DatriseAI-first ETL

Delighted Spotfire

AI-first ETL from Delighted into Spotfire. Governed entities, incremental sync, typed landing tables.

How Datrise loads Delighted into Spotfire

Datrise syncs Delighted's surveys, responses, scores, and follow-up workflows into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Delighted: NPS and micro-survey feedback platform.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Delighted entities map to Spotfire

Delighted entitySpotfire objectNotes
surveysdelighted_surveysid PK · custom fields → flattened columns for visualizations
responsesdelighted_responsesid PK · linked to delighted_surveys
scoresdelighted_scoresid PK · linked to delighted_surveys
follow-up workflowsdelighted_follow_up_workflowsid PK · linked to delighted_surveys

FAQ

How does Datrise handle Delighted's custom fields in Spotfire?

Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.

How does the Delighted to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

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