Recognizing and Preventing Common Data Modeling Anti-Patterns

Introduction

Data modelling is the backbone of any successful application. Good models age well, scale well, and make life easy for the teams that depend on them. Bad models create friction for years. They slow development, complicate analytics, and increase the cost of change. What makes it tricky is that the mistakes do not appear on day one. They reveal themselves when volume grows, when new features arrive, or when the business asks questions that your model cannot answer cleanly.

Avoiding anti-patterns is often more important than chasing the perfect design. If you avoid a few common pitfalls, your data systems will remain stable and easier to evolve. Below are the patterns that consistently appear across databases, warehouses, and modern cloud platforms. Along with each one, you will find the reasoning behind it and the practical fix.

The God Table Anti-Pattern

We’ve seen that one table, with 47 columns, somehow manages to store everything from user preferences to billing information. The “users” table includes fields such as favorite_color, last_login_ip, billing_address_line_2, and emergency_contact_phone.

This anti-pattern emerges gradually. You start with a clean user table, then someone asks, “Can we just add one more field?” Six months later, you’re staring at a monster that violates every principle of normalization.

Why it’s problematic: God tables create tight coupling between unrelated data, make queries slower, and turn simple updates into complex operations. When you need to modify the billing address structure, you’re touching the same table that handles authentication.

The fix: Break down your entities logically. Create separate tables for users, user preferences, billing addresses, and emergency contacts. Use foreign keys to maintain relationships. Your queries become more focused, your indexes more effective, and your sanity intact.

The EAV (Entity-Attribute-Value) Trap

EAV seems like a brilliant solution when flexibility is needed. Instead of adding columns for every possible attribute, you create a generic structure where each row represents one attribute of an entity.

The problems: Queries become nightmarish joins, data types get lost in translation, and performance tanks as your dataset grows. Try writing a query to find all products with both “red” color and “large” size, you’ll need multiple self-joins that make your database cry.

Example:

Better approaches: If you need flexibility, consider using JSON columns in PostgreSQL or MySQL, or better yet, use a document database like MongoDB for truly dynamic schemas. If you must use EAV, limit it to genuinely dynamic metadata, not core business entities.

The One-Size-Fits-All Primary Key

Using auto-incrementing integers for every primary key seems consistent, but it’s not always optimal.

The issue: You end up with meaningless joins and queries that require additional lookups to get human-readable information. Your URLs become opaque (

/products/12847

instead of

/products/wireless-headphones

), and debugging becomes harder when you can’t immediately understand what ID 12847 represents.

The solution: Use natural keys when they exist and are stable. Country codes, currency codes, and product SKUs make excellent primary keys. Reserve surrogate keys for entities that lack natural identifiers or when natural keys are likely to change.

The Timestamp Confusion

Date and time handling is where many data models fall apart. Many systems store dates as strings, mix time zones inconsistently, or use the wrong data types for temporal data.

Common mistakes:

• Storing dates as VARCHAR fields
• Mixing UTC and local times without clear conventions
• Using DATETIME when you only need DATE
• Not considering time zone requirements from the start

Best practices: Always use proper date/time data types. Store everything in UTC and convert to local time in your application layer. Be explicit about whether you need dates, times, or both. Document your time zone handling strategy clearly.

The Soft Delete Obsession

Soft deletes, marking records as deleted instead of actually removing them, seem like a safety net. But they often create more problems than they solve.

Every query needs to remember to filter out deleted records. Your indexes become less efficient. Your data grows indefinitely. And when you finally need to hard delete for compliance reasons, you realize your entire system assumes soft deletes.

When to use soft deletes: Only when you have specific audit requirements or need to maintain referential integrity for historical data. For most cases, proper backups and audit logs are better solutions.

Conclusion

Data modeling is a long-term investment. It shapes how your systems grow and how confidently you can answer questions. You do not need perfection.

Drop a query if you have any questions regarding Data modeling and we will get back to you quickly.

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