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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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Thank You for Attending my #SQLSatOrlando Session! Slides, Resources, Recording

SQL Saturday #477 in Orlando, FL has come and gone but what a turn out! The event was excellent, we had a great turnout for our session and had a blast! And as a bonus, the BBQ lunch, baked beans, coleslaw, mac n cheese and dessert were amazing. Seriously one of the best lunches I’ve had a SQL Saturday event! Plus, the Lego name tags were epic! 100% without a doubt the coolest name tag ever.

Thank you to everyone that attending my session this past weekend! I apologize for the lack of space but we had quite a turnout for our session. People were sitting in every aisle, piled up in the front, standing along the back walls and windows. You all had some really great questions and some very valid points. Because of you, our session ended up being a great discussion! Thank you so much!

Standing room only!

Download the Session Materials

If you’d like to download my PowerPoint slide deck that I used during the session, you can find the link to that down below. Also, if you’d like to download the notes Mitch and I used to prep and during the session, you’ll also find that link below.

Download Dustin’s and Mitch’s PowerPoint Slide Deck for Data Warehouse from the Ground Up

Download Dustin’s and Mitch’s Notes

Also, in the past I presented this material during an online webinar for Pragmatic Works so if you missed my session or the event entirely, you can watch the session recording for free!

Watch Dustin’s and Mitch’s Webinar Recording for Data Warehouse from the Ground Up

Data Warehouse Design Resources

There’s two books that I highly recommend if you’re looking to learn the tenants of designing a perfect star schema data warehouse database. These books are excellent and should be in every data warehouse professional’s library, in my opinion!

image The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
image Star Schema: The Complete Reference


Thank you for all the great feedback we received during and after our session. As speakers and

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SQL Internals Reading Data Records Part 1: Tag Bytes

  • 26 June 2012
  • Author: BradleyBall
  • Number of views: 4370

Hello Dear Reader while learning about SQL Server the more you learn the more you start looking at SQL Internals and how data is stored on Disk.  As you go deeper you will eventually use commands like DBCC IND to find Page numbers or DBCC PAGE to look at the contents of a data page to look at actual data records.  When you do you will also see a numeric output along the left hand portion of your screen, it is the data as it is stored on the page, but it isn’t in a format that we would normally read it.  I’ve been in presentations and read books written by some very smart people and I’ve heard them say that reading a data record takes a little practice.  This isn’t so much something that you will use in your job as it is having some fun exploring the tool we use every day, so come along Dear Reader and let’s practice together. 


We’ll start by looking at a graphical representation of a Data Record.  This is the best I’ve ever seen and it comes directly from Paul Randal (@PaulRandal | Blog) and the MCM Video series on Data Structures.


When we look at a Data Record the first part of it is the Tag Bytes.  Tag Bytes are two bytes and are made up of two parts Status Bits A and Status Bits B.  Status Bits A are detailed in Kalen Delaney’s excellent book, SQL Server 2008 Internals

Bit 0 contains versioning Information.  Bits 1 to 3 are used as a 3 bit value to cover a host of things, (we’ll get to in a moment).  Bit 4 indicates that a NULL Bitmap exists, there will always be a NULL Bitmap.  Bit 5 is meant to show if a variable length field exists.  Bit 6 will tell us if there is any versioning information in the record, and bit 7 is not used.  Status B Bits are only used to indicate a ghost forwarded record.  All of that information is stored in two bytes per data record, one byte for Status A and one byte for Status B.


So that was pretty technical, how do we relate this to English?  First off let’s talk about how a record is displayed.  On a page it will be displayed in Hexidecimal code, in order to read it we have to translate that to binary and then flip the binary.  It’s clear as mud but let’s walk through it real quick. 

First we’ll create a database and a table for this 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

Now that we have our table and our data record, let’s take a closer look.  I’ll be using DBCC IND and DBCC PAGE over and over again, the only thing that will change in future examples is the structure of the table we are creating.

DBCC IND(demoInternals, 'myTable1', 1)



Now we have our pages in our table, PageType 10 is an allocation unit our data page is a PageType 1, we can get our page number and plug it into DBCC PAGE.  Don’t forget to turn on Trace Flag 3604 so we can get the output of DBCC PAGE to our SSMS window.


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


I’m only going to post the part that is relevant to us, so this is extremely paired down, and I’m only grabbing our first bit of data

0000000000000000:   10 00e405 01000000 736f6d65 2070726f 64756374  ..ä.....some product


In red you will find our Status A Tag Byte and in blue our Status B. 


I hear what your saying Dear Reader, how is 10 all of that information I talked about up top, it doesn’t look like much.  But this is actually 0x10 in hexadecimal, you can use the converter tool at (plug in value 10) and watch as it is converted to binary.  In binary it is translated to 00010000, we are not quite finished remember we need to flip this value to get what we are looking for.   So how do we flip it, first we read it backwards one section at a time. 


Now one important thing to note is that our 3 bits even though they say 0-7 just like everything else they are binary values as well 0=000, 1=001, 2=010, 3=011, 4=100, 5=101, 6=110, and 7=111.

3-bit binary value

Decimal value



















So these tag tell us that this is a Primary Data  Record, it contains no Versioning Information, no variable length fields, and no Row Versioning information.


So let’s take this same row in our table and delete it to make it a ghost record and see how our Tag bytes change.



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


We don’t want to commit or rollback the transaction just yet, we want the record marked as ghosted.  Now let’s look at our column.

0000000000000000:   1c 00e405 01000000 736f6d65 2070726f 64756374  ..ä.....some product


Remember to convert your 1c value from Hexadecimal to binary, and flip your binary value.       



Remember our chart 110=6, so this is indeed a Ghost Data Record.  So now let’s drop our table and add a variable length column to it.  Then we’ll insert a record.

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'
       ,productExtendedDescription varchar(2000) DEFAULT 'Extended Product Description'
) ;   

INSERT INTO dbo.myTable1

Remember to use DBCC IND and DBCC PAGE to get our internal information, then let’s look at our record.  And translate our Tag A bytes.

0000000000000000:   30 00e405 01000000 736f6d65 2070726f 64756374  0.ä.....some product


Here are our results.



And once again we see translated we have our Primary Data record with a null bitmap and variable length columns.  One interesting note, if you alter your original table to have a variable length column you have to rebuild the table in order for the Tag bytes to change from 0x10 to 0x30.


Hope you enjoyed the read, I had fun writing this up.





Categories: Analysis Services
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