Marketo Bulk to Google BigQuery Integration

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

How Datrise loads Marketo Bulk into Google BigQuery

Datrise syncs Marketo Bulk's leads, activities, program memberships, email events, lists, and custom objects into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Marketo Bulk entities map to Google BigQuery

Marketo Bulk entity	Google BigQuery object	Notes
leads	`marketo_bulk_leads`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
activities	`marketo_bulk_activities`	TIMESTAMP events
program memberships	`marketo_bulk_program_memberships`	id PK · linked to marketo_bulk_leads
email events	`marketo_bulk_email_events`	TIMESTAMP events

FAQ

How does Datrise handle Marketo Bulk's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Marketo Bulk to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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