Raw data only. Northbeam lands as warehouse tables. It is not modeled in dashboards or Uni yet, and it does not replace the Northbeam app.
Vendor documentation
SourceMedium’s warehouse path is GCS, then load to BigQuery. We do not use Northbeam’s S3 destination or MCP server for this connector.
Foundation: request integration
Foundation delivers a shared project, so you do not have project-level Cloud Storage or BigQuery access and cannot grant Northbeam’s exporter on a bucket yourself.- In SourceMedium, open Connect a data source and find Northbeam.
- Click Request integration.
- SourceMedium is notified. The card shows Setting up until the team completes it.
Pro: self-serve export, then load into BigQuery
Pro delivers a dedicated Google Cloud project you administer. You create a bucket, grant Northbeam, point the export at it, then load the CSVs into BigQuery. Connect shows the project ID.What you need
- An active Northbeam account
- Access to your dedicated GCP project (Connect shows the project ID)
- For Data Export API jobs: a Northbeam API key and Data-Client-ID (Authentication)
1. Create a bucket and grant Northbeam
1
Create a GCS bucket in your dedicated project
In Google Cloud Storage, create a bucket in the same project as your BigQuery datasets. Use a name you will pass to Northbeam (for example
your-brand-northbeam-export).2
Grant Northbeam write access
In the bucket IAM, add
nb-data-export@north-beam-io.iam.gserviceaccount.com with Storage Object Admin. This is the address Northbeam documents for both the Data Export API GCS destination and the Sales page GCS export.3
Point Northbeam at the bucket
Pipeline (typical): use the Data Export API. Pass
bucket_name on the create request for Sales Attribution and/or Orders. Poll GET /v1/exports/data-export/result/{export_id} until status is SUCCESS. Files are CSV. A 200 OK on create does not mean the file is ready.Dashboard: on the Sales page, Export → Table Values → Google Cloud Storage, enter the bucket, click Validate. Chart Values cannot go to GCS.2. Hook GCS into BigQuery
Northbeam does not write BigQuery tables. After files land in GCS, load them with Google’s Cloud Storage CSV load.1
Confirm files arrived
In Cloud Storage, open the bucket and confirm a CSV (or a prefix of CSVs) from the Northbeam export. Note the URI, for example
gs://your-brand-northbeam-export/*.csv.2
Create a dataset (once)
In BigQuery, create a dataset for this source (for example
northbeam_export) in your dedicated project.3
Load the CSV into a table
In BigQuery: Create table → source Google Cloud Storage → paste the
gs:// URI → file format CSV. Use autodetect for a first load, or supply a schema once you know the columns. Google’s load guide covers options (skip header rows, quoted newlines, hive partitioning).From the bq CLI:4
Schedule the load if the export is recurring
A one-off load is enough to confirm the path. For a scheduled Northbeam export, add a matching BigQuery load (scheduled query that runs
LOAD DATA, or Cloud Storage transfers). SourceMedium can confirm tables are arriving once the first load succeeds.5
Confirm in SourceMedium
Click Connect on Northbeam in SourceMedium so we can mark the source as in setup.
What data syncs
Depends on the report:
Grain and columns follow the export you create. First release is those files loaded as warehouse tables.
Notes
- Northbeam stores exports in its own GCS by default. This connector uses an external bucket in your dedicated project.
- Format today is CSV. Result download links expire; re-fetch the result if a link dies.
- Attribution model, window, and accounting mode change the numbers. Set them on the export to match how you read Northbeam in the product.
- First release is tables you query, not a dashboard replacement.