DatriseAI-first ETL

FullStory Looker Studio

AI-first ETL from FullStory into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads FullStory into Looker Studio

Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

FullStory: Digital experience analytics with session replay context.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How FullStory entities map to Looker Studio

FullStory entityLooker Studio objectNotes
sessionsfullstory_sessionsid PK · custom fields → flattened columns for chart fields
eventsfullstory_eventsdate dimension columns events
funnelsfullstory_funnelsid PK · linked to fullstory_sessions
frustration signalsfullstory_frustration_signalsid PK · linked to fullstory_sessions

FAQ

How does Datrise handle FullStory's custom fields in Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the FullStory to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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