Data warehouses

Connecting a warehouse works the same way as any other universal API integration. You run a scheduled job on your side that queries the conversation rows that are new since its last run and posts each message to Dimension Labs.

How it works

1. Get access. Give the job its own read-only service user, role or service principal.
2. Query new rows. Select the rows whose updated_at is at or after a stored cursor, with the warehouse’s official Node.js driver or SQL API. A row that is updated later is sent again; that’s fine, because duplicates are removed automatically.
3. Map the columns. Each guide assumes a table with one row per message, and maps its columns to the tracker fields. Use your internal customer id for userId, never an email address or phone number.
4. Send it. Post each message with sendToDimensionLabs from dimensionlabs.mjs.
5. Run it. Load history once with node run.mjs and a start date, then run the job every 15 minutes, as in Scheduled sync. History goes to /trackhistorical and needs a backfill; new rows go to /track and are enriched automatically every day.

To bring Dimensions back into your warehouse, each guide ends with a section that pulls them with the Export API (with an Export API key from Settings) and loads the files into a table.

Guides

Warehouse	Driver or API used	Guide
Snowflake	snowflake-sdk, key pair authentication	Snowflake
Google BigQuery	@google-cloud/bigquery, service account	Google BigQuery
Databricks	@databricks/sql, OAuth service principal	Databricks
Amazon Redshift	Redshift Data API, IAM	Amazon Redshift

If you would rather not run a job, export the table to a CSV, TSV or JSONL file and upload it. Run it through the file checker first.