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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 - Creating and Using a Writeback Measure Group

  • 25 April 2011
  • Author: DevinKnight
  • Number of views: 29316
  • 0 Comments

Imagine you have spent the last 9 months developing a sales Data Warehouse and an Analysis Services cube.  Your end users love what you have built for them so far but it’s never good enough right?  You have a new requirement to allow your users to enter in projected or target sales for future months.  You think about your options for a while and narrow it down to 3 possible ways to solve the problem.

Option 1:  Have the users enter the targets into an excel spreadsheet and then import the data into your warehouse using a tool like SSIS.

Option 2:  Have the users enter the targets into a SharePoint list and then import the data into your warehouse using a tool like SSIS.

Option 3:  Enable writeback on the measure group’s partition so the users can enter the data in Excel and it will automatically write the data back to the cube and a separate writeback table in a SQL Server database also.

Each of these options has benefits and disadvantages but for the purpose of this post I will focus on option 3 (even writeback has disadvantages).  The goal is to allow users to enter targets with the least amount of effort and maintenance and accomplishes this for the most part. 

To get started you need to first add the table, view or even a named query that stores the granularity that targets will be entered in at.  For example, sales are probably recorded at a daily level but budgeting for targets on sales are likely done at a month level.  So the data source view may look something like the screenshot below. 

 

 

The highlighted object is a named query (this could be a physical table or view) called TargetCategorySales that stores the proper grain that targets would be entered and a place holder column for the soon to be entered targets.  The other non-highlighted tables are the supporting dimensions and the actual sales.

Next add the TargetCategorySales tables will get added as a measure group to the cube.  After adding the new measure group in the Cube Structure tab in the cube designer make your way over to the dimension usage tab and ensure the relationships are properly defined between dimensions and the new measure group.

 

Notice here you will see the granularity is different for each measure group.  The actual sales can be tracked all the way down to an individual product and a day, while the targets are only tracked to a category and month.  It’s probably a good idea to go ahead and reprocess now to ensure everything is still working properly

Next, go to the Partitions tab and right click on the target measure group and click Writeback Settings as shown below. 

 

 

 

 

This brings up a dialog box that will create a new SQL Server table that will store the data changes made by the user in Excel.  Name the table whatever you want then click OK.  This table will not be actually created until the next time you process.  So go ahead and process and you will see the new table in the database you selected in the dialog box earlier.  This table will be empty until the user enters in targets in Excel. 

The user now will open Excel and create a pivot table with the proper granularity, in this case the month and category level.  It’s probably a good idea to create a template for the users that has the appropriate granularity already set in the pivot table.  After creating a proper Excel template as shown below you can enable writeback in Excel 2010 by clicking Enable What-If-Analysis under the PivotTable Options menu.  You’ll also probably want to go ahead and click Automatically Calculate Changes under the same menu, which will apply your changes in Excel as you go. 

 

 

 

Now you’re ready to start adding some targets!  All you have to do is type right on top on the existing numbers in the pivot table.  After typing in your target values go back to the What-If-Analysis menu and click Publish Changes.

 

With these changes in the data it will now appear in the cube for browsing and comparing to actuals.  You can go back and look at the writeback table to see all of the changes that were recorded as shown below.

 

Now after a while this writeback table can get very large so you will eventually want to integrate these targets back into the data warehouse using some kind of ETL process.

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