DatriseAI-first ETL

Marketo Bulk DuckDB

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

How Datrise loads Marketo Bulk into DuckDB

Datrise syncs Marketo Bulk's records, events, and configuration objects into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Marketo Bulk: SaaS or API data source for analytics and warehouse sync.

DuckDB: In-process analytics database for fast local OLAP.

How Marketo Bulk entities map to DuckDB

Marketo Bulk entityDuckDB objectNotes
recordsmarketo_bulk_recordsid PK · custom fields → JSON or STRUCT columns
eventsmarketo_bulk_eventsTIMESTAMP WITH TIME ZONE events
configuration objectsmarketo_bulk_configuration_objectsid PK · linked to marketo_bulk_records

FAQ

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

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

How does the Marketo Bulk to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect Marketo Bulk to DuckDB the easy way

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