Kommo → Google BigQuery
AI-first ETL from Kommo into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Kommo into Google BigQuery
Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Kommo: Conversational CRM for WhatsApp, chat funnels, and sales automation.
Google BigQuery: Serverless analytics warehouse on GCP.
How Kommo entities map to Google BigQuery
| Kommo entity | Google BigQuery object | Notes |
|---|---|---|
| conversational CRM events | kommo_conversational_crm_events | TIMESTAMP 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 Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Kommo to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
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