FullStory → Tableau
AI-first ETL from FullStory into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Tableau
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
FullStory: Digital experience analytics with session replay context.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How FullStory entities map to Tableau
| FullStory entity | Tableau object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns for Tableau fields |
| events | fullstory_events | date/datetime fields 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 Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the FullStory to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
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