DatriseAI-first ETL

Microsoft Dataverse Tableau

AI-first ETL from Microsoft Dataverse into Tableau. Governed entities, incremental sync, typed landing tables.

How Datrise loads Microsoft Dataverse into Tableau

Datrise syncs Microsoft Dataverse's records, events, and configuration objects into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Microsoft Dataverse: SaaS or API data source for analytics and warehouse sync.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Microsoft Dataverse entities map to Tableau

Microsoft Dataverse entityTableau objectNotes
recordsmicrosoft_dataverse_recordsid PK · custom fields → flattened columns for Tableau fields
eventsmicrosoft_dataverse_eventsdate/datetime fields events
configuration objectsmicrosoft_dataverse_configuration_objectsid PK · linked to microsoft_dataverse_records

FAQ

How does Datrise handle Microsoft Dataverse's custom fields in Tableau?

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

How does the Microsoft Dataverse to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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