carto-import-export-data

name	carto-import-export-data
description	Import geospatial files into the data warehouse via CARTO, export results back out, and prepare tilesets for fast map rendering.
license	MIT

carto-import-export-data

Move data into the warehouse from local files / URLs (carto import), pull data out (carto activity export for usage data; warehouse-native unloads for everything else), and prepare tilesets for performant map rendering of large geospatial datasets.

When to use this skill

• The user has a CSV / GeoJSON / Shapefile / GeoParquet file and wants it queryable in the warehouse.
• The user wants to refresh an existing table from a remote URL.
• The user wants to render a 10M+-row spatial dataset on a map (needs tileset preparation).
• The user is bulk-exporting CARTO activity data to disk for offline analysis.

If the user just wants to query a file already in the warehouse, jump to carto-query-datawarehouse. If they want to discover what's already there, carto-explore-datawarehouse.

Quick reference

# Import a local file
carto import --file ./data.csv \
  --connection carto_dw \
  --destination project.dataset.table

# Import from a URL
carto import --url https://example.com/data.geojson \
  --connection carto_dw \
  --destination my_project.demo.regions

# Async (return immediately, poll separately)
carto import --file ./big.parquet \
  --connection carto_dw \
  --destination my_project.demo.huge \
  --async

# Overwrite existing table
carto import --file ./data.csv \
  --connection carto_dw \
  --destination my_project.demo.t \
  --overwrite

What's in this skill

Topic	Reference
carto import — flags, formats, size limits, async	references/imports.md
Tileset preparation for large maps	references/tilesets.md
Exporting data: warehouse-native unloads vs activity export	references/exports.md

Always-on guidance

• --connection is the connection name (from connections list), not the warehouse project ID. If you only know the project, run carto connections list --json first to find the matching connection.
• --destination is the fully-qualified target name in the warehouse's syntax: project.dataset.table (BigQuery), DATABASE.SCHEMA.TABLE (Snowflake), schema.table (Postgres/Redshift), catalog.schema.table (Databricks), SCHEMA.TABLE (Oracle).
• 1GB hard limit per file. For larger files, split or pre-stage to cloud storage and use --url to a presigned URL.
• --no-autoguessing skips column type detection — use it when you've prepared a precise schema and don't want CARTO to second-guess types (especially for columns that look numeric but should stay string, like ZIP codes).
• Imports are async at the API level. The CLI defaults to polling-to-completion; pass --async to return immediately. The CLI prints a job ID in async mode that you can use to check progress.
• For tilesets, the workflow is import → SQL job to materialize a tileset table → reference the tileset in a map. The tileset itself is created in the warehouse, not by the CLI.