DatriseAI-first ETL

PipeRun Looker Studio

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

How Datrise loads PipeRun into Looker Studio

Datrise syncs PipeRun'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

PipeRun: CRM widely used in Latin America for sales pipeline and customer ops.

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

How PipeRun entities map to Looker Studio

PipeRun entityLooker Studio objectNotes
contactspiperun_contactsid PK · custom fields → flattened columns for chart fields
accountspiperun_accountsid PK · linked to piperun_contacts
dealspiperun_dealsid PK · linked to piperun_contacts
activitiespiperun_activitiesdate dimension columns events

FAQ

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

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

Related pipelines

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