DatriseAI-first ETL

Propertybase to Looker Integration

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

How Datrise loads Propertybase into Looker

Datrise syncs Propertybase's leads, contacts, listings, opportunities, showings, and brokerage offices 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

Propertybase: Salesforce-based real estate CRM for brokerages.

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

How Propertybase entities map to Looker

Propertybase entityLooker objectNotes
leadspropertybase_leadsid PK · custom fields → flattened columns (nested fields expanded for modeling)
contactspropertybase_contactsid PK · linked to propertybase_leads
listingspropertybase_listingsid PK · linked to propertybase_leads
opportunitiespropertybase_opportunitiesid PK · linked to propertybase_leads

FAQ

How does Datrise handle Propertybase'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 Propertybase 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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