Such dimensions can also contribute
This row limit is in place to reduce the cost of data processing. It is important to remember that the number of rows that will be processed to produce a data table could be very different from the number of rows actually present in your data table. For example, there could be a case where the data table has a cardinality of 20, but its corresponding underlying data table has a row limit of 7.In that case, the resulting data table will contain lebanon number data only 14 rows (20-7+1), and one of the rows will contain the category “(Other)” that groups the data from the remaining 7 rows. There might be a case where the underlying data table needs to process dimensions that are not part of the report you are viewing. to the cardinality limit. And if these dimensions are high cardinality dimensions , it could impact the cardinality limit of the underlying data tables across your entire GA4 property.
For these reasons, you might hit the row limit even when you are using low cardinality dimensions in your data table. Therefore, it is possible to see “(Other)” in your standard reports even when you are using a low cardinality dimension. You can increase the row limit of the underlying data tables by expanding the dataset or by directly accessing raw data at the event and user level through Exploration Reports or BigQuery.
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