DatriseAI-first ETL

Eloqua to Snowflake Integration

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

How Datrise loads Eloqua into Snowflake

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

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Eloqua: B2B marketing automation and lead nurturing.

Snowflake: Cloud data warehouse with separated compute and storage.

How Eloqua entities map to Snowflake

Eloqua entitySnowflake objectNotes
contactseloqua_contactsid PK · custom fields → VARIANT columns
accountseloqua_accountsid PK · linked to eloqua_contacts
campaignseloqua_campaignsid PK · linked to eloqua_contacts
email eventseloqua_email_eventsTIMESTAMP_TZ events

FAQ

How does Datrise handle Eloqua's custom fields in Snowflake?

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

How does the Eloqua to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

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