DatriseAI-first ETL

kvCORE Looker Studio

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

How Datrise loads kvCORE into Looker Studio

Datrise syncs kvCORE's contacts, accounts, deals, activities, and lifecycle events 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

kvCORE: Real estate CRM for leads, listings, and agent follow-up.

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

How kvCORE entities map to Looker Studio

kvCORE entityLooker Studio objectNotes
contactskvcore_contactsid PK · custom fields → flattened columns for chart fields
accountskvcore_accountsid PK · linked to kvcore_contacts
dealskvcore_dealsid PK · linked to kvcore_contacts
activitieskvcore_activitiesdate dimension columns events

FAQ

How does Datrise handle kvCORE'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 kvCORE 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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