Keap → Snowflake
AI-first ETL from Keap into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Keap into Snowflake
Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows 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
Keap: SMB CRM with pipeline automation, email, and appointment flows.
Snowflake: Cloud data warehouse with separated compute and storage.
How Keap entities map to Snowflake
| Keap entity | Snowflake object | Notes |
|---|---|---|
| SMB contacts | keap_smb_contacts | id PK · custom fields → VARIANT columns |
| opportunities | keap_opportunities | id PK · linked to keap_smb_contacts |
| automations | keap_automations | id PK · linked to keap_smb_contacts |
| appointment workflows | keap_appointment_workflows | id PK · linked to keap_smb_contacts |
FAQ
How does Datrise handle Keap'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 Keap 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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