DatriseAI-first ETL

RD Station CRM Amazon Redshift

AI-first ETL from RD Station CRM into Amazon Redshift. Governed entities, incremental sync, typed landing tables.

How Datrise loads RD Station CRM into Amazon Redshift

Datrise syncs RD Station 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

RD Station CRM: CRM widely used in Latin America for sales pipeline and customer ops.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How RD Station CRM entities map to Amazon Redshift

RD Station CRM entityAmazon Redshift objectNotes
contactsrd_station_crm_contactsid PK · custom fields → SUPER columns
accountsrd_station_crm_accountsid PK · linked to rd_station_crm_contacts
dealsrd_station_crm_dealsid PK · linked to rd_station_crm_contacts
activitiesrd_station_crm_activitiesTIMESTAMPTZ events

FAQ

How does Datrise handle RD Station 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 RD Station 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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