DatriseAI-first ETL

Google Analytics 4 to Google BigQuery Integration

AI-first ETL from Google Analytics 4 into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Analytics 4 into Google BigQuery

Datrise syncs Google Analytics 4's events, users, sessions, traffic sources, conversions, and audiences into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Google Analytics 4: Event-based web and app analytics from Google.

Google BigQuery: Serverless analytics warehouse on GCP.

How Google Analytics 4 entities map to Google BigQuery

Google Analytics 4 entityGoogle BigQuery objectNotes
eventsga4_eventsTIMESTAMP events
usersga4_usersid PK · linked to ga4_events
sessionsga4_sessionsid PK · linked to ga4_events
traffic sourcesga4_traffic_sourcesid PK · linked to ga4_events

FAQ

How does Datrise handle Google Analytics 4's custom fields in Google BigQuery?

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

How does the Google Analytics 4 to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

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