DatriseAI-first ETL

FullStory DuckDB

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

How Datrise loads FullStory into DuckDB

Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

FullStory: Digital experience analytics with session replay context.

DuckDB: In-process analytics database for fast local OLAP.

How FullStory entities map to DuckDB

FullStory entityDuckDB objectNotes
sessionsfullstory_sessionsid PK · custom fields → JSON or STRUCT columns
eventsfullstory_eventsTIMESTAMP WITH TIME ZONE 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 DuckDB?

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

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect FullStory to DuckDB the easy way

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