs provide searchable metadata, lineage, and documentation.
Real-world Use Case
Organizations with complex data landscapes where users need to discover, understand, and trust data assets across the organization.
Advantages
- Improved data discovery
- Better data understanding
- Enhanced data governance
- Reduced data silos
Disadvantages
- Maintenance overhead
- Adoption challenges
- Requires consistent metadata practices
- Integration complexity
Implementation Example
# Data Catalog Pattern class DataCatalog: def __init__(self): self.assets = {}
def register_asset(self, asset_id, metadata): self.assets[asset_id] = metadata
def search(self, query): return [asset for asset in self.assets.values() if query.lower() in asset["description"].lower()]
catalog = DataCatalog() catalog.register_asset("users_table", { "description": "User demographic data", "owner": "data_team", "schema": ["user_id", "name", "email"] })