Perfect Analysis Services (SSAS) reports in Excel using Power Query

Excel-reports on SSAS cubes (multidimensional and tabular) can have some flaws that now can be overcome by using Power Query for sourcing your cubedata:

  • filter your cube by complete Excel-tables without loading them to the model/cube
  • Apply nice number and date formats to non-measure number and date fields in your row- or column section
  • create fast detailed reports (multiple attributes in your row sections, overcome the slow MDX that the pivots on cubes produce)

As with the recent Power Query update (26) you can now create your own MDX and DAX-statements for retrieving data from a cube, it is also possible to pass individual parameters from your Excel-sheet to the queries. This is a prerequisite for dynamically reducing the number of returned fields to the query, thereby allowing a decent performance of these reports.

So how about filtering the query by a table that sits in your local Excel file? Can we do an inner-join just like on the SQL-server-sourceRead more

Excel Cubefunctions: Trick for dynamic sets

Today I read Andrew Todd’s brilliant guest post on Powerpivotpro.com about some really cool tricks on cubefunctions. The second one shows a very elegant solution to what I’ve formerly done with my “hidden-Pivot”-technique: Avoid the cubeset-functions by referencing a hidden pivot table instead. So if the set of “cubeslices” you want to use in your reports is static, this is the most elegant way to go in my eyes.

But if it is dynamic, like in a Top-10 ranking or similar, this technique still has it’s reason of being: Say you want to show a section in your dashboards that shows top-x elements, a calculation of their share on the total, then an aggregation on all others and a sum on all like this:

NewImage1

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Power Query Management Studio reloaded: Now supports MDX

Very happy to see that my Power Query Management Studio isn’t just perceived nerdy but useful as well 🙂 Thanks to Dusty for his nice review.

So let’s push it a bit further and add some MDX functions to it that cannot be done by DMVs:

  • get a list of all unique fields used in a specific MDX query
  • translate your code to a different cube using a simple field-translation table

How to use this for MDX:

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Is it time to remove detail fields from our cubes?

During my evaluation of Power Query as a reporting engine I wondered why we should keep detail fields in our cube at all if the preferred output is a flat table anyway. Cubes are meant for aggregation, aren’t they?

Especially in the Finance- & Accounting area you will come across many cubes with detail fields because sometimes you simply need to perform analysis on ledger entry level. But this seems like a loose/loose scenario in my eyes: Not only do these detail reports often perform badly, their biggest negative impact might lie in the fact that they cause the fact tables to be x-times bigger than the next aggregation level, thereby decreasing the overall performance of the cube.

So how about this approach then: Use Power Query for your reports on detail level: Directly connect to your fact table in the DWH and merge to your SSAS-data in order to retrieve the attributes/filters only. Or keep your fact tables in a dedicated DB if your DWH serves other purposes as well and you fear the performance impacts of those queries.

So this would leave the cubes’ fact tables with much less data -> improving performance.

I tried some scenarios that worked fine. But putting the fact tables into a separate tabular model instead of a relational DB performed quite badly.

Does anyone have experience with this approach? If you know someone who might, please forward.

What do you think about this approach, any other obstacles I’ve missed?

Report Design with Power Query (1): Cascading Time Granularities

Cascading Time Granularities

When the client is not happy with the pivot report layout options on cubes in Excel, my usual reaction is “OK no problem, then we’re going to use cubefunctions instead”. This went well until recently: My client told me that he wanted a report like above: Years totals first, followed by quarters & months totals, but with growing number of years! All nicely close together, no gaps. Normally I don’t mind cubereports with dynamic table length (will blog about this later), but this is basically 3 dynamic reports under each other (yes they are still alive and will probably stay 😉 ):

  Screen_3Years

So it was time to test what Chris Webb has talked about in this blogpost: Using Power Query as a report authoring tool in Excel. The idea was to use Power Query’s append-function to attaching 3 different report: Year, Quarter, Month. They could then have different lengths, the append-operation would seamlessly stitch them together. The connection to the cubedata was easy enough, thanks to his brilliant step-by-step guide. But then there were some challenges to solve:

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