Why does my dashboard in Data Studio load slowly and how to optimize it?

When loading our reports in Google Data Studio, it is always beneficial to know the factors that can affect its performance and consequently, the loading time. Let's take a look at several of them and explain how you can optimize to the maximum the Data Studio performance.

Here are the most important ones to consider:

The number of widgets per page. Using many widgets on a single page can affect the performance of Data Studio, since they will have to load all at once. If you are in this situation, you can optimize the loading by distributing the widgets among several pages.

The type of widgets: depending on the complexity and the amount of data contained in the widget, Data Studio may take longer to process it. It is important to choose the widget that best suits the information you want to display and configure it to process only the necessary information.

Filters: when filtering, it is important to know that all the data will be loaded first and then the filters will be applied, so if we have made a very large query even if we need little data, the loading time will be that of the total data.

The temporal range and dimensions: the larger the time range we want to obtain data from, and the smaller the temporal sample units of the data, the more information will be processed, and therefore, the loading time will increase. We recommend selecting only the time period you are going to work with and the dimensions you need.

What other ways are there to optimize the loading in Data Studio?

Use the Dataslayer option to limit the number of rows displayed. This will reduce the amount of data to be processed.

Where possible, set a fixed time range on widgets that covers only the data you want to retrieve.

Use the Google Extract Data connector to load the data (only if you refresh the data once a day or week).

If you work with very high data volumes, the best option is to upload the data to BigQuery automatically and without using any code thanks to Dataslayer, and then use the Google BigQuery connector in Data Studio.
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