Build an AI Context Layer
1. Run the import
From the project root (the folder containing .bruin.yml):
bruin import database --connection duckdb-default --schema ecommerce ai-analyst
Replace duckdb-default with your connection name, ecommerce with your schema, and keep ai-analyst as the pipeline folder path.
Imported 3 tables and Merged 0 from data warehouse 'duckdb' (schema: ecommerce) into pipeline 'ai-analyst'
Merged counts tables that already had an asset file, so re-running the import after a schema change updates existing files instead of duplicating them.
The last argument is the pipeline folder - the one containing
pipeline.yml- not the project root where.bruin.ymllives. Pointing it at the project root is the single most common error here.
Two notes on scope. --schema takes one schema, which is what you want for a first pass; on BigQuery you can repeat --schemas raw --schemas analytics to do several at once. Supported sources are Snowflake, BigQuery, PostgreSQL, Redshift, Athena, Databricks, DuckDB, ClickHouse, Azure Synapse, MS SQL Server, and MongoDB. The import reference has the rest of the flags.
Leaving --connection off launches an interactive picker instead, which is handy if you have several connections and cannot remember the name.