FullStory → Spotfire
AI-first ETL from FullStory into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Spotfire
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties 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
FullStory: Digital experience analytics with session replay context.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How FullStory entities map to Spotfire
| FullStory entity | Spotfire object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns for visualizations |
| events | fullstory_events | date/time columns events |
| funnels | fullstory_funnels | id PK · linked to fullstory_sessions |
| frustration signals | fullstory_frustration_signals | id PK · linked to fullstory_sessions |
FAQ
How does Datrise handle FullStory'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 FullStory to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
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Early access
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