DatriseAI-first ETL

PipeRun to Looker Studio Integration

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 deals, contacts, companies, pipeline stages, activities, and sales teams 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: Sales pipeline management for Brazilian revenue teams.

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

How PipeRun entities map to Looker Studio

PipeRun entityLooker Studio objectNotes
dealspiperun_dealsid PK · custom fields → flattened columns for chart fields
contactspiperun_contactsid PK · linked to piperun_deals
companiespiperun_companiesid PK · linked to piperun_deals
pipeline stagespiperun_pipeline_stagesid PK · linked to piperun_deals

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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