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Data Catalog
Overview
The Data Catalog service provides a centralized system for data asset discovery, management, and governance within the stock-bot platform. It serves as the single source of truth for all data assets, their metadata, and relationships, enabling efficient data discovery and utilization across the platform.
Key Features
Data Asset Management
- Asset Registration: Automated and manual registration of data assets
- Metadata Management: Comprehensive metadata for all data assets
- Versioning: Tracks changes to data assets over time
- Schema Registry: Central repository of data schemas and formats
Data Discovery
- Search Capabilities: Advanced search across all data assets
- Categorization: Hierarchical categorization of data assets
- Tagging: Flexible tagging system for improved findability
- Popularity Tracking: Identifies most-used data assets
Data Governance
- Access Control: Fine-grained access control for data assets
- Lineage Tracking: Visualizes data origins and transformations
- Quality Metrics: Monitors and reports on data quality
- Compliance Tracking: Ensures regulatory compliance for sensitive data
Integration Framework
- API-first Design: Comprehensive API for programmatic access
- Event Notifications: Real-time notifications for data changes
- Bulk Operations: Efficient handling of batch operations
- Extensibility: Plugin architecture for custom extensions
Integration Points
Upstream Connections
- Data Processor (for processed data assets)
- Feature Store (for feature metadata)
- Market Data Gateway (for market data assets)
Downstream Consumers
- Strategy Development Environment
- Data Analysis Tools
- Machine Learning Pipeline
- Reporting Systems
Technical Implementation
Technology Stack
- Runtime: Node.js with TypeScript
- Database: Document database for flexible metadata storage
- Search: Elasticsearch for advanced search capabilities
- API: GraphQL for flexible querying
- UI: React-based web interface
Architecture Pattern
- Domain-driven design for complex metadata management
- Microservice architecture for scalability
- Event sourcing for change tracking
- CQRS for optimized read/write operations
Development Guidelines
Metadata Standards
- Adherence to common metadata standards
- Required vs. optional metadata fields
- Validation rules for metadata quality
- Consistent naming conventions
Extension Development
- Plugin architecture documentation
- Custom metadata field guidelines
- Integration hook documentation
- Testing requirements for extensions
Performance Considerations
- Indexing strategies for efficient search
- Caching recommendations
- Bulk operation best practices
- Query optimization techniques
Future Enhancements
- Automated metadata extraction
- Machine learning for data classification
- Advanced lineage visualization
- Enhanced data quality scoring
- Collaborative annotations and discussions
- Integration with external data marketplaces