Delighted → Sisense
AI-first ETL from Delighted into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Delighted into Sisense
Datrise syncs Delighted's surveys, responses, scores, and follow-up workflows 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
Delighted: NPS and micro-survey feedback platform.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Delighted entities map to Sisense
| Delighted entity | Sisense object | Notes |
|---|---|---|
| surveys | delighted_surveys | id PK · custom fields → flattened columns for the cube |
| responses | delighted_responses | id PK · linked to delighted_surveys |
| scores | delighted_scores | id PK · linked to delighted_surveys |
| follow-up workflows | delighted_follow_up_workflows | id PK · linked to delighted_surveys |
FAQ
How does Datrise handle Delighted'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 Delighted to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Delighted
Early access
Connect Delighted 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.