FullStory → Mode
AI-first ETL from FullStory into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads FullStory into Mode
Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
FullStory: Digital experience analytics with session replay context.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How FullStory entities map to Mode
| FullStory entity | Mode object | Notes |
|---|---|---|
| sessions | fullstory_sessions | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the FullStory to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for FullStory
Early access
Connect FullStory to Mode the easy way
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