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Tableau Dashboard Performance Series: Tip#36: Number of Data Sources

This raises many eyebrows. Why does the number of data sources impact my dashboard. And, I would say, it shouldn’t. And we have made many advancements where it might not.

However, in some of the older version prior to 10, we saw a direct co-relation between the number of data sources and the initial load time it took for a dashboard to load on the Tableau Server. If you are seeing that having a higher number of data sources is causing an issue, I would advise you to create dashboards that don’t use more than 3 data sources and compare it.

In any case, if you are combining more than 3 data sources for a single dashboard, I really think you should be using your enterprise data store (EDW or similar) to get the data so you aren’t combining data in real-time with data sources that are very different in terms of their technologies.

Another thing to consider is that Tableau makes separate connection to each one of the data sources so if you have 29 data sources to a database, Tableau might be doing some of the metadata work 29 times.

Tableau Dashboard Performance Series: Tip#35: Tableau Server Load

This happens to fall in the last category where there are certain items that are completely out of control for business teams.

However, having additional information hasn’t even hurt anyone. So, if time and again, you just need to ask your IT counterparts if the servers has enough hardware and it keeping up with the load of the users and the usage, they may appreciate that. In fact, you might need to prepare for some funding too so you can not only ask the right question but offer to help provide $ needed to ensure your success.

Tableau Dashboard Performance Series: Tip#34: Database Load

This happens to fall in the last category where there are certain items that are completely out of control for business teams.

However, having additional information hasn’t even hurt anyone. So, if time and again, you just need to ask your IT counterparts if the database that you are using to analyze your data has enough hardware to support the load on it.

In fact, often times, for enterprises, the data strategy involves setting up databases that support analytical loads. But, unfortunately, once these databases are setup, they are not often maintained how they should. Feel free to contact me if you would like to talk more about this

Tableau Dashboard Performance Series: Tip#33: Data Structures

This happens to fall in the last category where there are certain items that are completely out of control for business teams.

However, having additional information hasn’t even hurt anyone. With data structures, we often mean that data is typically setup in tables and columns and the design of such tables and columns can have an impact on your dashboards.

What??? Yes, think of a table that was created for all possible needs for many different analysts and users. There is no way that all possible adhoc queries can be thought of ahead of time. This may mean that for certain production use cases for operational reporting, you may end up revisiting the data structures after you have decided on fixed set of requirements.

For ad hoc analysis, use Tableau Prep and Tableau extracts , IMHO.

Tableau Dashboard Performance Series: Tip#32: Database Tuning

This is a big topic and often, NOT, something that an analyst of a business user needs to understand or care about.

And typically outside of your control. Having said that, if you can create an extract that is performing better than the analytical DB that you are provided access to, you may want to show the performance on extracts and have your DB team work on tuning so you can EXPECT similar performance.