By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Anith Sen: Five Simple Database Design Errors You Should Avoid
Share
Notification Show More
Latest News
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
Aa
SmartData Collective
Aa
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
Last updated: 2009/10/19 at 8:16 PM
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

analyzing big data for its quality and value

Use this Strategic Approach to Maximize Your Data’s Value

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing
Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC
Quality Control Tips for Data Collection with Drone Surveying
3 Huge Reasons that Data Integrity is Absolutely Essential



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
KarenLopez October 19, 2009
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

analyzing big data for its quality and value
Big Data

Use this Strategic Approach to Maximize Your Data’s Value

6 Min Read
data lineage tool
Big Data

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

6 Min Read
data quality and role of analytics
Data Quality

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

8 Min Read
data collection with drone use
Data Collection

Quality Control Tips for Data Collection with Drone Surveying

9 Min Read

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

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?