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«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!

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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:


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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

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SQL Saturday #453–Minnesota 2015 Session Recap–A Window into Your Data

SQL Saturday Minnesota

TSQL WIndow Functions

Thanks for attending my session on T-SQL Window Functions. I hope you learned something you can take back and use in your projects or at your work. You will find an link to the session and code I used below. If you have any questions about the session post them in comments and I will try to get you the answers.

The presentation can be found here:

The code was put into a Word document that you can get here:

This session is also backed by an existing blog series I have written.

T-SQL Window Functions – Part 1- The OVER() Clause

T-SQL Window Functions – Part 2- Ranking Functions

T-SQL Window Functions – Part 3: Aggregate Functions

T-SQL Window Functions – Part 4- Analytic Functions


MSDN Resources:

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Check IsNumeric() with Derived Column Transform in SSIS Package

  • 1 September 2010
  • Author: DustinRyan
  • Number of views: 81508

At some point or another, you've probably run into the road block that is the lack of an ISNUMERIC() equivalent within the SSIS expression language. While you can't use ISNUMERIC() in an SSIS transform, such as a Conditional Split Transform or a Derived Column Transform, that doesn't mean you can't check to see if a field is numeric using the SSIS expression language (If you feel so inclined, you can use a Script Task to check if a field is numeric. Tim Murphy covers that in his blog here).

As I said before, there is a way we can use a Derived Column Transform (or Conditional Split) to check if a field is numeric. After dragging in a Derived Column Transform into your Data Flow Task, create a new column to be added as a new column to your data flow. Give it a meaningful name and use this expression:

(DT_I4)CheckForNumeric == (DT_I4)CheckForNumeric ? 1 : 0


check for numeric with derived column

Then near the bottom of the Derived Column Transform Editor window, click Configure Error Output. You need to tell SSIS to Ignore failure on Error, as seen here:

Ignore failure

Optionally, you could choose to redirect rows that are not numeric to the Error output of the Derived Column and then handle those rows there.

Now when we run the Data Flow Task, we should see results like this:

IsNumeric results with Derived Column

You can see that the rows that are not numeric have a NULL value for the IsNumeric field we created with the Derived Column. Rows that are numeric have a 1. This way it is easy for us to determine which rows of a certain field are numeric and which are not numeric by checking for NULLs in our field called IsNumeric.

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