DatriseAI-first ETL

DealerSocket to Looker Integration

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

How Datrise loads DealerSocket into Looker

Datrise syncs DealerSocket's leads, customers, vehicles, deals, showroom visits, and trade-ins 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

DealerSocket: Automotive dealer CRM for showroom and desking workflows.

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

How DealerSocket entities map to Looker

DealerSocket entityLooker objectNotes
leadsdealersocket_leadsid PK · custom fields → flattened columns (nested fields expanded for modeling)
customersdealersocket_customersid PK · linked to dealersocket_leads
vehiclesdealersocket_vehiclesid PK · linked to dealersocket_leads
dealsdealersocket_dealsid PK · linked to dealersocket_leads

FAQ

How does Datrise handle DealerSocket'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 DealerSocket to Looker sync stay up to date?

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

Related pipelines

Early access

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