Senseforce to Sisense Integration

How Datrise loads Senseforce into Sisense

Datrise syncs Senseforce's assets, sensor readings, events, machines, time series values, and alarms 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

Senseforce: Industrial IoT machine telemetry and analytics.

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

How Senseforce entities map to Sisense

Senseforce entity	Sisense object	Notes
assets	`senseforce_assets`	id PK · custom fields → flattened columns for the cube
sensor readings	`senseforce_sensor_readings`	id PK · linked to senseforce_assets
events	`senseforce_events`	date/time fields events
machines	`senseforce_machines`	id PK · linked to senseforce_assets

FAQ

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

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

Related pipelines

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