FullStory → Birst
AI-first ETL from FullStory into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Birst
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
FullStory: Digital experience analytics with session replay context.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How FullStory entities map to Birst
| FullStory entity | Birst object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns |
| events | fullstory_events | date/time dimensions 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 Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the FullStory to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
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Early access
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