Microsoft Dataverse → Birst
AI-first ETL from Microsoft Dataverse into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Microsoft Dataverse into Birst
Datrise syncs Microsoft Dataverse's records, events, and configuration objects into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Microsoft Dataverse: SaaS or API data source for analytics and warehouse sync.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Microsoft Dataverse entities map to Birst
| Microsoft Dataverse entity | Birst object | Notes |
|---|---|---|
| records | microsoft_dataverse_records | id PK · custom fields → flattened columns |
| events | microsoft_dataverse_events | date/time dimensions events |
| configuration objects | microsoft_dataverse_configuration_objects | id PK · linked to microsoft_dataverse_records |
FAQ
How does Datrise handle Microsoft Dataverse's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Microsoft Dataverse to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
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- Microsoft Dataverse → PostgreSQL
- Microsoft Dataverse → MySQL
- Microsoft Dataverse → Microsoft SQL Server
- Microsoft Dataverse → Oracle Database
- Microsoft Dataverse → Snowflake
- Microsoft Dataverse → Google BigQuery
- Microsoft Dataverse → Amazon Redshift
Early access
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