DatriseAI-first ETL

Eloqua to DuckDB Integration

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

How Datrise loads Eloqua into DuckDB

Datrise syncs Eloqua's contacts, accounts, campaigns, email events, forms, and lead scores 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

Eloqua: B2B marketing automation and lead nurturing.

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

How Eloqua entities map to DuckDB

Eloqua entityDuckDB objectNotes
contactseloqua_contactsid PK · custom fields → JSON or STRUCT columns
accountseloqua_accountsid PK · linked to eloqua_contacts
campaignseloqua_campaignsid PK · linked to eloqua_contacts
email eventseloqua_email_eventsTIMESTAMP WITH TIME ZONE events

FAQ

How does Datrise handle Eloqua'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 Eloqua 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

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