DatriseAI-first ETL

1С:CRM Amazon Redshift

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

How Datrise loads 1С:CRM into Amazon Redshift

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

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

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

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How 1С:CRM entities map to Amazon Redshift

1С:CRM entityAmazon Redshift objectNotes
contacts1c_crm_contactsid PK · custom fields → SUPER columns
accounts1c_crm_accountsid PK · linked to 1c_crm_contacts
deals1c_crm_dealsid PK · linked to 1c_crm_contacts
activities1c_crm_activitiesTIMESTAMPTZ events

FAQ

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

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

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

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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