DatriseAI-first ETL

Mssql SQL Server Sisense

AI-first ETL from Mssql SQL Server into Sisense. Governed entities, incremental sync, typed landing tables.

How Datrise loads Mssql SQL Server into Sisense

Datrise syncs Mssql SQL Server'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

Mssql SQL Server: SaaS or API data source for analytics and warehouse sync.

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

How Mssql SQL Server entities map to Sisense

Mssql SQL Server entitySisense objectNotes
recordsmssql_sql_server_recordsid PK · custom fields → flattened columns for the cube
eventsmssql_sql_server_eventsdate/time fields events
configuration objectsmssql_sql_server_configuration_objectsid PK · linked to mssql_sql_server_records

FAQ

How does Datrise handle Mssql SQL Server'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 Mssql SQL Server to Sisense sync stay up to date?

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

Related pipelines

Early access

Connect Mssql SQL Server to Sisense the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.