DatriseAI-first ETL

Pipedrive Looker Studio

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

How Datrise loads Pipedrive into Looker Studio

Datrise syncs Pipedrive's deals, persons, organizations, activities, and stage movement analytics 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

Pipedrive: Pipeline-first CRM for sales teams.

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

How Pipedrive entities map to Looker Studio

Pipedrive entityLooker Studio objectNotes
dealspipedrive_dealsid PK · custom fields → flattened columns for chart fields
personspipedrive_personsid PK · linked to pipedrive_deals
organizationspipedrive_organizationsid PK · linked to pipedrive_deals
activitiespipedrive_activitiesdate dimension columns events

FAQ

How does Datrise handle Pipedrive'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 Pipedrive 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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