sql-query

To execute raw SQL safely against dataset aliases, use the SqlClient wrapper to run queries on manifest-mapped datasets. The skill processes the queries and transforms the columnar API responses into structured row objects for downstream consumption.

Parsing columnar SQL query responses into row objects involves converting the column and row arrays returned by the SqlClient wrapper into an array of structured row objects. This transformation makes the response data usable for downstream analytics and app integrations.

Yes, you can combine AI-generated SQL with the SqlClient wrapper to automate data workflows. This integration allows you to generate SQL queries dynamically and execute them against manifest-mapped datasets to retrieve structured results automatically.

The best way to run SQL on mapped datasets for app integrations is to execute queries via the SqlClient wrapper against manifest-mapped dataset aliases. The skill safely parses the columnar results into structured objects ready for immediate application consumption.

Yes, you need a manifest of mapped datasets to execute SQL queries. The SqlClient wrapper relies on these manifest-mapped dataset aliases to target the correct data sources and safely return the columnar response data for parsing.

SQL query results are returned in a columnar format of columns and rows by the underlying SqlClient wrapper. You must convert this raw columnar response into an array of row objects to make the data usable for downstream analytics and app integrations.