1. Should This Be Normalized?
When Database Normalization Seems Abnormal

2. About Me, Chris Voss Professional side Personal side
SQL/Database Developer at Fresenius Medical Care
Worked with SQL Server for 9 years (started with 2008 R2)
Started as web/data analyst and QA person, then a BI developer, then shifted between analysis and architecture since
Personal side
From Raleigh via Philadelphia
Avid runner (2x marathoner, nd place age group winner @ Nags Head Woods 5k)
Autism spectrum advocate (Autism Society of NC Human Rights Committee)
Lover of obscure pop culture references
About Me, Chris Voss
LinkedIn:

3. What is this about? Normalization vs. denormalization
About the forms, and how they work
First, second, third…Boyce-Codd if you’re lucky
A forum on when normalization actually works, versus if it doesn’t
Audience participation!
Hint: all questions will ultimately have the same answer
What is this about?

4. A definition
What is normalization anyway?

5. The structuring of a relational database to increase integrity and reduce redundancy
Concept introduced by Edgar F. Codd in 1970 while working on data storage
Involves facts and dimensions to look up transactions and references
Normalization

6. What Makes Normalization Good
Less duplication means database size is smaller
In many cases, the first point leads to data models optimized for applications & products
Only need to join necessary tables when querying
New data can easily be inserted with integrity
What Makes Normalization Good

7. Normal forms
First, second, third, and Boyce-Codd

8. First Normal Form “The key”
Elimination of repeating groups and columns
No two rows are identical
Use of composite keys
Primary key to identify
Use the one-to-many relationship to develop without multiple columns
First Normal Form

9. 1NF RacerID Last Name First Name Gender City State Event Race Type 1
Johnson
Jordan F
Waltham MA
Patriot 5k 5k 2
Grant
Dennis M
Easton PA
Boston Marathon
Marathon 3
Denise 4
Cooper River Bridge Run
10k 5
Quincy
Sarah
Manchester NH 6
Fenton
Mitchell
Raleigh NC
Oktoberfest 4 Miler
4 Miler 7
Durham 8
Cape Dash Run

10. Second Normal Form “The whole key”
Everything from First Normal Form still applies
Single column primary keys
Duplicate data sets are removed
Determinants are based on the primary key
Cardinality reduction
Second Normal Form

11. 2NF RacerID Last Name First Name Gender City State 1 Johnson Jordan F
Waltham MA 2
Grant
Dennis M
Easton PA 3
Denise 4
Quincy
Sarah
Manchester NH 5
Fenton
Mitchell
Raleigh NC 6
Durham
EventID
Event
Race Type 1
Boston Marathon
Marathon 2
Cooper River Bridge Run
10k 3
Patriot 5k 5k 4
Oktoberfest 4 Miler
4 Miler 5
Cape Dash Run

12. Third Normal Form “Nothing but the key”
Everything from first and second normal forms apply
Essentially an extension of second normal form
Figuring out if a determinant is not an entity
If A relates to C, C cannot determine B
Third Normal Form

13. 3NF RacerID Last Name First Name Gender LocationID 1 Johnson Jordan F2
Grant
Dennis M 3
Denise 4
Quincy
Sarah 5
Fenton
Mitchell 6
3NF
EventID
Event
Race Type 1
Boston Marathon
Marathon 2
Cooper River Bridge Run
10k 3
Patriot 5k 5k 4
Oktoberfest 4 Miler
4 Miler 5
Cape Dash Run
LocationID
City
State 1
Waltham MA 2
Easton PA 3
Manchester NH 4
Raleigh NC 5
Durham

14. Boyce-Codd Normal Form
Now the transitive dependencies are gone
Every row has a unique identity
If A determines B, it’s because A is a super key
You can usually go straight from first to BCNF by looking at determinants
Race: RaceName, RaceState
Distance: DistanceCode, RaceDistance
Participant: ParticipantName, ParticipantAddress, ParticipantCity, ParticipantState, ParticipantZip
Boyce-Codd Normal Form

15. That is normalization, but our question is…
Should This Be Normalized?
That is normalization, but our question is…

16. When Normalization Fails
JOIN A…JOIN B…JOIN C
Too many joins to slow performance
Every table with the varying data types
Too many dimensions to slow performance
What about all those aggregates?
When Normalization Fails

17. So, we should denormalize?
Let’s think about it.

18. Why denormalize? The Advantages The Disadvantages
Reporting environments often require great performance for frequent pulls
Some calculations can be readily applied
Analytics and data science teams may have an easier time connecting variables
The three types of write anomalies are included
If more write operations are included, everything could actually take longer
Do we know all the rules or do we need to document more?
Write anomalies where a duplicate of

19. Has anyone tried to make a view out of the information they needed?
How many ways can you get to a single fact?
Are there derivable values that can be stored?
Can you collapse the records into one table?
Are there more possibilities for indexes?
Ways to denormalize
Index can be more useful

20. Further use cases
A forum on (de)normalization, where we run through scenarios

21. An address table is populated via trigger when members are inserted
Table only has a primary key clustered index, and gets complaints for slowness
Should this be normalized?
For applications? Reporting?
What should we consider?
How many addresses per member
Other member demographics
How often is this table being called
Address in the box

22. You have a table with phone numbers, split into area code, and first 3 then 4 digits
The audience is customer service, directly accessing the database through an application
Should this be normalized?
The phone number
Country
Code
Area
Office
Prefix
Line
Number 1
215
834
5858
972
976
0227 44
0114
807
6591
305
117
7076

23. A Garmin tracker has history of race results with previous names and addresses included
The PowerBI gurus want to use this for a model on performance across different events
Does denormalization apply here?
What should we consider?
Computing race paces
Storage space
Scalability of results
Partitions
Customer history

24. If you split transactional from analytical, the former can usually stay normalized
Bad performance is often easier to fix than bad data
If you can get to a fact, then you can get to the same fact using a different method, you have a redundancy
Key Factors

25. IT DEPENDS.
Normalize until it hurts
Denormalize until it works

26. More Questions and Answers?

27. Thanks for coming!
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