DatriseAI-first ETL

FullStory Databricks SQL Warehouse

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

How Datrise loads FullStory into Databricks SQL Warehouse

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

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

FullStory: Digital experience analytics with session replay context.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How FullStory entities map to Databricks SQL Warehouse

FullStory entityDatabricks SQL Warehouse objectNotes
sessionsfullstory_sessionsid PK · custom fields → VARIANT or STRUCT columns
eventsfullstory_eventsTIMESTAMP 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 Databricks SQL Warehouse?

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

How does the FullStory to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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