DatriseAI-first ETL

Kommo Databricks SQL Warehouse

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

How Datrise loads Kommo into Databricks SQL Warehouse

Datrise syncs Kommo's conversational CRM events, chats, leads, and sales automation triggers into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

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

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Kommo entities map to Databricks SQL Warehouse

Kommo entityDatabricks SQL Warehouse objectNotes
conversational CRM eventskommo_conversational_crm_eventsTIMESTAMP 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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Kommo to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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