DatriseAI-first ETL

Microsoft Azure Sisense

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

How Datrise loads Microsoft Azure into Sisense

Datrise syncs Microsoft Azure's records, events, and configuration objects 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 Azure: SaaS or API data source for analytics and warehouse sync.

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

How Microsoft Azure entities map to Sisense

Microsoft Azure entitySisense objectNotes
recordsmicrosoft_azure_recordsid PK · custom fields → flattened columns for the cube
eventsmicrosoft_azure_eventsdate/time fields events
configuration objectsmicrosoft_azure_configuration_objectsid PK · linked to microsoft_azure_records

FAQ

How does Datrise handle Microsoft Azure'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 Azure to Sisense sync stay up to date?

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

Related pipelines

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