How to select column which are needed to create report in tableau

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I have tableau desktop. I am creating a report using 5 tables out of 5 table 2 tables are big. These tables are joined and applied filter. extract creation taking a long time (6-7 hours and still running). big tables have 100+ columns, I use only 12 columns to build my report. Now, there is an option to use custom SQL which take less time for creating extract but then I cannot use tableau to its full potential. any suggestion is welcome. I am looking for the name of the column I can choose for creating the extract.

Sep 26, 2018 in Tableau by Naruto
• 710 points
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1 answer to this question.

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Please follow this process:

Make database connection

Join tables

Go to sheet and take required fields needed in report then right click on connection and create a extract then don't forget to click Hide unused fields and then apply required filtering and create a extract

This process should show you only required fields out of all fields.

Especially for very large extracts, you can also consider the option to aggregate to visible dimensions when making an extract. That can dramatically reduce the size of the extract and time to create and access it. But that option requires care to be sure you use the faster extract in a way that still gets accurate results. There are assumptions built in to that feature.

An extract is really a cached query result. If you perform aggregation when creating the extract, you can compute totals, mins, max, avg etc during extract creation, and then simply display the aggregate values in Tableau. This can save a lot of time. Of course, you can’t then further drill down past the level of detail in the extract in that case.

More importantly, if you perform further aggregation in Tableau, you have to be careful that the double aggregation gives the result you intend. Some functions are always safe — sums of sums, mins of mins, maxes of maxes always give the same answer as if you only did one large aggregation operation. These are called additive operations. Other combinations may or may not give the result you intend, averages of averages, and definitely countd of countd can be unexpected - although sometimes repeated aggregation can be well defined - averages of daily sums can make sense for example.

So performing aggregation during extract creation can lead to huge performance gains at visualization time - you effectively precompute much or all of the information you need to display. You just have to understand how it works and use accordingly. Experiment.

By the way, that feature uses the default aggregation defined for each measure in the data source. Usually SUM(). You can change that in the data pane.
answered Sep 26, 2018 by AwesomeSauce
• 860 points

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