Category: Power BI

Imke Feldmann shares a new M function with us:

Look at the M-code that has been generated in the formula bar: “type text” is in quotes and this makes it a text-string. The function dialogue doesn’t give an option to actually select or enter a type expression. This would be without quotes like so:

MyFunction( type text )

So if I aim to feed my function a text value to dynamically create a type from it, I need a function that returns a type and accepts a text value to identify the actual type.

Click through for Imke’s version as well as a second version in the comments.

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John White shows how you can get paginated reports in Power BI at a lower cost:

In our scenario, we have a paginated report that uses a published Power BI dataset as a data source. as of this writing, there is no API call available to render a paginated report on demand, so we will rely on the scheduled subscription capability. in order to minimize the cost of the solution, we want the dedicated capacity to run as little as possible.

The solution will consist of an Azure logic app, and Power BI paginated report scheduling. An Azure logic app uses the same set of actions that a Flow in Power automate does, but is a little more flexible in its permissions model.

Read on for the step-by-step instructions.

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Teo Lachev has some early thoughts on large datasets in Power BI:

At Ignite 2019 Microsoft announced the public preview of large datasets in Power BI Premium. This is a significant milestone as now datasets can grow up to the capacity’s maximum memory (previously, the max size was 10 GB with P3 plan), thus opening the possibility of deploying organizational semantic models to Power BI. I consider this feature mostly suitable for organizational BI as I don’t imagine business users dealing with such large data volumes. I tested large datasets during its private preview, and I’d like to share some notes.

Teo has some open questions, and I’d like to see this shifted down to SSAS too.

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Imke Feldmann finds some nice performance improvements with aggregating data after a join using Power Query:

When you join a table to another table in Power Query, the UI gives you the option to either expand the columns (default) or aggregate the contents of the joint tables. That’s useful if multiple rows are returned for the rows of the table that has been joined to (left table):

But this method is extremely slow. Compared to “simply” expanding all values to new rows (which took around 5 seconds), the aggregation took around 50 seconds. The automatically generated code uses the “Table.AggregateTableColumn”-function.

Read on to see two separate attempts to speed things up.

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