DatriseAI-first ETL

Kommo Microsoft SQL Server

AI-first ETL from Kommo into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.

How Datrise loads Kommo into Microsoft SQL Server

Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers into Microsoft SQL Server as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.

Ideal for Microsoft-stack analytics and Power BI Import models.

Endpoints

Kommo: Conversational CRM for WhatsApp, chat funnels, and sales automation.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How Kommo entities map to Microsoft SQL Server

Kommo entityMicrosoft SQL Server objectNotes
conversational CRM eventskommo_conversational_crm_eventsdatetime2 events
chatskommo_chatsid PK · linked to kommo_conversational_crm_events
leadskommo_leadsid PK · linked to kommo_conversational_crm_events
sales automation triggerskommo_sales_automation_triggersid PK · linked to kommo_conversational_crm_events

FAQ

How does Datrise handle Kommo's custom fields in Microsoft SQL Server?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft SQL Server types.

How does the Kommo to Microsoft SQL Server sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.

Related pipelines

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