DatriseAI-first ETL

Microsoft Dataverse to Sisense Integration

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

How Datrise loads Microsoft Dataverse into Sisense

Datrise syncs Microsoft Dataverse's tables, rows, columns, relationships, choice sets, and change tracking into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Microsoft Dataverse: Business data platform behind Dynamics and Power Apps.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Microsoft Dataverse entities map to Sisense

Microsoft Dataverse entitySisense objectNotes
tablesmicrosoft_dataverse_tablesid PK · custom fields → flattened columns for the cube
rowsmicrosoft_dataverse_rowsid PK · linked to microsoft_dataverse_tables
columnsmicrosoft_dataverse_columnsid PK · linked to microsoft_dataverse_tables
relationshipsmicrosoft_dataverse_relationshipsid PK · linked to microsoft_dataverse_tables

FAQ

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

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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