DatriseAI-first ETL

Autopilot to Looker Integration

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

How Datrise loads Autopilot into Looker

Datrise syncs Autopilot's contacts, journeys, lists, email events, triggers, and custom fields 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

Autopilot: Visual marketing automation and customer journeys.

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

How Autopilot entities map to Looker

Autopilot entityLooker objectNotes
contactsautopilot_contactsid PK · custom fields → flattened columns (nested fields expanded for modeling)
journeysautopilot_journeysid PK · linked to autopilot_contacts
listsautopilot_listsid PK · linked to autopilot_contacts
email eventsautopilot_email_eventsdate/time dimension columns events

FAQ

How does Datrise handle Autopilot'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 Autopilot 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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