DatriseAI-first ETL

QuickBooks Looker

AI-first ETL from QuickBooks into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads QuickBooks into Looker

Datrise syncs QuickBooks's customers, invoices, bills, payments, and chart-of-accounts entries into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

QuickBooks: SMB accounting for invoices, expenses, and ledger activity.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How QuickBooks entities map to Looker

QuickBooks entityLooker objectNotes
customersquickbooks_customersid PK · custom fields → flattened columns (nested fields expanded for modeling)
invoicesquickbooks_invoicesid PK · linked to quickbooks_customers
billsquickbooks_billsid PK · linked to quickbooks_customers
paymentsquickbooks_paymentsid PK · linked to quickbooks_customers

FAQ

How does Datrise handle QuickBooks's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the QuickBooks to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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