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The discrepancy in data between the Google Analytics API and BigQuery exports could be caused by various factors. One possible reason is sampling. Google Analytics API sometimes uses sampling to quickly generate approximate numbers, especially when dealing with large reports. To get an accurate count, consider breaking down your queries into smaller date ranges. Furthermore, Google Analytics Data API includes a maximum limit of 10 million rows per request, so if you’re dealing with large volumes of data, you might need to retrieve the data in multiple requests.
The differences might also be due to how BigQuery and Google Analytics process and store data differently. BigQuery includes both session-level and hit-level data, while Google Analytics API only retrieves aggregated information. Also, some events might not be recorded or considered in Google Analytics due to filters or tracking code issues, but they are included in BigQuery.
Lastly, note that handling of joins can cause difference in row numbers when you’re using more than one dimension. Google Analytics API uses left outer join. If there is no match for right dimension, you’ll get one row less. Understanding these differences can help you make correct requests and interpret results accurately.
