answering-natural-language-questions-with-dbt

This Skill answers natural language questions by translating them into direct queries for metrics and dimensions defined in dbt's semantic layer, executing them against your data warehouse to retrieve precise analytical results without manual SQL.

Yes, you can generate and execute ad-hoc SQL queries against dbt models. When the semantic layer is insufficient for data analysis, this Skill writes and runs custom SQL directly against your data warehouse to retrieve the required business intelligence.

Yes, you need the dbt CLI installed to query metrics from a data warehouse. This Skill requires the dbt CLI and dbt Semantic Layer tools to discover dbt models, understand data structure, and execute queries for comprehensive data retrieval.

dbt model discovery works for business intelligence analysis by exploring the dbt manifest and catalog to understand your data structure, allowing the Skill to identify relevant tables and write accurate ad-hoc SQL queries for comprehensive data warehouse retrieval.

The best way to get total sales metrics from last quarter using dbt is to query the semantic layer for a defined total sales metric. If the metric is not defined, this Skill generates and executes ad-hoc SQL against relevant dbt models to provide the answer.

A limitation of using a semantic layer for data warehouse queries is that it only returns pre-defined metrics and dimensions. When you need custom aggregations or unmapped data, this Skill falls back to generating and executing ad-hoc SQL against dbt models.