DatriseAI-first ETL

Azure Blob Storage to Sisense Integration

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

How Datrise loads Azure Blob Storage into Sisense

Datrise syncs Azure Blob Storage's containers, blobs, blob metadata, snapshots, access tiers, and blob versions 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

Azure Blob Storage: Object storage for blobs and data lake files on Azure.

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

How Azure Blob Storage entities map to Sisense

Azure Blob Storage entitySisense objectNotes
containersazureblobstorage_containersid PK · custom fields → flattened columns for the cube
blobsazureblobstorage_blobsid PK · linked to azureblobstorage_containers
blob metadataazureblobstorage_blob_metadataid PK · linked to azureblobstorage_containers
snapshotsazureblobstorage_snapshotsid PK · linked to azureblobstorage_containers

FAQ

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

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

Related pipelines

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