Dimensional Data Modeling

I am going to do a quick dimensional modeling primer before using the conceptual model to create a logical model.  Dimensional modeling creates a star schema comprised of two basic types of tables:


  1. Fact Tables - these tables contain measures and keys.  The measures are the facts.  If I am counting students then the fact may be as simple as using the value 1 to indicate the existence of registration for a specific term.  The measures can be additive, semi-additive, or non-additive.  Additive measures can be easily summed.  Semi-Additive measures like account balances for example would mean nothing if summed.  Non-additive facts such as GPAs may need to be broken down into their parts (IE individual grades).  The fact table will be the center of the "star".
  2. Dimension Tables - these tables are filters or slicers you will use to aggregate or refine your data sets.  For example, there may be a student dimension table which includes information about a student which permits a report developer to get a count of currently enrolled students based on their demographics such as their age or their financial aid status.
That is a lot of words to let sink in.  However, I prefer pictures.  Below you will see the conceptual model from the previous blog post.  It is pretty clear Student Retention is the business process which will need a fact table.
The fact table will be at the center of the "star".

In the next diagram I have highlighted the potential dimension tables.  These are the way the facts will be filtered.  Notice, I removed the Student Financial Aid and Student Engagement bubbles.  They are actually fact table candidates and in dimensional modeling, fact tables do not directly link.

The next step will be creating a logical model.  This is when we start looking at what attributes and measures will actually be included in the final model.

Popular posts from this blog

Power BI Report Server: "An Error Has Occurred" or Power BI Report Server: "My Power BI Report is Coming up Blank" or Power BI Report Server: "401 and 403” Errors From Nowhere

Image
The information below is an extremely brief summary of the information collected to solve the elusive problem.  There are points I left out because I could not find supporting documentation and I did not want to release suppositions into the universe (why does an NTLM only issue impact a system using kerberos?).  I also want to thank my friend Josh Corrick for staying the course and seeing this through to the end.  See Josh's blog here:  https://corrick.io/blog/ Without warning, our Power BI Report Server began having issues presenting Power BI reports.   The reports would appear to render but would eventually return a blank report canvas.   There was not an error presented and this was impacting all Power BI reports.   An important note is the Power BI reports had worked as expected previously and no changes had been made to the report server such as upgrades or configuration changes. Subsequently, and seemingly unrelated, an error notificatio...
Read more

SQL SERVER 2017: STRING_AGG

STRING_AGG is a function released in SQL Server 2017.   The Oracle equivalent LIST_AGG has been available since 11gR2.  In the meantime, SQL Server users have resorted to black magic in order to get string values from separate rows. STRING_AGG is very welcome but there are some gotchas to be aware of.  Below is a simple example that works just like you think it would. SELECT STRING_AGG(FIrst_Name, ', ') WITHIN GROUP (ORDER BY First_Name) LIST FROM             (SELECT DISTINCT   First_name FROM [dbo].[Employee]     WHERE First_Name like 'A%' OR First_Name like 'B%') A ORDER BY LIST; You can run the query on any database you have string values and you will get something like this: A David, Aaren, Aaron, Abdul, Bob, Brian... If you want to split by the first letter of the name, do not make the mistake of using an over clause.  You will get an error stating STRING_AGG is not a window function....
Read more

Dynamic Label Positions With SSRS

Image
On a recent tabular "dashboard" with several bar charts in a small area our team found vertical positioning of the labels to be troublesome.  When the labels were set to "bottom" and the bar was too short the label would truncate the significant digits.  On the other hand, when the labels were set to "top" the numbers would be truncated if the column was too tall. Example of bar chart with label set to bottom. Same chart with the middle column label set to top. First we thought about using Smart Labels  but we quickly found that smart labels are not so smart when the labels are turned 90 degrees to make them vertical.  As you can see in the examples above the size of the numbers would make horizontal labels messy. So the obvious choice was to use a function to place the label on top when the bar was small and on the bottom once the bar was tall enough. In Report Builder, go to your chart and select a specific column - not just the chart....
Read more