Dynamic Filtering With Power BI + SSAS

Patrick LeBlanc shows in a video how to implement dynamic filtering with SSAS Tabular & Multidimensional in Power BI:

In this video, Patrick answers your question about how to do this in Analysis Services Tabular and Multidimensional. Also, he adds a little bit of SQL to the mix.

Make sure to watch the previous dynamic filtering videos to understand the basics of how to do this.

To begin, you need to make sure to get the URL for your published report.

I completely agree with Patrick about doing as much as you can in the source, especially if there will be more than one potential consumer aside from Analysis Services.

Ignoring SSAS Dynamic Formatting

Chris Webb shows that tools like Power BI ignore formatting in SCOPE statements:

What’s more (and this is a bit strange) if you look at the DAX queries that are generated by Power BI to get data from the cube, they now request a new column to get the format string for the measure even though that format string isn’t used. Since it increases the amount of data returned by the query much larger, this extra column can have a negative impact on query performance if you’re bringing back large amounts of data.

There is no way of avoiding this problem at the moment, unfortunately. If you need to display formatted values in Power BI you will have to create a calculated measure that returns the value of your original measure, set the format string property on that calculated measure appropriately, and use that calculated measure in your Power BI reports instead:

Click through for more details and a workaround.

OLAP Limitations In Tableau

Tim Cost points out areas of friction when trying to use Tableau to connect to a multi-dimensional Analysis Services cube:

I love Tableau, I do NOT however, love working with Tableau when it is connected to an OLAP cube (like Microsoft SQL Server Analysis Services).  I don’t enjoy working with cube data in Tableau because basically all the coolest parts of Tableau won’t work or won’t work in the ways you might expect.  I don’t see this as a failing of Tableau, I lay the blame on the OLAP cube.  The main issue with working against a cube in Tableau is that you talk to a cube with MDX, where we talk to almost every other data source with SQL.  MDX (or Mind Destroying Expressions as I think of them), are just a huge pain to work with.  As hard as it is for ME to write MDX, for Tableau it’s even harder. Here are some things that you should consider before committing to a Tableau project with Microsoft SQL Server Analysis Services as a data source

Click through for ten such considerations.

SSRS Mobile Report Drillthrough

Patrick LeBlanc shows how to drill from a mobile SQL Server Reporting Services report to a paginated report (built on Analysis Services):

17. The report appears but does not execute because the parameters are not set. Why not?

Well, after inspecting the URL (http://localhost/ReportServer/Pages/ReportViewer.aspx?%2fHigher+Education+Solution%2fReports%2fAnnual+Enrollment+Details&DateSchoolYear=2007&Term=Spring), it passed the values as expected. What is the problem? Remember, the parameters are populated from and SSAS model, so that means we need to send the values formatted as such. This format is:

[TableName].[Attribute].&[Value]

No problem, just build that string as part of the URL. Guess what, that doesn’t work either. What you need to do encode certain characters in the URL. For example, to pass year it needs to look like this [Date].[School Year].&[{{SelectionList.SelectedItem}}].

Click through for a step-by-step guide.

SSAS Extended Events

Eugene Polonichko shows how to use Extended Events with SQL Server Analysis Services:

Cubes require frequent monitoring since their productivity decreases quite often (slowdowns during query building, processing time increment). To find out the reason of decrease, we need to monitor our system. For this, we use SQL Server Profiler. However, Microsoft is planning to exclude this SQL tracing tool in subsequent versions. The main disadvantage of the tool is resource intensity, and it should be run on a production server carefully, since it may cause a critical system productivity loss.

Thus, Extended Events is a general event-handling system for server systems. This system supports the correlation of data from SQL Server which allows getting SQL Server state events.

If you’re already familiar with relational engine XEs, this isn’t much of a stretch.

That 53rd Week

Jens Vestergaard notes that you can sometimes have a 53rd week in the year:

There are a lot of great examples out there on how to build your own custom Time Intelligence into Analysis Services (MD). Just have a look at this, this, this, this and this. All good sources for solid Time Intelligence in SSAS.
One thing they have in common though, is that they all make the assumption that there is and will always be 52 weeks in a year. The data set I am currently working with is built on ISO 8601 standard. In short, this means that there is an (re-) occurrence of a 53rd full week as opposed to only 52 in the Gregorian version which is defined by: 1 Gregorian calendar year = 52 weeks + 1 day (2 days in a leap year).

The 53rd occurs approximately every five to six years, though this is not always the case. The last couple of times  we saw 53 weeks in a year was in 1995, 2000, 2006, 2012 and 2015. Next time will be in 2020. This gives you enough time to either forget about the hacks and hard-coded fixes in place to mitigate the issue OR bring your code in a good state, ready for the next time.

Dates and currency are hard problems.

Calculated Dimensions

Ginger Grant shows how calculated dimensions can solve the classic role-playing dimension problem in Analysis Services Tabular:

Working with role playing dimensions, which are found when you have say multiple dates in a table and you want to relate them back to a single date table, have always been problematic in SQL Server Analysis Services Tabular. Tabular models only allow one active relationship to a single column at a time. The picture on the left shows how tabular models represent a role playing dimension, and the model on the right is the recommended method for how to model the relationships in Analysis Services Tabular as then users can filter the data on a number of different date tables.

The big downside to this is one has to import the date table into the model multiple times, meaning the same data is imported again and again. At least that was the case until SQL Server 2016 was released. This weeks TSQL topic Fixing Old Problems with Shiny New Toys is really good reason to describe a better way of handling this problem.

Read on for how to implement calculated dimensions.

MDX Calculation Duration

Chris Webb wants to know how long specific MDX calculations take:

In my last two blog posts (see here and here) I showed how to use the Calculation Evaluation and Calculation Evaluation Detailed Information trace events to work out which MDX calculations are evaluated when a query runs in Analysis Services Multidimensional. That’s very useful, but wouldn’t it be great if you could work out how long any single calculation contributes to the overall duration of a query? If you could, it would make performance tuning MDX calculations much easier.

While you can’t get an exact amount of time taken for each calculation, the good news is that it is possible to get a duration rounded to the next second if your calculation is evaluated in bulk mode.

It’s an interesting way of backing into an answer.

Detail Rows Expression In SSAS Tabular

Chris Webb shows a new feature in SSAS Tabular vNext:

What drillthrough does in SSAS Multidimensional, and what the new Detail Rows Expression property in SSAS Tabular v.next does, is allow an end user to see the detail-level data (usually the rows in the fact table) that was aggregated to give the value the user clicked on in the original PivotTable.

Read through for an example as well as how it’s already an improvement over Multidimensional’s dillthrough.

Basics Of Azure Analysis Services Management

Bill Anton walks through some of the basics of managing an Azure Analysis Services cube:

The quickest win – from an ROI perspective – for Azure AS is the ability to pause the instance during extended periods of inactivity – for example, at night, when there aren’t any users running reports.

This can be achieved via the Suspend-AzureRmAnalysisServicesServer cmdlet we saw in the previous post.

Read on for a few tips of this ilk, including resizing the server.

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