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«November 2015»

DirectQuery in Power BI Desktop

In the latest Power BI Desktop a new Preview features was released that now allows you to connect using DirectQuery to either SQL Server or Azure SQL Databases.  DirectQuery is a really neat feature that allows you to point to the live version of the data source rather than importing the data into a data model in Power BI Desktop. 

Normally when you want to get an updated dataset in the Power BI Desktop you would have to manually click the refresh button (this can be automated in the Power BI Service), which would initiate a full reimport of your data.  This refresh could take a variable amount of time depending on how much data your have.  For instance, if you’re refreshing a very large table you may be waiting quite a while to see the newly added data. 

With DirectQuery data imports are not required because you’re always looking at a live version of the data.  Let me show you how it works!

Turning on the DirectQuery Preview

Now, because DirectQuery is still in Preview you must first activate the feature by navigating to File->Options and settings->Options->Preview Features then check DirectQuery for SQL Server and Azure SQL Database


Once you click OK you may be prompted to restart the Power BI Desktop to utilize the feature.

Using DirectQuery in Power BI Desktop

Next make a connection either to an On-Premises SQL Server or Azure SQL database.

Go to the Home ribbon and select Get Data then SQL Server.


Provide your Server and Database names then click OK. ***Do not use a SQL statement.  It is not currently supported with DirectQuery***


From the Navigator pane choose the table(s) you would like to use.  I’m just going to pick the DimProduct table for this example and then click Load.  You could select Edit and that would launch the Query Editor where you could manipulate the extract.  This would allow you to add any business rules needed to the data before visualizing it.


Next you will be prompted to select what you want to connect to the data. Again, Import means the data

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The Big Data Blog Series

Over the last few years I’ve been speaking a lot on the subject of Big Data. I started by giving an intermediate session called “Show Me Whatcha’ Workin’ With”. This session was designed for people who had attended a one hour introductory session that showed you how to load data, to look at possible applications … Continue reading The Big Data Blog Series
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SSAS - Defining Attribute Relationships in 2005 and 2008

  • 21 September 2010
  • Author: DevinKnight
  • Number of views: 50953

Attribute relationships inside of Analysis Services are a critical piece to the design of your dimensions.  The benefits of creating attribute relationships are multifold.  It can reduce storage space, speed up processing time, improve query performance, and allow usage of member properties. 

So how do attribute relationships work?  The simplest answer is that it tells the formula engine how to roll up measure values.  It defines a one-to-many relationship between attributes in a dimension. 

For example, let’s say you’re working on a date dimension and you need to define a user defined hierarchy for your users because when they look at date they always look at it by Year – Quarter – Month –Date.  Creating this user defined hierarchy saves the user time in dragging each field over individually and helps performance.  If you want to follow along this uses the Adventure Works sample.

To create this hierarchy you must first drag fields over from the Data Source View pane to the Attributes pane to identify them as attributes.  Next, you’ll drag them in the appropriate order (meaning highest level of the hierarchy goes at the top and lowest at the bottom) to the Hierarchies pane to define the user defined hierarchy.  When creating this hierarchy you will likely receive a warning telling you that attribute relationships need to be defined to increase performance.  Here is your big clue into how important attribute relationships are to Analysis Services. 

The design of these relationships can be very different depending on the environment you’re working in.  This is probably the biggest change in SSAS development between SQL Server 2005 and 2008. 

If you’re working in 2005 your screen will look like the below screenshot.  This is the completed design so what you must do to replicate this is expand each attribute in the Attributes pane.  Once this is done you will see ‘L’ shaped attributes under the dimension key.  This is what is used for defining attribute relationships in 2005.  You must drag each ‘L’ shaped attribute from under the dimension’s key to each attribute that it relates to at a lower level.  So, the ‘L’ shaped Year attribute goes under Quarter, Quarter goes under Month, and Month doesn’t go anywhere. 

If you’re working in 2008 your screen will look like the below screenshot.  This design is very different than 2005 because there’s another tab that is now used specifically for attribute relationships.  Personally I think this newer method is a little easier to understand.  This time after creating the hierarchy you just drag the lowest level on top of the next highest level.  So drag Month on top of Quarter and then Quarter on top of Year.  The top level of the hierarchy should always be on the far right.  The relationship between Date and Month is already defined because every attribute has an implicit relationship with the key of the dimension.


Hope this gets you a good start on your dimension design.  If you followed along using Adventure Works then the dimension will actually fail when processing.  The next step is to define KeyColumns when necessary to ensure each attribute is uniquely understood within the user defined hierarchy.

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