In many Power BI datasets, you’ll notice a separate table containing measures. While this isn’t really necessary, it can be useful.
The downside is that as the number of measures grows, this table will be absolutely flooded with measures, which makes it hard to find the measure you need.
The usual solution for this problem is creating folders in the table... but at first sight, it looks like you can only create folders at the table level, not within the folders themselves. It is possible, though - if you know how.
When applying conditional formatting to a group of cells in Excel, you’re usually limited to the basic built-in icon sets. In other words: you can’t add icons yourself.
If a worksheet contains rows upon rows of numbers, things can look quite cluttered.
As you can see in the example above, the large number of zero values draws the eye away from the fields that contain actual data.
One of the tricks to make the worksheet look less chaotic is by simply not showing the zero values. There are several ways to do this in Excel.
Excel has multiple functionalities that combine text from several cells into one big text. This is called ‘text concatenation’. In the examples below, we’ll assume our table includes the following columns: Street, Number, Postal Code and City.
During our trainings for new Power BI users, we often notice that they don’t always realise how much creating a correct data model could help them. For example:
People often import data straight from the source into the model, map out the necessary relationships and start creating the visuals.
In this blog series, we’ll explore some easy ways to reduce the size of a Power BI model. This blog post teaches you about merging tables.
In this blog series, we’ll explore some easy ways to reduce the size of a Power BI model. This blog posts teaches you how to split columns.
In this blog series, we’ll explore some easy ways to reduce the size of a Power BI model. In this blog post I will tell you why you should remove some fields.
In our previous blog post, we explained how we can use VertiPaq Analyzer to analyse the amount of memory our model needs. This memory is also linked to the speed with which the model calculates and refreshes data. In this blog series, we’ll discuss several ways to limit this memory usage.
One of the things you could do to refresh your data quicker and speed up calculations is resizing the data model. But how do you even figure out the current size of your data model? Is it the same as its file size?
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