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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 Internals Reading Data Records Part 2: Null Bitmap Offset

  • 27 June 2012
  • Author: BradleyBall
  • Number of views: 5187
Hello Dear Reader as I’m writing this the remnants of Tropical Storm Debby have finally blown off the coast of Florida and the sun is starting to shine.  The kids are so happy to see the blue skies that they are only out done by the dogs, whom I discovered are not fans of ran whatsoever. This is the first tropical storm we’ve had since we moved to FL, and the earliest one on record for hurricane season.  We’ll have to see if this is an omen of things to come or if it was just a happy accident that gave the state some much needed rain.

But enough of the rain and the Sunshine you stopped by for some talk about SQL Internals and that is just what we are going to get to.


When last we met we were discussing how to read the Tag Bytes of a Data Record.  As you will recall I posted the following picture of a Data Record from Paul Randal (@PaulRandal | Blog) and the MCM Video series on Data Structures.  I’ve updated it to point to our next topic of discussion the Null Bitmap Offset. 


I’ve heard this also referred to as the Fixed Data Record Length portion of the record, and the two confused me at first until I realized they were one in the same.  The purpose of these bytes are to tell us how much fixed length data is stored in the fixed length columns.  To stay consistent we will use the same example as we did in Part 1 .

So if you are missing that code here it is, first we’ll create a database and insert a record.

USE master;

IF EXISTS(SELECT name FROM sys.databases WHERE Name=N'demoInternals')
              DROP Database demoInternals


USE demoInternals

Let's create a Clustered Index

IF EXISTS(SELECT NAME FROM sys.tables WHERE name=N'myTable1')
       DROP TABLE dbo.myTable1
       myID INT IDENTITY(1,1)
       ,productName char(500) DEFAULT 'some product'
       ,productDescription CHAR(1000) DEFAULT 'Product Description'
) ;   

Insert one data record
INSERT INTO dbo.myTable1


You can see that we have nothing but fixed length records in this example.  We’ll use DBCC IND and DBCC PAGE to get our values again.

DBCC IND(demoInternals, 'myTable1', 1)


Remember PageType 10 is an allocation unit and we want to look at our data page so we want PageType 1.  We’ll turn on Trace Flag 3604 so we can see the DBCC PAGE output on our SSMS screen.


DBCC PAGE('demoInternals', 1, 276, 3)

I’m stripping the data out to only what is relevant for today.  The output of DBCC PAGE will have much more information on there as well.

0000000000000000:   1000e40501000000 736f6d65 2070726f 64756374  ..ä.....some product


Today we are looking just at the block in red e405, these are hexadecimal values that are group together in a two byte pair.  To read them we need to reverse them, instead of e405 we are actually looking at 0x05e4.  We’ll use our conversion web page from yesterday to see what this value is (for the tool just type in 05e4),


The value we get back is 1508.  If you look at our schema and add that up it seems a little off at first.  An integer is 4 bytes, plus 500 for our char, plus 1000 for our second char values, 4+500+1000=1504.  So where did the other 4 bytes come from? 


Our Tag Bytes are 2 bytes, and our Null Bitmap Offset are also 2 bytes.  Add those four in and you get 1508.  So let’s do one more example just to test this out.  We can’t use yesterday’s second example because it was the same table, we can’t use the third because all we did was add a variable length column to the table which wouldn’t show up in the fixed length portion of a record.  Looking at that example we can see that.

0000000000000000:   3000e405 01000000 736f6d65 2070726f 64756374  0.ä.....some product


So we will need to make a new table with a different value for our fixed length fields. 

Create Table fixedRecord
       ( myID int
       , mychar char(5)

INSERT INTO dbo.fixedRecord(myid, mychar)
VALUES(1, 'X')

Now let’s find our data page.

DBCC IND(demoInternals, 'fixedRecord', 1)


Remember to set our Trace Flag for 3604 on, if you open a new SSMS query for the script, and look at our data page.


DBCC PAGE('demoInternals', 1, 282, 3)

This table that we have created has a 4 byte integer field and a 5 byte char field for a total of 9 bytes.  Add in our 2 bytes for our Tag Bytes and our 2 bytes for our Null bitmap offset and we should be sitting at 13 Bytes. (Once again only posting the relative portion of the DBCC PAGE output.)

0000000000000000:   10000d00 01000000 58202020 20020000           ........X    ...


Our results are 0d00, remember to reverse these so we get the hexadecimal output of 0x000d, this actually translates down just to d which is equal to 13.





I’ve enjoyed studying and learning on this topic, and judging by the number of hits on part 1 you guys did toLaughing.  Hope to see you next time Dear Reader and as always Thanks for stopping by.





Categories: Analysis Services
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