kwp-data-data-context-extractor

Data warehouse schema discovery is automated by extracting domain knowledge from analysts to generate structured reference files. This process explores database schemas and defines key business entities to document table relationships and KPI formulas.

Documenting SQL metrics for AI agents involves generating tailored data analysis skills and standardized reference files. This captures company-specific KPI formulas and entity definitions to ensure consistent data interpretation across various SQL dialects.

Onboarding an AI agent to company-specific data knowledge uses Bootstrap Mode to rapidly initialize a new data analysis profile. It explores primary sales tables to discover schemas and define business logic for your warehouse.

Integration with data analysis tools is required to perform schema exploration and generate structured reference files. This integration allows the context extractor to discover database schemas and extract entity definitions across various SQL dialects.

You can incrementally add new domain contexts to existing data analysis skills using Iteration Mode. This mode improves existing skills by adding metric definitions, table relationships, and new domain contexts.

The data context extractor supports schema discovery across various SQL dialects. It facilitates the discovery of database schemas, entity definitions, and KPI formulas to generate tailored data analysis skills and documentation.