DatriseAI-first ETL

DealerSocket to Looker Studio Integration

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

How Datrise loads DealerSocket into Looker Studio

Datrise syncs DealerSocket's leads, customers, vehicles, deals, showroom visits, and trade-ins into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

DealerSocket: Automotive dealer CRM for showroom and desking workflows.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How DealerSocket entities map to Looker Studio

DealerSocket entityLooker Studio objectNotes
leadsdealersocket_leadsid PK · custom fields → flattened columns for chart fields
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 Studio?

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

How does the DealerSocket to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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