Connect a BigQuery data source and ingest GA4 event data

GA4 exports behavioral event data to BigQuery in a well-defined table structure, making BigQuery a common staging layer for organizations that want to combine web analytics with their customer profile data. Connecting that staging layer to a CDP requires OAuth-based authentication to the cloud project, field-level mapping from the source table's schema to the platform's canonical event schema, and a scheduling strategy that pulls only new or changed rows on each run rather than reloading the full table.

The OAuth credential setup is often the most friction-heavy step: practitioners need GCP project-level permissions to create an OAuth application, must navigate the consent-screen configuration, and must explicitly authorize the BigQuery API scope to obtain a valid refresh token. Once credentials are in place, field mapping is the analytical judgment task: deciding which GA4 dimensions map to which XDM paths determines whether the ingested data can later be joined to profile records for identity resolution. The delta field selection determines ingestion efficiency — choosing a high-cardinality timestamp column enables true incremental loads; choosing a low-cardinality or absent delta field forces full table scans.

Parallel viability: High parallelism. BigQuery-to-CDP ingestion is a vendor-agnostic pattern. Composable equivalents include BigQuery → dbt transformations → Snowflake → Segment (Connections) or BigQuery → Fivetran/Airbyte → any warehouse with a CDP connector. The AEP BigQuery Source Connector adds value specifically when the destination schema is AEP's XDM model and the data needs to participate in AEP's Real-time Customer Profile; teams using a different profile store or analytical warehouse should evaluate the composable path on its merits.