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«February 2016»

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!

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.


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

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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.


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.


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.


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:


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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Hierarchy Types in SSAS

  • 22 July 2010
  • Author: kylewalker
  • Number of views: 30278

One of the most powerful tools in the BI stack is the SSAS cube.  The cube is a collection of related dimensions and measures that, once completed, can provide an enormous about of data that can be sliced just about any way you want it...  Oh, and it'll do it just as fast as you can click your mouse.  Now like I said, within a cube there are dimensions.  These are a lot like the ones you'll see in your data warehouse.  A lot of times, within dimension, you'll find hierarchies.  Hierarchies are logical entities that an end user can use to analyze fact data.  These entities can be made of one or multiple levels and can manifest itself in one of three ways: balanced, unbalanced, or ragged.

A balanced hierarchy is one that all of the branches of the hierarchy reach to the same level and each member's parent belongs to the level immediately above it (no gaps in levels).  One common example of a balanced hierarchy can be found in a date dimension.  Here is an illustration of what I'm talking about:

Date Dimension

As you can see, all of the branches reach an equal length and there are no gaps in levels.

The second type of hierarchy is unbalanced.  In an unbalanced hierarchy, all of the branches of the hierarchy don't reach to the same level but each member's parent does belong to the level immediately above it.  One example of this type of hierarchy is an employee hierarchy.  There may be some positions on the same level that don't have direct reports, while others do.  Here is an illustration:

Employee Dimension

The last type of hierarchy is called ragged.  In a ragged hierarchy, each level has a consistent meaning from branch to branch, but one or more branches might have a missing level.  An example of this may be a geography hierarchy, as seen below.

Geography Dimension

I hope this sheds some light on the different types of hierarchies you'll be working with on your next SSAS project.

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