To integrate BigQuery with Abacus.AI, you need to set up the connector and provide the necessary permissions.
Gather Information:
PROJECT ID: You can find the project ID by following the instructions at Google API Support. The project ID is a unique identifier for your Google Cloud project.
DATASET ID (OPTIONAL): To get the Dataset ID, browse the dataset in the BigQuery console or follow the instructions at BigQuery Dataset Metadata. The Dataset ID is the identifier for the specific dataset you want to connect to.
Note: The Dataset ID in the UI is pre-pended with the Project ID, i.e., Project ID: Dataset ID. Capture the Dataset ID by taking the string after the ":".
Important: If the Dataset ID is not set, tables must be referenced along with the dataset name, e.g., dataset_name.table_name
.
Access Abacus.AI Connected Services Dashboard:
Enter Connector Information:
Save
Set BigQuery Data Viewer Permissions:
Configure Permissions in BigQuery:
Verify Connector Setup:
Allowing write access back to BigQuery is always optional. If you choose to enable this, follow the instructions below based on whether you provided a dataset ID or not.
Navigate to Dataset:
Share Dataset:
Add Roles:
Navigate to IAM:
Add Service Account:
Add Roles:
Once the BigQuery connector is set up, you can fetch data to train models in Abacus.AI.
Create a New Project:
Create New Dataset:
Name the Dataset:
Read from External Service:
Enter Dataset Details:
Configure Schema Mapping:
There are two suggested solutions:
Solution 1: Grant the service account access to the specific tables these views reference. Provide the "BigQuery Data Viewer" role to the specific underlying tables for the service account.
Solution 2: Create an Authorized View (https://cloud.google.com/bigquery/docs/authorized-views) which will grant the view access to the underlying data, allowing Abacus.AI to query from the view without having to grant permission to the table(s) which make up the view.