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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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Creating Analysis Services Partitions Using BIDS

  • 10 April 2011
  • Author: briankmcdonald
  • Number of views: 33028

As your database grows in size, Analysis Services cubes that use that database grow along with it. As such, one thing that can improve performance of your cube is partitioning (splitting up) your measures. In this post, I am going to quickly show you how to switch from a table binding partition (default) to a query binding partition using a cube that I have built off of the AdventureWorksDW2008R2 database. In the end, you’ll know how to split your large measure groups into smaller chunks of data based on the year. 


When you have your cube opened, navigate to the Partitions tab. A screenshot of what this tab looks like is shown in figure 1. You can have a partition set up for the entire table (not split up) or you can write queries that would return the data you want to be included in that partition.


Figure 1: Partitions Tab

Partitions Tab  


For my example, I am going to break out my Internet Sales measure by years. Which for the AdventureWorksDW2008R2.dbo.FactInternetSales table, we have 2005 – 2008. So, I’m going to create four partitions starting with 2005. To switch the default table binding partition to query mode, select “Query Binding” binding type as shown in figure 2 below.


Figure 2: Switching the Binding Type

Switch Binding Type  


After you switch it to query binding, you’ll be shown a query that you want this partition to contain. Since the first set of Internet Sales records were in 2005, I am just going to update the script to contain “WHERE OrderDateKey <= 20051231” as shown in figure 3.


Figure 3: Update the Script

Update the Query 


After modifying the name assigned to my new partition to “Fact Internet Sales 2005” and choosing to design aggregations later, I now have the results shown in figure 4.


Figure 4: Partition Created for 2005

Partition 2005


Now, I need to click on the “New Partition…” link to create my other partitions in a similar fashion. The slimmed down scripts used to create each of these partitions are shown in script 1.


Script 1: Queries to Partition by Year


--2005 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey <= '20051231'


--2006 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20060101' AND OrderDateKey <= '20061231'


--2007 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20070101' AND OrderDateKey <= '20071231'


--2008 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20080101' AND OrderDateKey <= '20081231'


The results after creating all of these partitions should look like that shown in figure 5 below.


Figure 5: All Yearly Partitions Created

After All Partitions are Created


That’s all there is to creating partitions. So, after assigning these partitions to my AggregationDesign as shown in the example above, deploying and reprocessing, accessing the data within the partitions should be much quicker than having to search through a massive table containing hundreds of millions of records.


I hope that you have enjoyed this post. If you did, please take just a moment to rate it below! Also, if you don’t already, please be sure to follow me on twitter at @briankmcdonald. Also note that you can subscribe to an RSS feed of my blogs.



Brian K. McDonald, MCDBA, MCSD
Business Intelligence Consultant – Pragmatic Works

Email: | Blogs: SQLBIGeek | SQLServerCentral | BIDN

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