Connect BigQuery to Uplyst
BigQuery SQL results can enrich Uplyst business entities with analytical measures, reporting history and calculated values. Customer and transaction identifiers connect those results to records held in CRM, ERP and operational systems.
BigQuery provides REST APIs and client libraries for running GoogleSQL queries. Approved datasets or views, selected columns and reporting periods define the information used by the connection. The job project and data project can have different permission and cost ownership.
Receivables query
24 September 2026
Job context
- Job project
finance-analysis- Job ID
receivables_20260924_0800- Processing location
- EU
- Completed
Source context
- Data project
enterprise-data- Approved view
finance.receivables- Reporting period
- September 2026
- Records through
Returned invoice record
- Invoice
INV-88- Currency
- GBP
- Invoice principal
- 36,000.00
- Open receivable
- 14,000.00
Analysis that supports daily decisions
Finance teams can connect receivables or revenue analysis to invoices, receipts and customer accounts. Operations teams can relate historical fulfilment measures to current orders and service commitments.
Late records and corrections can change previous reporting periods. Refresh and reconciliation schedules keep those changes visible alongside current activity.
Preserve the meaning of analytical results
BigQuery's nested and repeated fields can describe line items and other related records. The mapping connects those structures to their parent business entities while preserving exact numeric values, currencies and timestamps.
Dataset location determines where query processing can take place. The query reference and reporting period remain associated with the result so teams can trace a measure to its calculation.
| BigQuery data | Business context |
|---|---|
| Repeated order line items | Products and quantities within an order |
| Financial totals | Currency and reporting period |
| Customer identifiers | Links to CRM and service activity |
| Query job reference | Calculation origin |
Google Cloud identity and access
A workload identity needs permission to run query jobs and read the approved data. BigQuery access controls operate at project, dataset and table levels, with column controls and row access policies available for more specific restrictions.
The hosting environment determines the appropriate authentication method. BigQuery client libraries support Application Default Credentials, allowing the connection to use the environment's configured identity.
Query execution and cost
Selected columns and time filters keep recurring queries focused on the data required by the business process. Processing estimates and workload cost controls help align refresh frequency with the BigQuery billing model.
Query jobs can continue after a client request times out. Checking the existing job's status before resubmitting avoids unnecessary repeat work and makes interrupted processing easier to recover.