Google BigQuery
Overview
Integrate with BigQuery using the CData connector. We utilise the official CData Python (opens in a new tab) connector to ingest your data.
Features
| Feature Name | Supported |
|---|---|
| Full Import | Yes |
| Incremental Import | No |
Getting started
Requirements and prerequisites
Access to BigQuery project and dataset.
Set up guide
Create a source
Create a new source, enter a Name, select "BigQuery (CData)" from the connector type list, and then click Create.
Configure a secret
Create a new secret (or reuse an existing one), and provide two values:
- Project ID (opens in a new tab) - ID for your Google Cloud Project.
- Dataset ID (opens in a new tab) - ID you selected while creating a dataset.
Authorise with credentials
Y42 supports two types of authorisation: OAuth and service accounts. We highly recommend using service accounts, as it is more secure and easier to fine-grain access to BigQuery data using service accounts.
OAuthJWT - Service account JSON key (opens in a new tab) in the following format:
{ "type": "service_account", "project_id": "PROJECT_ID", "private_key_id": "KEY_ID", "private_key": "-----BEGIN PRIVATE KEY-----\nPRIVATE_KEY\n-----END PRIVATE KEY-----\n", "client_email": "SERVICE_ACCOUNT_EMAIL", "client_id": "CLIENT_ID", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://accounts.google.com/o/oauth2/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/SERVICE_ACCOUNT_EMAIL"}
Schema configuration
Configure the schema by selecting desired columns and tables.