DatriseAI-first ETL

Salesforce Marketing Cloud Snowflake

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

How Datrise loads Salesforce Marketing Cloud into Snowflake

Datrise syncs Salesforce Marketing Cloud's records, events, and configuration objects 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

Salesforce Marketing Cloud: SaaS or API data source for analytics and warehouse sync.

Snowflake: Cloud data warehouse with separated compute and storage.

How Salesforce Marketing Cloud entities map to Snowflake

Salesforce Marketing Cloud entitySnowflake objectNotes
recordssalesforce_marketing_cloud_recordsid PK · custom fields → VARIANT columns
eventssalesforce_marketing_cloud_eventsTIMESTAMP_TZ events
configuration objectssalesforce_marketing_cloud_configuration_objectsid PK · linked to salesforce_marketing_cloud_records

FAQ

How does Datrise handle Salesforce Marketing Cloud'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 Salesforce Marketing Cloud 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.

Related pipelines

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