DatriseAI-first ETL

Auth0 Google BigQuery

AI-first ETL from Auth0 into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Auth0 into Google BigQuery

Datrise syncs Auth0's authentication logs, sign-ins, user identity changes, and security events 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

Auth0: Identity source for authentication and sign-in telemetry.

Google BigQuery: Serverless analytics warehouse on GCP.

How Auth0 entities map to Google BigQuery

Auth0 entityGoogle BigQuery objectNotes
authentication logsauth0_authentication_logsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
sign-insauth0_sign_insid PK · linked to auth0_authentication_logs
user identity changesauth0_user_identity_changesTIMESTAMP events
security eventsauth0_security_eventsTIMESTAMP events

FAQ

How does Datrise handle Auth0'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 Auth0 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.

Related pipelines

Early access

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