FullStory → Redash
AI-first ETL from FullStory into Redash. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Redash
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, 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 for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.
Ideal for lightweight, query-driven dashboards.
Endpoints
FullStory: Digital experience analytics with session replay context.
Redash: Open-source SQL client for queries, visualizations, and dashboards.
How FullStory entities map to Redash
| FullStory entity | Redash object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns for query results |
| 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 Redash?
Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.
How does the FullStory to Redash 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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