DatriseAI-first ETL

1С:CRM Snowflake

AI-first ETL from 1С:CRM into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads 1С:CRM into Snowflake

Datrise syncs 1С:CRM's contacts, accounts, deals, activities, and lifecycle events into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

1С:CRM: CRM with strong adoption in CIS markets for sales and operations.

Snowflake: Cloud data warehouse with separated compute and storage.

How 1С:CRM entities map to Snowflake

1С:CRM entitySnowflake objectNotes
contacts1c_crm_contactsid PK · custom fields → VARIANT columns
accounts1c_crm_accountsid PK · linked to 1c_crm_contacts
deals1c_crm_dealsid PK · linked to 1c_crm_contacts
activities1c_crm_activitiesTIMESTAMP_TZ events

FAQ

How does Datrise handle 1С:CRM's custom fields in Snowflake?

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

How does the 1С:CRM to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

Connect 1С:CRM to Snowflake the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.