Kommo → Mode
AI-first ETL from Kommo into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Kommo into Mode
Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Kommo: Conversational CRM for WhatsApp, chat funnels, and sales automation.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Kommo entities map to Mode
| Kommo entity | Mode object | Notes |
|---|---|---|
| conversational CRM events | kommo_conversational_crm_events | temporal columns 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Kommo to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
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