DatriseAI-first ETL

Ga4 Databricks SQL Warehouse

AI-first ETL from Ga4 into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Ga4 into Databricks SQL Warehouse

Datrise syncs Ga4's records, events, and configuration objects 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

Ga4: SaaS or API data source for analytics and warehouse sync.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Ga4 entities map to Databricks SQL Warehouse

Ga4 entityDatabricks SQL Warehouse objectNotes
recordsga4_recordsid PK · custom fields → VARIANT or STRUCT columns
eventsga4_eventsTIMESTAMP events
configuration objectsga4_configuration_objectsid PK · linked to ga4_records

FAQ

How does Datrise handle Ga4'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 Ga4 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

Connect Ga4 to Databricks SQL Warehouse the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.