DatriseAI-first ETL

Visma E Conomic Looker

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

How Datrise loads Visma E Conomic into Looker

Datrise syncs Visma E Conomic's records, events, and configuration objects into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

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

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Visma E Conomic entities map to Looker

Visma E Conomic entityLooker objectNotes
recordsvisma_e_conomic_recordsid PK · custom fields → flattened columns (nested fields expanded for modeling)
eventsvisma_e_conomic_eventsdate/time dimension columns 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 Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Visma E Conomic to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

Early access

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