DatriseAI-first ETL

Eloqua to Databricks SQL Warehouse Integration

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

How Datrise loads Eloqua into Databricks SQL Warehouse

Datrise syncs Eloqua's contacts, accounts, campaigns, email events, forms, and lead scores into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Eloqua: B2B marketing automation and lead nurturing.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Eloqua entities map to Databricks SQL Warehouse

Eloqua entityDatabricks SQL Warehouse objectNotes
contactseloqua_contactsid PK · custom fields → VARIANT or STRUCT columns
accountseloqua_accountsid PK · linked to eloqua_contacts
campaignseloqua_campaignsid PK · linked to eloqua_contacts
email eventseloqua_email_eventsTIMESTAMP events

FAQ

How does Datrise handle Eloqua's custom fields in Databricks SQL Warehouse?

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

How does the Eloqua to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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