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Power BI Publish to Web for Anonymous Access is Here

Earlier this week on Wednesday the Microsoft Power BI made an incredibly exciting announcement and released Power BI “publish to web” as a preview feature. This is HUUUUGE news! This was probably the top requested feature and its finally here thanks to the hard work and dedication of the Microsoft Power BI team!

Read Getting Started with R Visuals in Power BI

Power BI “publish to web” allows you to easily expose a Power BI report to the world through an iframe that can be embedded wherever you like.

To publish your Power BI report to the web, log into your Power BI site.

Find the report that you want to share and click File in the top left.
Power BI publish to web

You’ll see a message pop up box similar to below. Click the yellow button to create the embed code.
Power BI publish to web preview

This is where you’ll see a very important warning!
WARNING: Reports that you expose through the “publish to web” feature will be visible to everyone on the internet! This means NO AUTHENTICATION is required to view the report that is embedded in your application.
warning 2

Once you do that, you’ll receive an embed code that you can then use to expose your Power BI report within your blog as seen below!

https://msit.powerbi.com/view?r=eyJrIjoiYTNjNzcwNjctNTczMy00ZDMxLWFlMGUtMDViODA1NGZiNmI0IiwidCI6IjcyZjk4OGJmLTg2ZjEtNDFhZi05MWFiLTJkN2NkMDExZGI0NyIsImMiOjV9

As you can see the report maintains all the interactivity features of Power BI. And as your Power BI report updates and changes, those changes will be reflected in your embedded Power BI reports!

Pretty awesome!

Additional Resources

Read the Power BI “publish to web” announcement here.

Read the Power BI “publish to web” documentation here.

Feedback

Let me know what you think of this feature or if you have any questions. Leave a comment down below.


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MDX NON EMPTY KEYWORD VS NONEMPTY FUNCTION

Non Empty vs NonEmpty

Hey everyone, in this blog I want to address a very common MDX Question. What is the difference between the NON EMPTY keyword and NONEMPTY function? To take it a step further which one should you use?

Non Empty keyword VS NONEMPTY Function.

The big difference between the NON EMPTY keyword and the NONEMPTY function is when the evaluation occurs in the MDX. The NON EMPTY keyword is the last thing that is evaluated, in other words after all axes have been evaluated then the NON EMPTY keyword is executed to remove any empty space from the final result set. The NONEMPTY function is evaluated when the specific axis is evaluated.

Should I use NON EMPTY keyword or NONEMPTY function?

Ok Mitchell, so you told me when each of these are evaluated but really you haven’t told me anything up until this point. Can you tell me which one I should use already? Well, unfortunately, it depends. Let’s walk through an example of each using the BOTTOMCOUNT function.

BOTTOMCOUNT FUNCTION with NON EMPTY Keyword

In this example I’m returning the bottom ten selling products for internet sales. Notice that I have returned all products that have no internet sales, this is not necessarily a bad thing, maybe you want to return products that don’t have sales.

image

However if you don’t want to return these products then we can try using the NON EMPTY keyword. In the below example you can see the results when I add NON EMPTY to the ROWS axis.

image

WHOOOAAA, what happened?? A lot of people would have expected the results here to show the bottom ten products that DID have sales. However, that is not the case, remember that I said the NON EMPTY keyword is evaluated LAST after all axes have been evaluated. This means that first the bottom ten selling products which have $0 in sales are first returned and then the NON EMPTY keyword removes all that empty space from the final result.

BOTTOMCOUNT function with NONEMPTY function.

So let’s try this again, if you want to return the bottom ten products that had sales then we must first remove the empty space before using the BottomCount function. Take a look at the code below:

image

In this code we first remove the empty space before using the BOTTOMCOUNT function. The result is we return the bottom ten products that had internet sales. Once again neither one is right or wrong here it just depends on what you want in your final result.

NON EMPTY Keyword vs. NONEMPTY Function – Performance

There is a very common misconception that the NONEM

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SSAS - Defining Attribute Relationships in 2005 and 2008

  • 21 September 2010
  • Author: DevinKnight
  • Number of views: 51475
  • 0 Comments

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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