DatriseAI-first ETL

Autopilot to Snowflake Integration

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

How Datrise loads Autopilot into Snowflake

Datrise syncs Autopilot's contacts, journeys, lists, email events, triggers, and custom fields 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

Autopilot: Visual marketing automation and customer journeys.

Snowflake: Cloud data warehouse with separated compute and storage.

How Autopilot entities map to Snowflake

Autopilot entitySnowflake objectNotes
contactsautopilot_contactsid PK · custom fields → VARIANT columns
journeysautopilot_journeysid PK · linked to autopilot_contacts
listsautopilot_listsid PK · linked to autopilot_contacts
email eventsautopilot_email_eventsTIMESTAMP_TZ events

FAQ

How does Datrise handle Autopilot'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 Autopilot 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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