DatriseAI-first ETL

Klaviyo Snowflake

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

How Datrise loads Klaviyo into Snowflake

Datrise syncs Klaviyo's profiles, segments, flows, campaigns, and attributed revenue 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

Klaviyo: E-commerce marketing automation with email and SMS.

Snowflake: Cloud data warehouse with separated compute and storage.

How Klaviyo entities map to Snowflake

Klaviyo entitySnowflake objectNotes
profilesklaviyo_profilesid PK · custom fields → VARIANT columns
segmentsklaviyo_segmentsid PK · linked to klaviyo_profiles
flowsklaviyo_flowsid PK · linked to klaviyo_profiles
campaignsklaviyo_campaignsid PK · linked to klaviyo_profiles

FAQ

How does Datrise handle Klaviyo'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 Klaviyo 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

Early access

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