Different processing methods for Google Analytics data in BigQuery can result in discrepancies between daily export and fresh daily.
Differences in event counts
Feature |
Fresh daily export |
Daily export |
Differences in export types |
Events per user limit (typically encountered when missetting User Id) |
Enforce events per user limit continuously. Events exceeding the limit may be dropped throughout the day. |
Enforce events per user limit after processing all events for the day. |
Different events may be dropped due to the timing of enforcement. |
Sub and rollup property events filter |
May over-filter or under-filter due to limited session context. |
Has full session context for filtering. |
Potential for over-filtering or under-filtering in streaming processing. Sub and rollups aren’t covered by the Fresh Daily SLA. |
Spam events filter |
As spam filters are deployed throughout the day, some events will be exported before the filter is deployed. |
Applies spam filters after all events are collected for the day. |
More spam events might be present in streaming data. |
Differences in event dimensions and metrics
Feature |
Fresh daily export |
Daily export |
Differences in export types |
Active user ID |
Determined based on partial data due to continuous processing. |
Determined based on all events for the day. |
Possible discrepancies in active_user_id dimension. |
Audience evaluation |
Evaluated without offline and synthetic events. |
Evaluated with all events, including offline and synthetic events. |
Differences in audience evaluation results. |
Attribution results |
Search Ads 360 (SA360) and app attribution results may not be present |
Contains SA360 and app attribution results. |
Some widening sources aren’t available. |
Server-side state |
Partial data due to continuous processing every 30-60 minutes. |
All events from the current data. |
Differences in dimensions and metrics that rely on server-side state like engaged_sessions and last_purchase_date. |