DatriseAI-first ETL

FullStory Snowflake

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

How Datrise loads FullStory into Snowflake

Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

FullStory: Digital experience analytics with session replay context.

Snowflake: Cloud data warehouse with separated compute and storage.

How FullStory entities map to Snowflake

FullStory entitySnowflake objectNotes
sessionsfullstory_sessionsid PK · custom fields → VARIANT columns
eventsfullstory_eventsTIMESTAMP_TZ 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 Snowflake?

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

How does the FullStory to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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