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
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
    data analytics for trademark registration
    Optimizing Trademark Registration with Data Analytics
    6 Min Read
    data analytics for finding zip codes
    Unlocking Zip Code Insights with Data Analytics
    6 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

Nomination Period Underway for the 2011 Government Big Data Solutions Award
Beware: You are being watched!
Canonical URLs and Faceted Search
Plan to Attend SIGIR ‘09!
The Future of the Grid: From Telecommunications to Cloud-Based Servers



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

crypto marketing
How a Crypto Marketing Agency Can Use AI to Create Powerful Native Advertising Strategies
Blockchain Exclusive Marketing
data driven insights
How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
Analytics Big Data Exclusive
image fx (37)
Boosting SMS Marketing Efficiency with AI Automation
Exclusive
pexels pavel danilyuk 8112119
Data Analytics Is Revolutionizing Medical Credentialing
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Smart Data
Best PracticesBig DataData ManagementData QualityDecision ManagementPredictive AnalyticsRisk ManagementSocial Data

Can Smart Data Ensure Cybersecurity and Data Protection?

6 Min Read

The Circle of Quality

6 Min Read

Big Data

6 Min Read

Red Flag or Red Herring?

5 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
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

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

Sign in to your account

Username or Email Address
Password

Lost your password?