Category: Power BI

Chris Webb has a workaround for dataset refresh errors when using Power BI and writing M code:

This is just a variation on the widely-used M pattern for using functions to iterate over and combine data from multiple data sources; Matt Masson has a good blog describing this pattern here. In this case I’m doing the following:

• Defining a table using #table() with three rows containing three search terms.
• Defining a function that calls the metadata API. It takes one parameter, a search term, and returns a value indicating whether the search was successful or not from the JSON document returned. What the API actually returns isn’t relevant here, though, just the fact that I’m calling it. Note the highlighted lines in the code above that show how I’m constructing the URL passed to Web.Contents() by simply concatenating the base URL with the string passed in via the custom function’s Term parameter.
• Adding a custom column to the table returned by the first step, and calling the function defined in the second step using the search term given in each row.

This query refreshes with no problems in Power BI Desktop. However, when you publish a report that uses this code to PowerBI.com and try to refresh the dataset, you’ll see that refresh fails and returns a rather unhelpful error message:

Data source error Unable to refresh the model (id=1264553) because it references an unsupported data source.

The nature of the problem makes sense, and Chris provides one method of getting around this error.

Avi Singh shares a few methods of allowing users to personalize their Power BI dashboards:

iv. Row Level Security

Row Level Security proved to be an effective approach for us to provide users a personalized view of their Dashboard & Reports based on the Organization they belonged to. The org hierarchy data was pulled directly from the Human Resource (HR) system, which allowed the Power BI Model to identify which user belonged to which department. In our sample data set, it looks as below.

Read the whole thing.

Chris Webb shows the RelativePath and Query parameters of Web.Contents in M:

Generates a call that returns 20 results, rather than the default 10:

https://data.gov.uk/api/3/action/package_search?q=cows&rows=20

Obviously these options make it easier to construct urls and the code is much clearer, but there are also other benefits to using these options which I’ll cover in another blog post soon.

This makes for a more maintainable, dynamic URL generation.  Think about an internal product dashboard, where you might need to make API calls to pull in data by product (or maybe you want to send people to an external link for each product).  This can help you parameterize your URLs quite easily.

Adam Saxton explains where Power BI stores your data and how they choose the region:

This selection is what drives the location of where your data will be stored. Power BI will pick a data region closest to this selection. This selection CANNOT BE CHANGED! You will want to think about the location that makes the most sense for you.

For example, if the majority of your organization’s users are in Australia, and you are in the United States, it probably makes more sense to select Australia for the country. You may also have legal requirements that your organization’s data needs to be in a specific country.

The “cannot be changed” part means that this decision is a lot more important than you might first realize.

Adam Aspin explains some of the concepts behind DirectQuery:

In this article I will not be looking at Power BI as a data visualization tool. Indeed, I will not be discussing in anything more than a cursory way the data transformation and modelling capabilities of this product. Instead, I would like to focus on using Power BI Desktop to connect directly to certain data sources. So although you need no previous knowledge of Power BI Desktop to understand this article, I will be providing only an extremely succinct glance at the extensive array of the data mashup and modelling techniques that are available. Nor will I be explaining any of the approaches that can be taken to create visuals and dashboards. I will, however, try out a few classic techniques using the Power BI Query Editor and Data Model to see if there are any limits to using DirectQuery when compared to data loaded into the in-memory model. The aim here is to see just how, and in what circumstances, DirectQuery can help you to analyze your data faster and more easily.

This article is lengthy but good.  Check it out.