BigQuery

Overview#

The BigQuery connector proxies Google BigQuery REST API v2 calls. Service account JSON credentials are sealed in QuantaVault. Supports query jobs, table inserts, and dataset operations.

Prerequisites#

• 1A GCP project with BigQuery API enabled
• 2A service account with BigQuery Data Viewer + Job User roles
• 3Service account JSON key file

Configuration#

Follow these steps to connect BigQuery to QuantaSeal. You can configure integrations via the Admin Console or directly via the API.

1. 1

   Create a service account with BigQuery Data Editor + Job User roles.

2. 2

   Download the JSON key file.

3. 3

   Seal: POST /api/v2/vault/seal with credential_type: custom_kv.

4. 4

   Create integration with system_type: bigquery, config: {project_id, dataset_id}.

Authentication Types#

Seal the full service account JSON as custom_kv with key 'service_account_json'.

All credential types are sealed in QuantaVault with ML-KEM-768 + AES-256-GCM and wrapped by your tenant AWS KMS CMK before storage. See the Vault API reference for the full list of credential types and seal/unseal endpoints.

Available Operations#

QuantaSeal enforces a default-deny operation policy. Only operations listed in your integration's allowed_operations array will be permitted. Add operations when creating or updating the integration.

Code Example#

Every proxy call returns a HybridCryptoEnvelope - the response is ML-KEM-768 key-encapsulated, AES-256-GCM encrypted, and signed with ML-DSA-65 + HMAC-SHA-512. Verify both signatures before trusting the decrypted payload.

curl -X POST https://api.quantaseal.io/api/v2/proxy/outbound \
  -H "X-API-Key: qs_live_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"integration_id":"int_BQ001","operation":"query","payload":{"sql":"SELECT customer_id, SUM(amount) as total FROM orders.transactions GROUP BY 1 LIMIT 100"}}'

Troubleshooting#