Tableau Dashboard Performance Series: Tip#5: Filter Conditions

There are so many things you can do by setting up the right set of filter conditions on a dashboard.

  • Use Visual Filters
  • Use filter conditions that make the smallest SQL (think include or exclude)
  • Use hierarchical filters instead of relevant filters when possible
  • Use Wildcard filters vs. enumerated lists

Watch the video clip starting at 26:53

Tableau Dashboard Performance Series : Tip#3: Utilizing Tableau Extracts

So, you got access to data, you know what you need from the data source and you are off to the races, building Tableau dashboards

EXCEPT, each one of your queries takes a long time to execute on the DB.

Here are some ideas around how to get past those issues:

  1. If you are told that the DB is well tuned, but it is NOT giving you the performance you need, you need to take the situation in your hands, right? That’s when Tableau extracts can help you out.
  2. If you are given a very complicated SQL that you don’t know how to reconstruct using Tableau visual joins, go ahead and plug in the SQL into the CUSTOM SQL view, and then extract the data
  3. If you are told that the final dashboards needs to be on live data but you need to create dashboards while the DB is being tuned, extract thed ata
  4. If you need to work offline on developing something, extract the data

As you can see there are many scenarions you need to extract the data, but for some reasons, the biggest reason that I STILL see in the field is that many databases are still not tuned for modern analytics that Tableau provides. In that case, we just need to extract the data out

Tableau extracts can make a huge different to your analytical queries as it is a columnar data store. If your data privacy and policies permit, consider using extracts instead of live database connections. Most definitely helpful during dashboard design phases even when final product needs to be based on live data

Here’s how

PRO TIP: Hiding unused fields from the data source can make a huge impact

Tableau Dashboard Performance Series : Tip#2: Number of Marks

The number of marks being shown in a sheet will directly impact the performance of that sheet.

Most of the dashboards are meant to provide a summary of detailed data to our audience. What is the point in having millions of data points in a dashboard

(I guess, EXCEPT, when trying to find outliers or create a density map )

so, in every situation where you can reduce the # of marks to only what you need, you will see significant change in the amount of time it takes to create that view

The previous tip I wrote about this highlights is very well. The next tip is on the Tableau extracts

Tableau Dashboard Performance Series : Tip#9: Custom SQL

Custom SQL can have a HUGE impact on the performance of your visuals for the live connections.

If you are using extracts, there will be no impact to the dashboard performance. But, for live connections, Custom SQL can be really deadly

Watch the clip from my youtube video about Custom SQL

Here is the full recording

Tableau Dashboard Performance Series : Tip#1: Tabular Data

This just happens to one of the biggest reasons for bad performance. Tableau is meant to provide you visibility into your data in a way that you can interact with it.

Long gone are the days when you NEEDED to create long tabular reports to have access to all your data

Just reducing the amount of data displayed at the row level, makes a huge difference.

Go to this tip in my TC19 session

Below is an example of how using “Guided Navigation” principles allows you to show the tabular data only for a certain slice can help improve performance

Tableau Dashboard Performance Series : Tip#6: Sorting

alright, so you want to see the list of your Top 100 customers

Top 100 Customers sorted based on “# of products purchased”

A valid scenario and a reasonable request

Here are those two views (same list of customers just sorted differently)

This slideshow requires JavaScript.

Sorting can have an impact on performance as well, especially, when the metric by which we are sorting has no business relevance and is not already included in the query Tableau is going to execute. 

Watch this the clip from the youtube video about sorting to learn more

NBA 2019 – Playoff Action

It’s that time of the year and for me, its time to do playoff data analysis again

I have been doing this analysis to see if a simpler version of my metric called “Scoring Impact” works the same as the PER developed by NBA.



From the looks of it, my model is way easier to explain and actually shows a co-relation to the PER. Enjoy the analysis here and let’s go…. Bucks?