FullStory → Chartio
AI-first ETL from FullStory into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Chartio
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
FullStory: Digital experience analytics with session replay context.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How FullStory entities map to Chartio
| FullStory entity | Chartio object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns for visual SQL |
| events | fullstory_events | temporal 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 Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the FullStory to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for FullStory
Early access
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