Kommo → Looker
AI-first ETL from Kommo into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Kommo into Looker
Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Kommo: Conversational CRM for WhatsApp, chat funnels, and sales automation.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Kommo entities map to Looker
| Kommo entity | Looker object | Notes |
|---|---|---|
| conversational CRM events | kommo_conversational_crm_events | date/time dimension 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 Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Kommo to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
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