What is Data Catalog
Data Catalog - Organized inventory of available data assets
A metadata management system that provides a searchable inventory of all data assets in an organization, including descriptions, schemas, ownership, and quality metrics. Data catalogs help teams discover and understand multimodal datasets across the organization.
How It Works
A data catalog collects metadata about data assets from across the organization, including databases, data lakes, APIs, and file systems. It indexes technical metadata (schemas, types, statistics), business metadata (descriptions, tags, owners), and operational metadata (freshness, quality, usage). Users search and browse the catalog to find relevant datasets, understand their structure, and assess their fitness for use.
Technical Details
Open-source catalogs include DataHub, OpenMetadata, and Amundsen. Commercial options include Alation and Collibra. Catalogs integrate with data sources via crawlers or push-based metadata ingestion. Features include automated schema extraction, data profiling, lineage visualization, access control, and collaboration (comments, ratings). Search uses both keyword and metadata-based filtering.
Best Practices
- Automate metadata extraction from data sources rather than relying on manual documentation
- Assign data owners responsible for maintaining metadata accuracy and quality
- Tag datasets with consistent business terminology for cross-team discoverability
- Include data quality scores and freshness indicators alongside dataset descriptions
Common Pitfalls
- Building a catalog that becomes stale because metadata is not automatically refreshed
- Not enforcing data ownership, resulting in orphaned datasets with no maintainer
- Making the catalog too complex for everyday use, reducing adoption
- Cataloging only structured data while ignoring multimodal assets like images, videos, and audio files
Advanced Tips
- Catalog multimodal datasets with modality-specific metadata (resolution, duration, frame rate)
- Use catalog metadata to power data-aware features in AI applications (automated data selection)
- Integrate the catalog with lineage tracking for end-to-end data understanding
- Build semantic search over the catalog using embeddings for natural language data discovery