![]() Use caution with row level calculations that involve a parameter.For instance, if an aggregated extract included COUNTD(Customer_ID), the group by clause of the SQL query used to retrieve data from the underlying source would include Customer_ID. ![]() In order for Tableau to be able to recalculate a distinct count, it has to bring all of the unique members of the base field into the extract. Use caution with aggregations such as COUNTD or other non-additive aggregations.When publishing an extract that will not or should not be refreshed, connect directly to the extract file as a data source before publishing.Incremental refreshes are not possible after an additional file has been appended to a file based data source because the extract has multiple sources at that point.This is because Excel is not strongly typed, and Tableau to does know with certainty that a column that contains numbers contains only integers. When creating an incremental refresh against an Excel data source, only Date columns will be available to use for defining new rows.Therefore, using a date column such as “Last Updated” to drive an incremental refresh could result in duplicate rows in the extract. When performing an incremental extract, records are not replaced.In addition, larger files are more likely to be fragmented on a disk than smaller ones. This is because, by definition, incremental extracts only grow in size, and as a result, the amount of data and areas of memory that must be accessed in order to satisfy requests only grow as well. Incremental extracts become less performant over time. ![]() If there are no new records to add during an incremental extract, the bulk of the processes associated with performing an incremental extract still execute.
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