The last update of Power Query finally supports Analysis Services Multidimensional and Tabular. I waited this version for a very long time, but now it's finally here!
Chris Webb already wrote an interesting blog post with several useful link and informations.
You can connect to both Multidimensional and Tabular, but queries are generated in MDX. For this reason, I consider Multidimensional as a primary source by now. Many improvement can be done for multidimensional, whereas Tabular would benefit from DAX support at first.
I want to share my feedback and I already look forward to future improvements: please use the comment to this blog post to share your priorities for these features I would like to see.
Expose Keys
Each Hierarchy in Multidimensional has one or more levels, corresponding to dimension attributes.
Each attribute has up to three properties:
- Key
- Can be multipart, which means it has one or more columns, each column can have a different data type
- Name
- This is always a string. If not defined, it corresponds to the Key, which must have a single column. If the attribute has multipart key, the Name has to be defined in the Multidimensional model
- Value
- This property is optional and is not used often. However, it could represent the name in a numeric value and it’s used when the key cannot be used to represent the corresponding value. This property is not shown to the user but can be used in MDX calculations.
The Name is the one shown in the user interface of power Query. However, when you extract data from a cube, most of the times you need the key column(s) in order to create relationships with other query. For example, a very common scenario is creating three tables, corresponding to two dimensions and a fact table. The keys used to connect the dimension tables to the fact table are mandatory, but are not exposed in the attribute name visible in the user interface of a cube.
Thus, exposing the Key is very important. Please remember that the Key can be a multipart key, so you might have one or more columns to add.
If a user request the Key, by default I would show all the columns of a multipart key so he can remove those columns that are not required (but usually you’ll need all of them). Since the Value is not requested so often, I would expose it as a separate menu.
Surrogate Keys and Other Hidden Attributes
Depending on the cube design, it would be nice exposing those attributes that are hidden in the cube.
For example, in a well-designed cube, the model should not expose the surrogate keys in a visible attribute, because this would create a dependency in reports that would break a filter based on a surrogate key in case the relational tables are reprocessed and surrogate keys are regenerated. The general idea is that a surrogate key does not have a semantic meaning. Thus, it shouldn’t be exposed to an end user.
However, if you are importing several tables from a Multidimensional model, trying to create one table per dimension and one table per fact table (maybe by changing the cardinality, reducing the number of dimensions used), then you should import the surrogate keys too, at least for SCD type 2. Unfortunately, there are no info in the cube model that could help you discriminating between SCD1 and SCD2, so I’m not sure about what could be the best practice in this case. Probably, hidden attributes should be accessible only by advanced users, exposing them by default could be really confusing and I would avoid that.
This is an area where it’s hard to make a right choice, a compromise is required.
Related Properties
An attribute can have other properties related to it. By default, all browsable parent attributes can be considered attribute’s properties. However, when we talk about attribute’s properties we usually refer to the non-browsable attribute. Each non-browsable attribute is just another attribute. Non-browsable attributes are not shown in the list of attributes of a dimension, but they should be available to the user that want to import data for a certain column. The user interface could be designed in several ways for that:
- Show attribute’s properties in a window from which the user can drag&drop – but maybe confusing – the UI should show only properties existing for a particular attribute and each attribute might have different properties. User interface might be a concern here.
- Automatically import all the properties of an attribute (maybe by asking confirmation to the end user?) when adding that attribute to a query. Then the user can remove the columns that are not required in the Query.
Show Formatted Measures
Sometime it might be useful to import the formatted value of a measure. However, I would not replace the number imported today with the formatted value, because the latter could be a string that does not have any semantic meaning. Providing the option of importing the formatted measure as an additional column in the PowerQuery window would be very welcome, but don’t just replace one with the other.
Invisible attributes
Currently, Power Query shows all the attributes of a dimension, which in general is a good thing. However, I would put a checkbox that shows/hides invisible attributes. By default, I would show only visible attributes, because this is what user would be more familiar with. THe “show invisible attributes/columns” should be an advanced view.
Multiple selection without measures
if you select attributes from several dimensions without selecting a measure, you obtain as a result the Crossjoin between the tables you selected. In my opinion, this is counterintuitive and useless: I cannot imagine a use case where this would be meaningful. A much better solution would be importing every dimension as a single table, just as you do when you select many tables from SQL Server. It is the user that will handle joins between table, if necessary. My suggestion is to keep the existing behavior (import a single table) only when you import also a measure, even if I would like to be able to import all the dimensions and the set of measures as separate tables in the data model – creating one query for each dimension and one query for each measure group (or for each cube – not sure about the better approach here).