DatriseAI-first ETL

Plausible to Looker Integration

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

How Datrise loads Plausible into Looker

Datrise syncs Plausible's pageviews, visitors, sessions, referral sources, goals, and page paths into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Plausible: Privacy-friendly website traffic analytics.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Plausible entities map to Looker

Plausible entityLooker objectNotes
pageviewsplausible_pageviewsid PK · custom fields → flattened columns (nested fields expanded for modeling)
visitorsplausible_visitorsid PK · linked to plausible_pageviews
sessionsplausible_sessionsid PK · linked to plausible_pageviews
referral sourcesplausible_referral_sourcesid PK · linked to plausible_pageviews

FAQ

How does Datrise handle Plausible's custom fields in Looker?

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

How does the Plausible to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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