Business Intelligence Blogs

View blogs by industry experts on topics such as SSAS, SSIS, SSRS, Power BI, Performance Tuning, Azure, Big Data and much more! You can also sign up to post your own business intelligence blog.

«October 2015»

Data Warehouse from the Ground Up at SQL Saturday Orlando, FL on Oct. 10th

SQL Saturday #442SQL Saturday #442 is upon us and yours truly will be presenting in Orlando, Florida on October 10th alongside Mitchell Pearson (b|t). The session is scheduled at 10:35 AM and will last until 11:35 AM. I’m very excited to be presenting at SQL Saturday Orlando this year as it’ll be my first presenting this session in person and my first time speaking at SQL Saturday Orlando! If you haven’t registered yet for this event, you need to do that. This event will be top notch!

My session is called Designing a Data Warehouse from the Ground Up. What if you could approach any business process in your organization and quickly design an effective and optimal dimensional model using a standardized step-by-step method? In this session I’ll discuss the steps required to design a unified dimensional model that is optimized for reporting and follows widely accepted best practices. We’ll also discuss how the design of our dimensional model affects a SQL Server Analysis Services solution and how the choices we make during the data warehouse design phase can make or break our SSAS cubes. You may remember that I did this session a while back for Pragmatic Works via webinar. I’ll be doing the same session at SQL Saturday Orlando but on-prem! ;)

So get signed up for this event now! It’s only 11 days away!

Read more

Create Date Dimension with Fiscal and Time

Here are three scripts that create and Date and Time Dimension and can add the fiscal columns too. First run the Dim Date script first to create the DimDate table. Make sure you change the start date and end date on the script to your preference. Then run the add Fiscal Dates scripts to add the fiscal columns. Make sure you alter the Fiscal script to set the date offset amount. The comments in the script will help you with this.

This zip file contains three SQL scripts.

Create Dim Date

Create Dim Time

Add Fiscal Dates

These will create a Date Dimension table and allow you to run the add fiscal script to add the fiscal columns if you desire. The Create Dim Time will create a time dimension with every second of the day for those that need actual time analysis of your data.

Make sure you set the start date and end date in the create dim date script. Set the dateoffset in the fiscal script.

Download the script here:


Read more

Excel Tip #29: Forcing Slicers to Filter Each Other when Using CUBE Functions

As I mentioned in my original post, Exploring Excel 2013 as Microsoft’s BI Client, I will be posting tips regularly about using Excel 2013 and later.  Much of the content will be a result of my daily interactions with business users and other BI devs.  In order to not forget what I learn or discover, I write it down … here.  I hope you too will discover something new you can use.  Enjoy!


You have went to all the trouble to build out a good set of slicers which allow you to “drill” down to details based on selections. In my example, I have created a revenue distribution table using cube formulas such as:

=CUBEVALUE(“ThisWorkbookDataModel”,$B6, Slicer_Date, Slicer_RestaurantName, Slicer_Seat_Number, Slicer_TableNumber)


Each cell with data references all the slicers. When working with pivot tables or pivot charts, the slicers will hide values that have no matching reference. However, since we are using cube formulas the slicers have no ability to cross reference. For example, when I select a date and a table, I expect to see my seat list reduce in size, but it does not. All of my slicers are set up to hide options when data is available. There are two examples below. In the first, you can see that the seats are not filtered. However, this may be expected. In the second example, we filter a seat which should cause the tables to hide values and it does not work as expected either.



As you can see in the second example, we are able to select a seat that is either not related to the selected table or has no data on that date. Neither of these scenarios is user friendly and does not direct our users to see where the data matches.

Solving the Problem with a “Hidden” Pivot Table

To solve this issue, we are going to use a hidden pivot table. In most cases we would add this to a separate worksheet and then hide the sheet from the users. For sake of our example, I am going to put the pivot table in plain sight for the examples.

Step 1: Add a Pivot Table with the Same Connection as the Slicers

In order for this to work, you need to add a pivot table using the same connection you used with the slicers. The value you use in the pivot table, should only be “empty” or have no matches when that is the expected result. You want to make sure that you do not unintentionally filter out slicers when data exists. In my example, I will use the Total Ticket Amount as the value. That will cover my scenario. In most cases, I recommend looking for a count type valu

Read more

Creating Analysis Services Partitions Using BIDS

  • 10 April 2011
  • Author: briankmcdonald
  • Number of views: 32031

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

Twitter: @briankmcdonald | LinkedIn:

Categories: Blogs
Rate this article:


Other posts by briankmcdonald

Please login or register to post comments.