DatriseAI-first ETL

Azure Table Storage to Sisense Integration

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

How Datrise loads Azure Table Storage into Sisense

Datrise syncs Azure Table Storage's tables, entities, partition keys, row keys, properties, and timestamps 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 Table Storage: Key-value NoSQL store on Azure.

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

How Azure Table Storage entities map to Sisense

Azure Table Storage entitySisense objectNotes
tablesazure_table_storage_tablesid PK · custom fields → flattened columns for the cube
entitiesazure_table_storage_entitiesid PK · linked to azure_table_storage_tables
partition keysazure_table_storage_partition_keysid PK · linked to azure_table_storage_tables
row keysazure_table_storage_row_keysid PK · linked to azure_table_storage_tables

FAQ

How does Datrise handle Azure Table 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 Table 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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