Can you expalin how do you improve dashboard performance?

• Use an extract :Extracts are a quick and easy way to make most workbooks run faster.There is nothing else that comes close to the efficiency gained using an extract. If you don’t absolutely need live data, extracting is the best bet.
• Reduce the scope: whether you’re creating a view, dashboard, or story, it’s tempting to pack a lot of information into your visualization because it’s so easy to add more fields and calculations to the view and more sheets to the workbook. But the result can be that the visualization becomes slower and slower to render.Remember that each worksheet runs one or more queries against the data, so the more sheets, the longer it takes to render the visualization.
• Reduce the number of marks on the view :While there is no hard and fast rule on what defines “too many marks,” be aware that more marks mean that more processing power and memory is required to render them. You can find the number of marks by looking at the lower left of the Tableau Desktop window in the status bar. Watch out for large crosstabs and for maps with complex custom polygons. Keep in mind that too many data points on a view can also reduce the visual analytics value by causing information overload.To avoid this problem, compile related views and connect them with action filters so that you can go from an overview to a more granular view as you explore the data.
• Use Booleans and integers output in calculations :when you create calculated fields; the data type you use has a significant impact on the calculation speed. Integers and Booleans are generally much faster than strings. If your calculation produces a binary result (for example, yes/no, pass/fail, over/under), be sure to return a Boolean result rather than a string.
• Limit the data being introduced to each worksheet if you are not planning on using a set of rows, you should filter them out of the data set as early as possible. If your table contains all sales, and you only want to look at US sales, use a data source filter to restrict data to the US alone. Less data is quicker execution.
• Use Context filter creating one or more context filters improves performance as users do not have to create extra filters on a large data source, reducing the query-execution time.You can create by dragging a filed into ‘Filters’ tab and then, Right-Click that field and select ‘’Add to Context”.You can create by dragging a filed into ‘Filters’ tab and then, Right-Click that field and select ‘’Add to Context”.
• Hide unused fields :Even though we bring all fields from the tables; we end up only using few of them. So, it’s better to remove them from the extract by selecting “Hide All Unused Fields” in worksheets. Bringing fewer data should always improve performance.

• Eliminate any non-essential components from the visualization.This refers to values that would appear on the Pages, Filters, and Level of Detail Shelves. If they are purely there for the user to see if they scroll over a point, then they aren’t not only not adding any value but also impacting the performance.

• Reduce filters usage Take it easy on parameters and quick filters. Despite the name, too many quick filters will actually slow you down. Particularly if you set them to use ‘Only Relevant Values’ and you’ve got lots of discrete lists. For every Quick filter which is added, Tableau has to execute the query in the database to find out which values to display.
• Alternatively, you can use Action filters which do not create any additional database query. Avoid quick filters with Dropdown or Multi-select on high-cardinality columns (columns which has more number of unique values; example: e-mail ids) as it takes too much amount of time to return the result. Alternatively, you can use wildcard match option.
• Alternatively, you can use Action filters which do not create any additional database query. Avoid quick filters with Dropdown or Multi-select on high-cardinality columns (columns which has more number of unique values; example: e-mail ids) as it takes too much amount of time to return the result. Alternatively, you can use wildcard match option.
• Assuming Referential Integrity In some cases, you can improve query performance by selecting the option to Assume Referential Integrity from the Data menu. When you use this option, Tableau will include the joined table in the query only if it is specifically referenced by fields in the view. Using this setting is appropriate when you know that your data has referential integrity but your database is not enforcing or cannot enforce referential integrity. If you have the option of configuring referential integrity in your database that is a better option than using this setting because it can improve performance both in the database and in Tableau. The Assume Referential Integrity option in Tableau can only affect performance on Tableau’s end. If your data does not have referential integrity and you turn on this setting, query results may not be reliable.
• Avoid using custom SQL When you connect to multiple tables in your database, Tableau writes a query that has been optimized for that data source. Unless you are very experienced with join operation optimization, writing custom SQL may cause Tableau’s connection to slow down.
• Remove unnecessary calculations, parameters, and sheets.
• Use indexes in the database and use those fields for filtering
• Push heavy lifting (calculations) to database
• Keep report simple and answering specific questions, it not only puts a cognitive load on the reader but also loads on tableau engine.

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