BigQuery export for server-side tagging (SST) is the process of sending raw event data from a server-side Google Tag Manager container directly to Google BigQuery for storage and analysis. Unlike standard GA4 BigQuery export, which sends processed analytics data, SST-to-BigQuery export captures events at the server container level before they are routed to their final destinations.
This provides organisations with a complete, unsampled dataset that can be queried, transformed, and joined with other business data using SQL, unlocking advanced analytics capabilities beyond what standard dashboards offer.
Setting up BigQuery export from a server-side GTM container involves creating a BigQuery dataset, configuring a BigQuery tag within the server container, and mapping event parameters to table columns. The server container processes incoming events and writes them to BigQuery in real time or via streaming inserts.
Schema design is critical; organisations should plan their table structure to accommodate the specific event parameters and user properties they need for analysis while avoiding unnecessary data collection that could create privacy liabilities.
Server-side BigQuery export offers significant privacy advantages. Because data flows through the server container before reaching BigQuery, organisations can strip personally identifiable information, redact IP addresses, and filter events based on consent status before data is stored.
Seers’ integration with server-side tagging ensures that consent signals are respected throughout the data pipeline, meaning BigQuery only receives data that has been collected with proper legal basis. This creates an analytics infrastructure that is both powerful and privacy-compliant by design.
Strengthen server-side tracking and data privacy with Seers AI
START FREE TODAY