DatriseAI-first ETL

Visma E Conomic Amazon Redshift

AI-first ETL from Visma E Conomic into Amazon Redshift. Governed entities, incremental sync, typed landing tables.

How Datrise loads Visma E Conomic into Amazon Redshift

Datrise syncs Visma E Conomic's records, events, and configuration objects 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

Visma E Conomic: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Visma E Conomic entities map to Amazon Redshift

Visma E Conomic entityAmazon Redshift objectNotes
recordsvisma_e_conomic_recordsid PK · custom fields → SUPER columns
eventsvisma_e_conomic_eventsTIMESTAMPTZ events
configuration objectsvisma_e_conomic_configuration_objectsid PK · linked to visma_e_conomic_records

FAQ

How does Datrise handle Visma E Conomic'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 Visma E Conomic 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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