SSASDiag: A Tool To Help Diagnose Analysis Services Problems

There are a lot of great community-developed tools out there for Analysis Services developers to use (BI Developer Extensions, DAX Studio, Tabular Editor, Analysis Services Query Analyzer to name a few) and they have saved me vast amounts of time and effort over the years. When I joined Microsoft last month I came across one which I had never seen before but which is nevertheless quite mature and feature-rich: the SSAS Diagnostics Tool or SSASDiag for short. It’s available on GitHub here:

…and you can read the documentation here:—Analysis


It’s an open source tool developed by the people who support Analysis Services here at Microsoft and is intended to help them collect and analyse the information they need to troubleshoot on-premises SSAS  issues, but it’s available for anyone to use. I haven’t had a chance to take a proper look at it yet myself, unfortunately, but I thought it would be interesting for any SSAS fans out there to check out.

[Thanks to Jon Burchel for providing all the background information for this post]

Sharing Data Between Organisations With Azure Data Share

If you’ve ever built a BI solution it’s likely you will have had to integrate third party data, and if that’s the case you will know how painful it often is to get your hands on that data. Badly designed portals you have to log into every week to download the data, CSV files emailed to you, APIs with complex authentication – it’s usually an unreliable, slow and manual process. This is why I was interested to read about a new Azure service that previewed this week called Azure Data Share that aims to provide a simple and secure way to share data between organisations.

You can read the announcement blog post here:

…read the documentation here:

…and watch an introductory video here:

What could this be used for in Power BI? Well, just for fun I uploaded the contents of a Common Data Model folder to a storage account in one Azure account and used Azure Data Share to copy that folder into a storage account in another Azure account. In the destination the data was stored in Azure Data Lake Gen2 storage, so I was able to attach the CDM folder as a dataflow in Power BI. It took a lot of trial-and-error on my part to get the permissions on the various storage accounts working properly (I’m not an expert on this…) but it worked. This proves that third-party data can be exposed directly inside Power BI as a dataflow using Azure Data Share, which I think is pretty darned cool. If you’re a company that sells data and you want your customers to be able to consume that data easily in Power BI, I think this might be a good way to do it.

Why Power BI Slicers Sometimes Show Values That Do Not Exist

The problem of blank values being shown in a Power BI slicer is fairly well known, but did you know that in some circumstances slicers can show other values that you would not expect to see? While there’s a good reason for this it can nevertheless be mildly confusing for developers and end users alike when it happens.

Let’s take a look at one way this can happen. Here’s an M query that returns a table with up to five random numbers in:

    Source = 
    RandomNumbers = 
                , 0)
    RemoveDuplicates = 
    ToTable = 
    ChangedType = 
            {{"Column1", Int64.Type}}

Here’s an example of the output:


The key point to notice is that every time this query refreshes, a potentially different set of numbers will be returned.

Now consider a report page built from this table with three visuals on it:


There is a slicer on the left with five items in it, a table showing the actual contents of the table (I’ve disabled visual interactions so the slicer doesn’t slice the table) with five rows and a card showing the output of the following measure:

Selected Number = 
SELECTEDVALUE(MyNumbers[Column1], "Nothing Selected")

In the screenshot above you can see I have selected the value 78 in the slicer and the measure – as you would expect – displays that value.

Now what happens when you refresh the dataset and the table contains a different set of numbers? Here’s what:



Summarising what is shown here:

  • After the refresh has taken place the number 78 no longer appears in the underlying table, which contains five rows still
  • However, because 78 was selected before refresh took place, it still appears – and is still selected in – the slicer as an extra value meaning there are now six items visible in the slicer
  • Even though 78 appears and is selected, the measure returns “Nothing Selected” because the DAX SelectedValue() function has detected that no single value has been selected
  • As soon as another value in the slicer is selected (in this case 83), the value 78 disappears from the slicer

Something similar may occur when using the Sync Slicers functionality to synchronise two slicers with different source columns that contain different values (as described here). For example, say you have a second table created as a DAX calculated table with the following expression:

MySecondTable = ROW("Column2",-1)


If you create a slicer from this table, use Sync Slicers to synchronise it with the existing slicer on the report shown above:


…and then select the value –1 in the new slicer, you’ll see it results in the value –1 appearing as selected in the original slicer even though that value does not appear in the underlying column:


Once again, if you select something else in the slicer, the –1 value will disappear.

In my opinion this behaviour makes sense and shouldn’t be changed. In any case, in the real world I always try to avoid situations where the values in a slicer might disappear: if you have a dimensional model and are building slicers from dimension tables rather than fact tables, you should find that values are rarely removed from dimension tables, if ever. That said I can’t help wondering if there are situations where this behaviour could be useful, for example in scenarios where you want to show an initial selection that displays no data but which can’t subsequently be reselected by an end user. As always, I’m interested in your thoughts and comments.

Creating Basic Greater Than Or Less Than Rules With Power BI Conditional Formatting

Although the conditional formatting by rules feature in Power BI was released a long time ago, one very common cause of confusion is with how to implement basic “greater than” or “less than” rules. For example, say you have a table with the following data in it:


…and you want to highlight the rows where Sales are greater than or equal to 150. So you click on the table, go to the Conditional Formatting options for the Sales field, turn on Background Color formatting and click Advanced Controls:


…then choose to format by rules. You see this dialog:


In particular, the part of this dialog where you set up the rule:


…seems to suggest that you need to enter a “is greater than or equal to” condition and a “is less than” condition for the rule to be valid. This is not true, and you don’t need to enter some arbitrarily large number for the “is less than” condition to make it work. The following screenshot shows how you can set up a rule to highlight all rows where Sales are greater than or equal to 150:


The two things to notice are:

  • I’ve entered 150 in the first condition, as you would expect
  • I have deleted the 0 from the second “is less than” condition, leaving the textbox empty (meaning that the text “Maximum” is visible but greyed out)

Here’s the result:


Job done. What about a slightly more complex but nonetheless common scenario where values greater than 0 are shown as green, values equal to 0 as yellow and values less than 0 as red (with maybe some special handling for blanks too)? Here’s a sample table:


(The Dummy column is only there to make sure the Oranges row, which contains a blank value, is visible in the table)

Here’s a set of rules that does what we need:


And here’s the output:


%d bloggers like this: