Kommo → Snowflake
AI-first ETL from Kommo into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Kommo into Snowflake
Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers 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
Kommo: Conversational CRM for WhatsApp, chat funnels, and sales automation.
Snowflake: Cloud data warehouse with separated compute and storage.
How Kommo entities map to Snowflake
| Kommo entity | Snowflake object | Notes |
|---|---|---|
| conversational CRM events | kommo_conversational_crm_events | TIMESTAMP_TZ events |
| chats | kommo_chats | id PK · linked to kommo_conversational_crm_events |
| leads | kommo_leads | id PK · linked to kommo_conversational_crm_events |
| sales automation triggers | kommo_sales_automation_triggers | id PK · linked to kommo_conversational_crm_events |
FAQ
How does Datrise handle Kommo'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 Kommo 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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