Glassfrog to Sisense Integration

How Datrise loads Glassfrog into Sisense

Datrise syncs Glassfrog's circles, roles, people, accountabilities, projects, and governance records 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

Glassfrog: Holacracy roles, circles, and governance records.

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

How Glassfrog entities map to Sisense

Glassfrog entity	Sisense object	Notes
circles	`glassfrog_circles`	id PK · custom fields → flattened columns for the cube
roles	`glassfrog_roles`	id PK · linked to glassfrog_circles
people	`glassfrog_people`	id PK · linked to glassfrog_circles
accountabilities	`glassfrog_accountabilities`	id PK · linked to glassfrog_circles

FAQ

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

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

Related pipelines

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