DatriseAI-first ETL

Marketo Bulk to Looker Integration

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

How Datrise loads Marketo Bulk into Looker

Datrise syncs Marketo Bulk's leads, activities, program memberships, email events, lists, and custom objects 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

Marketo Bulk: Bulk export of Marketo lead and activity history.

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

How Marketo Bulk entities map to Looker

Marketo Bulk entityLooker objectNotes
leadsmarketo_bulk_leadsid PK · custom fields → flattened columns (nested fields expanded for modeling)
activitiesmarketo_bulk_activitiesdate/time dimension columns events
program membershipsmarketo_bulk_program_membershipsid PK · linked to marketo_bulk_leads
email eventsmarketo_bulk_email_eventsdate/time dimension columns events

FAQ

How does Datrise handle Marketo Bulk'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 Marketo Bulk to Looker sync stay up to date?

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

Related pipelines

Early access

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