# Data Catalog

> Centralized metadata repository for discovering, understanding, and governing data assets.

- **Category**: Data Science
- **Subcategory**: Governance
- **Canonical URL**: https://designpattern.fyi/patterns/data-catalog/

---

## Description
**Context**: As data ecosystems grow, discovering and understanding data assets becomes challenging. Data catalogs provide searchable metadata, lineage, and documentation.


## Use Cases
Organizations with complex data landscapes where users need to discover, understand, and trust data assets across the organization.



## Implementation Example

```python
# 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"] })
```



## Trade-offs


### Advantages

- - Improved data discovery

- - Better data understanding

- - Enhanced data governance

- - Reduced data silos




### Considerations & Drawbacks

- - Maintenance overhead

- - Adoption challenges

- - Requires consistent metadata practices

- - Integration complexity