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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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Better Know A SSIS Transform – Conditional Split

  • 7 November 2009
  • Author: DevinKnight
  • Number of views: 40628

This is part 3 of my 29 part series called Better Know A SSIS Transform.  Hopefully you will find the series informative.  I will tell you a little about each transform and follow it up with a demo basic you can do on your own. 

The Conditional Split provides a way to evaluate incoming rows and separate those rows by an expression your design.  After these rows are separated they are sent to different outputs so they can either be cleansed, loaded separately, or detect changing data (a good substitute for the slowly changing dimension).  I will provide you some scenarios where you may have to use the conditional split for these reasons and how you would use it.  There are of course other possible reasons you may use the Condition Split but these what I typically use it for.

Cleansing Data Example

The scenario is I have a package that loads Company A customers.  The data that we receive is not always complete though.  Often I will have a zip code for a customer but no city or state.  Because this is a known issue the IT department has purchased a zip code extract that list all zip codes and their associated cites and states.


  • Add Flat File Source pointing to incoming customer data
  • Ensure all zip codes are standardized with a Derived Column Transform
  • Use Conditional Split to separate data that does not have a city and state
  • Send rows without city and state to Lookup Transform that will match zip codes and return missing city and states.  If it doesn’t find a match send the output to a table so the rows can be corrected by hand.
  • Use a Union All to combine original good data with corrected data from the Lookup Transform.
  • Send to Destination Table

Conditional Split Configuration


  • The condition is trimming any blank spaces in the columns and checking to see if the City and State columns are empty.  If they are empty those rows are sent to a Bad Data output.
  • All rows that don’t meet this condition are sent to the Default output Good Data.
  • Another method could be to convert these blank spaces to null before the Conditional Split then just check for null in the Bad Data condition.

Load Data Separate Example

The scenario is I have a package that loads customer mailing lists.  Company B sends out promotions and wants to separate those mailing list depending on a customers education level.  Those with some college and high school or less education will more likely receive my promotion to attend a career college.


  • Add a OLE DB Source to bring in data from my customer table
  • Use a Conditional Split to separate customers by education level
  • Connect outputs to Flat File Destinations to create mailing lists.

Conditional Split Configuration

  • The Completed College output is checking the EnglishEducation column for either a string value of Bachelors or Graduate Degree
  • The Some College output is checking the EnglishEducation column for a string value of Partial College
  • All other rows are sent to the default output named High School Education or Less

Detecting Changing Data Example

This common scenario is using an alternative method to using the Slowly Changing Dimension.  I have incoming records from Company C’s ecommerce system that need to be loaded to my data warehouse.  Before these records get loaded I need to check to see if they are either new, updated or duplicate records.


  • Add a OLE DB Source pointing to ecommerce database
  • Use a Lookup Transform on the destination table joining by the table primary key and rename all Output columns Target_(column_name).  Tell the transform to ignore failure when no matches are found.  A better method is to use either Checksum a Hash byte column for comparison, but this is a good starting method. The Checksum or Hash byte method creates a unique identifying number for each row so instead of comparing each column of a row you can compare just one column to detect a change.
  • Use Conditional Split to determine which records are new, updates or duplicates.
  • Send New records to final destination table
  • Send Updates to a staging table
  • Use an Execute SQL task in the Control Flow to process the updated rows into the destination table.  (This method is much faster than using OLE DB Command)

Conditional Split Configuration


  • The New Record output is checking to see if the Target_(primary_key) is null.  If it is null then we know it’s a new record.
  • If the Target_(primary_key) is not null then the Update output will compare each column to the destination table to see if there are any differences so we know that it needs to be updated.  Again the best method for doing this would be to use either Checksum or Hash byte to create a unique number that represents a row.  Then just compare that one column instead of all columns.
  • Anything that doesn’t meet these conditions are duplicates and we do not want to load.  Just don’t connect the Duplicate output to anything and these rows will not be loaded.
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