Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
    financial analytics
    Financial Analytics Shows The Hidden Cost Of Not Switching Systems
    4 Min Read
    warehouse accidents
    Data Analytics and the Future of Warehouse Safety
    10 Min Read
    stock investing and data analytics
    How Data Analytics Supports Smarter Stock Trading Strategies
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Anith Sen: Five Simple Database Design Errors You Should Avoid
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Anith Sen: Five Simple Database Design Errors You Should Avoid
Uncategorized

Anith Sen: Five Simple Database Design Errors You Should Avoid

KarenLopez
KarenLopez
3 Min Read
SHARE

Anith Sen, an SQL and database design guy based in Tennessee, has a well-written blog entry over on Simple-Talk about database design errors.

What I liked about Sen’s post is that he has taken great care to show data and table structures that appear to have some real world complexities to them while still being simple examples. I don’t know many bloggers who do this. Most examples seem to be slathered with “PersonName”, “ZIPCodes” and “tbl_EntityName” data modeling errors that distract me from the points being made. He includes data, table structures, and SQL. Kudos.

The 5 errors discussed are:

  1. Common Lookup Tables
  2. Check Constraint Conundrum
  3. Entity Attribute Value Table
  4. Application Encroachment on DB Design
  5. Misusing Data Values as Data Elements

Personally, I don’t agree that all of his examples are errors, per se, but I do agree that they are anti-patterns for most uses. My usual mantra of “all design decisions come down to cost, benefit, and risk” should apply. If we take, for instance, his example of statuses in a common code table, he seems to imply that all generalizations of status are inappropriate. I do agree with his reasoning as to why the pattern is …

More Read

Mary and Alice
Least Publishable Unit
The Case Against Collaboration, Part III
Social Media Meets Corporate
De-anonymizing Social Networks



Anith Sen, an SQL and database design guy based in Tennessee, has a well-written blog entry over on Simple-Talk about database design errors.

What I liked about Sen’s post is that he has taken great care to show data and table structures that appear to have some real world complexities to them while still being simple examples. I don’t know many bloggers who do this. Most examples seem to be slathered with “PersonName”, “ZIPCodes” and “tbl_EntityName” data modeling errors that distract me from the points being made. He includes data, table structures, and SQL. Kudos.

The 5 errors discussed are:

  1. Common Lookup Tables
  2. Check Constraint Conundrum
  3. Entity Attribute Value Table
  4. Application Encroachment on DB Design
  5. Misusing Data Values as Data Elements

Personally, I don’t agree that all of his examples are errors, per se, but I do agree that they are anti-patterns for most uses. My usual mantra of “all design decisions come down to cost, benefit, and risk” should apply. If we take, for instance, his example of statuses in a common code table, he seems to imply that all generalizations of status are inappropriate. I do agree with his reasoning as to why the pattern is costly, but I don’t see any reason why all statuses need to be in separate tables. I don’t believe all codes should be in one big table, either. Hence, my invocation of cost, benefit, and risk still applies.

A great article, though. Well worth your time to read and absorb.

Technorati Tags: database design,data modeling,antipatterns,errors,entity attribute value,lookup table,check constraint

TAGGED:data quality
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

edi compliance with AI
AI Is Transforming EDI Compliance Services
Exclusive News
companies using big data
5 Industries Driving Big Data Technology Growth
Big Data Exclusive
software developer using ai
California AI Companies That Are Set for Long-Term Growth
Development Exclusive
data science professor
The Power of Warm-Ups: Setting the Stage for Learning
Exclusive News

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

The Data Quality Goldilocks Zone

6 Min Read

Sun Tzu and the Art of Data Quality

6 Min Read

Pooh, Data Analyst

5 Min Read

Resistance is NOT Futile

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?