carto-explore-datawarehouse

name	carto-explore-datawarehouse
description	Discover what's in the connected warehouse — schemas, tables, columns, and CARTO named sources.
license	MIT

carto-explore-datawarehouse

Before writing SQL or building maps, an agent typically needs to know what's in the warehouse. This skill covers two CARTO surfaces for that:

• carto connections browse — walk the warehouse hierarchy (project → dataset → table).
• carto connections describe — inspect a specific table's columns and types.

And one CARTO-specific concept:

• Named sources — saved, parameterized SQL that maps and apps consume as if they were tables.

When to use this skill

• You don't know which tables / schemas exist in a connection.
• You need a column list and types before writing SQL or authoring a map.
• The user references "the named source for X" and you need to find it.

If you already know the table and just want to query it, jump straight to carto-query-datawarehouse.

Quick reference

# What connections are registered?
carto connections list --json

# Walk the hierarchy (no path = top level)
carto connections browse <connection-name>

# Drill in
carto connections browse <connection-name> "carto-demo-data"
carto connections browse <connection-name> "carto-demo-data.demo_tables"

# Get columns + types for a specific table
carto connections describe <connection-name> "carto-demo-data.demo_tables.nyc_collisions"

The exact path syntax depends on the engine:

Engine	browse path shape
BigQuery	project.dataset.table
Snowflake	DATABASE.SCHEMA.TABLE
Postgres / Redshift	schema.table (no leading project/database)
Databricks	catalog.schema.table

What's in this skill

Topic	Reference
connections browse and connections describe in detail	references/connection-browse.md
Named sources — what they are, how to list and inspect them	references/named-sources.md

Always-on guidance

• Browse before you query. A two-second connections browse usually saves a five-minute "table not found" loop.
• Use --page-size when a dataset has hundreds of tables; the default is 30.
• describe returns column types — use those types to write correct SQL (e.g. don't ST_DWithin against a STRING column the user mistakenly named geom).
• Named sources ≠ tables. They're parameterized queries. Inspect the underlying tables before assuming a column you see in the source exists in raw form.
• carto-demo-data is a public BigQuery dataset CARTO ships — carto connections browse <bq-connection> "carto-demo-data" works on any BigQuery connection that has the right IAM, and is a fast way to validate a fresh connection without touching customer data.