Aug 28, 2025
10 Common Mistakes in Database Design (And How to Avoid Them)
10 Common Mistakes in Database Design (And How to Avoid Them)
10 Common Mistakes in Database Design (And How to Avoid Them)
Learn the 10 most common database design mistakes and how to avoid them for efficient, scalable, and error-free systems.
Learn the 10 most common database design mistakes and how to avoid them for efficient, scalable, and error-free systems.
Learn the 10 most common database design mistakes and how to avoid them for efficient, scalable, and error-free systems.
Jonathan Fishner
9 minutes read
I’m Jonathan, co‑founder of ChartDB. A few years ago, one of my friends’ startups launched their app only to have it collapse because its database lacked proper foreign keys. Orders vanished, user data got out of sync, and recovery dragged on for days. I’ve been through similar challenges, and over the last decade of building internal tools and helping startups, I’ve learned a lot.
If you’re a startup CTO, developer, or tech architect, this post is for you. I’ll walk you through the ten most frequent database design mistakes I’ve seen and share how to dodge them from the start, using examples grounded in real scenarios, but all freshly crafted. Plus, I’ll show how ChartDB helps address each one.
Quick Note: What Is ChartDB.io?
ChartDB is an open-source tool that helps you visualise and organise your database schema with a single query. This is an ERD tool that can actually fit your workflow.
You can import schemas from Postgres, MySQL, SQL Server, SQLite, and more
Then edit tables and relationships visually
Export clean DDL, share your diagrams, and embed a live diagram into docs or Notion
10 Common Mistakes in Database Designs
Let’s discuss about the common mistakes that happen when designing a database.
1. Not Planning Ahead
Mistake: Jumping into database creation without a clear understanding of what data you need or how it evolves.
Example: Creating a Bookings table for a hotel app before clarifying how to support group bookings or multiple guests per room. Later, you realise you need to restructure everything using a GroupBookings model.
Fix: Before you write any SQL, sketch out the data model. Understand your entities, how they relate, and which queries will run most frequently. ChartDB help you visualise and adjust this on the fly.
2. Ignoring Normalisation (or Overdoing It)
Mistake: Either cramming too much into one table or overcomplicating your schema with unnecessary joins.
Example: A CourseRegistrations table stores registered course names in a single comma-separated field like 'Math, Physics, Chemistry'. You can't query individual registrations efficiently. On the flip side, splitting every small property into its own table quickly becomes overwhelming.
Fix: Normalize to third normal form (3NF) for clarity and maintainability. Only denormalize when you know it solves a specific problem. You want just enough structure without losing readability.
3. Mixing Granularities or Storing Redundant Data
Mistake: Mixing aggregated data with raw values or duplicating derived metrics.
Example: A KPI_Metrics table has rows that represent daily metrics, weekly rollups, and monthly summaries, distinguished only by a period_type column. It becomes a nightmare to query or trust.
Fix: Store raw events in one table and use separate reporting tables or views for each level of aggregation. Don’t store both age and birthdate. Keep source data clean and compute everything else as needed.
4. Poor Naming Practices
Mistake: Inconsistent, cryptic, or auto-generated names confuse developers and slow teams down.
Example: Table names like tbl_usrdata or columns like val_1, flag_a, or t1 give zero context.
Fix: Use meaningful, consistent naming like user_profiles, email_address, created_at. Stick to one style, snake_case works well for most databases. Make sure names tell a clear story.
5. Skipping Data Integrity Constraints
Mistake: Relying on your application to enforce business rules.
Example: A Payments table accepts negative values because amount isn’t validated, or transaction_id is allowed to be null. Eventually, someone inserts bad data and it causes issues downstream.
Fix: Let the database protect itself. Use NOT NULL, CHECK(amount > 0), UNIQUE, PRIMARY KEY, and FOREIGN KEY. Add constraints where they matter. It’s much easier to fix bugs before the data gets in.
6. Using Business Fields as Primary Keys
Mistake: Making columns like email, username, or product_code your primary key.
Example: In a Partners table, using company_name as the primary key seems natural until one rebrands. You’re now stuck updating every related record manually.
Fix: Always use synthetic keys like partner_id (integer or UUID) as your primary key. Then enforce uniqueness separately on business identifiers like email or company_name.
7. One Table Trying to Do It All
Mistake: Overloading a single table with too many unrelated responsibilities.
Example: You build a Notifications table that holds user alerts, system errors, and audit logs. Each has different behaviour, but now they all share the same schema.
Fix: Split them into separate tables - UserAlerts, SystemLogs, and AuditEvents. If they need to be queried together, use views or shared interfaces, but keep their data responsibilities clean.
8. Overloading Views or ETL Pipelines
Mistake: Nesting too many views or cramming too much logic into one transformation job.
Example: A dashboard pulls data from sales_view, which pulls from summary_view, which pulls from a 10-join monster view. One wrong assumption upstream breaks the entire chain.
Fix: Build layered views (raw_, stage_, final_) or modular ETL steps. Keep each layer focused and testable. If something breaks, you’ll know exactly where to look.
9. Treating Schema as Final
Mistake: Designing as if your schema won’t ever need to change.
Example: Your Profiles table has custom_field1, flag_x, misc_data, and other ambiguous fields. Over time, no one knows what’s used where.
Fix: Review your schema regularly. Refactor early. Use tools like ChartDB to identify unused fields or messy relationships. Schemas evolve, your system should too.
10. No Documentation or Testing
Mistake: When you skip documenting your schema or testing migrations, things fall apart silently.
Example: A status_code column uses numeric values from 1 to 3, but someone adds 4 and the entire billing report stops working.
Fix: Use COMMENT ON COLUMN to explain intent. Write tests for your migrations. Keep ER diagrams or schema markdowns up to date. Even light docs make a big difference when onboarding someone or debugging a bug.
How ChartDB Helps You Avoid These Mistakes
I co-founded ChartDB after seeing these problems repeat across teams and companies. So we built it to solve them directly:
Schema visualization with one query helps you catch problems before they get serious
Visual editing makes renaming, restructuring, and refactoring intuitive
Relationship detection helps identify missing foreign keys or constraints
Export options (SQL, DBML, JSON) streamline your documentation and migration process
Live diagram embedding in docs and Notion keeps everyone aligned and informed
Conclusion
Most of these mistakes are easy to make, especially when you're moving fast. I’ve made most of them myself. But with the right mindset and tools, they’re just as easy to avoid.
If you want to design better schemas, collaborate faster, and avoid those painful refactors later, give ChartDB a try. It’s free to start and takes just one query to get going.
Thanks for reading, and happy databasing.
I’m Jonathan, co‑founder of ChartDB. A few years ago, one of my friends’ startups launched their app only to have it collapse because its database lacked proper foreign keys. Orders vanished, user data got out of sync, and recovery dragged on for days. I’ve been through similar challenges, and over the last decade of building internal tools and helping startups, I’ve learned a lot.
If you’re a startup CTO, developer, or tech architect, this post is for you. I’ll walk you through the ten most frequent database design mistakes I’ve seen and share how to dodge them from the start, using examples grounded in real scenarios, but all freshly crafted. Plus, I’ll show how ChartDB helps address each one.
Quick Note: What Is ChartDB.io?
ChartDB is an open-source tool that helps you visualise and organise your database schema with a single query. This is an ERD tool that can actually fit your workflow.
You can import schemas from Postgres, MySQL, SQL Server, SQLite, and more
Then edit tables and relationships visually
Export clean DDL, share your diagrams, and embed a live diagram into docs or Notion
10 Common Mistakes in Database Designs
Let’s discuss about the common mistakes that happen when designing a database.
1. Not Planning Ahead
Mistake: Jumping into database creation without a clear understanding of what data you need or how it evolves.
Example: Creating a Bookings table for a hotel app before clarifying how to support group bookings or multiple guests per room. Later, you realise you need to restructure everything using a GroupBookings model.
Fix: Before you write any SQL, sketch out the data model. Understand your entities, how they relate, and which queries will run most frequently. ChartDB help you visualise and adjust this on the fly.
2. Ignoring Normalisation (or Overdoing It)
Mistake: Either cramming too much into one table or overcomplicating your schema with unnecessary joins.
Example: A CourseRegistrations table stores registered course names in a single comma-separated field like 'Math, Physics, Chemistry'. You can't query individual registrations efficiently. On the flip side, splitting every small property into its own table quickly becomes overwhelming.
Fix: Normalize to third normal form (3NF) for clarity and maintainability. Only denormalize when you know it solves a specific problem. You want just enough structure without losing readability.
3. Mixing Granularities or Storing Redundant Data
Mistake: Mixing aggregated data with raw values or duplicating derived metrics.
Example: A KPI_Metrics table has rows that represent daily metrics, weekly rollups, and monthly summaries, distinguished only by a period_type column. It becomes a nightmare to query or trust.
Fix: Store raw events in one table and use separate reporting tables or views for each level of aggregation. Don’t store both age and birthdate. Keep source data clean and compute everything else as needed.
4. Poor Naming Practices
Mistake: Inconsistent, cryptic, or auto-generated names confuse developers and slow teams down.
Example: Table names like tbl_usrdata or columns like val_1, flag_a, or t1 give zero context.
Fix: Use meaningful, consistent naming like user_profiles, email_address, created_at. Stick to one style, snake_case works well for most databases. Make sure names tell a clear story.
5. Skipping Data Integrity Constraints
Mistake: Relying on your application to enforce business rules.
Example: A Payments table accepts negative values because amount isn’t validated, or transaction_id is allowed to be null. Eventually, someone inserts bad data and it causes issues downstream.
Fix: Let the database protect itself. Use NOT NULL, CHECK(amount > 0), UNIQUE, PRIMARY KEY, and FOREIGN KEY. Add constraints where they matter. It’s much easier to fix bugs before the data gets in.
6. Using Business Fields as Primary Keys
Mistake: Making columns like email, username, or product_code your primary key.
Example: In a Partners table, using company_name as the primary key seems natural until one rebrands. You’re now stuck updating every related record manually.
Fix: Always use synthetic keys like partner_id (integer or UUID) as your primary key. Then enforce uniqueness separately on business identifiers like email or company_name.
7. One Table Trying to Do It All
Mistake: Overloading a single table with too many unrelated responsibilities.
Example: You build a Notifications table that holds user alerts, system errors, and audit logs. Each has different behaviour, but now they all share the same schema.
Fix: Split them into separate tables - UserAlerts, SystemLogs, and AuditEvents. If they need to be queried together, use views or shared interfaces, but keep their data responsibilities clean.
8. Overloading Views or ETL Pipelines
Mistake: Nesting too many views or cramming too much logic into one transformation job.
Example: A dashboard pulls data from sales_view, which pulls from summary_view, which pulls from a 10-join monster view. One wrong assumption upstream breaks the entire chain.
Fix: Build layered views (raw_, stage_, final_) or modular ETL steps. Keep each layer focused and testable. If something breaks, you’ll know exactly where to look.
9. Treating Schema as Final
Mistake: Designing as if your schema won’t ever need to change.
Example: Your Profiles table has custom_field1, flag_x, misc_data, and other ambiguous fields. Over time, no one knows what’s used where.
Fix: Review your schema regularly. Refactor early. Use tools like ChartDB to identify unused fields or messy relationships. Schemas evolve, your system should too.
10. No Documentation or Testing
Mistake: When you skip documenting your schema or testing migrations, things fall apart silently.
Example: A status_code column uses numeric values from 1 to 3, but someone adds 4 and the entire billing report stops working.
Fix: Use COMMENT ON COLUMN to explain intent. Write tests for your migrations. Keep ER diagrams or schema markdowns up to date. Even light docs make a big difference when onboarding someone or debugging a bug.
How ChartDB Helps You Avoid These Mistakes
I co-founded ChartDB after seeing these problems repeat across teams and companies. So we built it to solve them directly:
Schema visualization with one query helps you catch problems before they get serious
Visual editing makes renaming, restructuring, and refactoring intuitive
Relationship detection helps identify missing foreign keys or constraints
Export options (SQL, DBML, JSON) streamline your documentation and migration process
Live diagram embedding in docs and Notion keeps everyone aligned and informed
Conclusion
Most of these mistakes are easy to make, especially when you're moving fast. I’ve made most of them myself. But with the right mindset and tools, they’re just as easy to avoid.
If you want to design better schemas, collaborate faster, and avoid those painful refactors later, give ChartDB a try. It’s free to start and takes just one query to get going.
Thanks for reading, and happy databasing.
I’m Jonathan, co‑founder of ChartDB. A few years ago, one of my friends’ startups launched their app only to have it collapse because its database lacked proper foreign keys. Orders vanished, user data got out of sync, and recovery dragged on for days. I’ve been through similar challenges, and over the last decade of building internal tools and helping startups, I’ve learned a lot.
If you’re a startup CTO, developer, or tech architect, this post is for you. I’ll walk you through the ten most frequent database design mistakes I’ve seen and share how to dodge them from the start, using examples grounded in real scenarios, but all freshly crafted. Plus, I’ll show how ChartDB helps address each one.
Quick Note: What Is ChartDB.io?
ChartDB is an open-source tool that helps you visualise and organise your database schema with a single query. This is an ERD tool that can actually fit your workflow.
You can import schemas from Postgres, MySQL, SQL Server, SQLite, and more
Then edit tables and relationships visually
Export clean DDL, share your diagrams, and embed a live diagram into docs or Notion
10 Common Mistakes in Database Designs
Let’s discuss about the common mistakes that happen when designing a database.
1. Not Planning Ahead
Mistake: Jumping into database creation without a clear understanding of what data you need or how it evolves.
Example: Creating a Bookings table for a hotel app before clarifying how to support group bookings or multiple guests per room. Later, you realise you need to restructure everything using a GroupBookings model.
Fix: Before you write any SQL, sketch out the data model. Understand your entities, how they relate, and which queries will run most frequently. ChartDB help you visualise and adjust this on the fly.
2. Ignoring Normalisation (or Overdoing It)
Mistake: Either cramming too much into one table or overcomplicating your schema with unnecessary joins.
Example: A CourseRegistrations table stores registered course names in a single comma-separated field like 'Math, Physics, Chemistry'. You can't query individual registrations efficiently. On the flip side, splitting every small property into its own table quickly becomes overwhelming.
Fix: Normalize to third normal form (3NF) for clarity and maintainability. Only denormalize when you know it solves a specific problem. You want just enough structure without losing readability.
3. Mixing Granularities or Storing Redundant Data
Mistake: Mixing aggregated data with raw values or duplicating derived metrics.
Example: A KPI_Metrics table has rows that represent daily metrics, weekly rollups, and monthly summaries, distinguished only by a period_type column. It becomes a nightmare to query or trust.
Fix: Store raw events in one table and use separate reporting tables or views for each level of aggregation. Don’t store both age and birthdate. Keep source data clean and compute everything else as needed.
4. Poor Naming Practices
Mistake: Inconsistent, cryptic, or auto-generated names confuse developers and slow teams down.
Example: Table names like tbl_usrdata or columns like val_1, flag_a, or t1 give zero context.
Fix: Use meaningful, consistent naming like user_profiles, email_address, created_at. Stick to one style, snake_case works well for most databases. Make sure names tell a clear story.
5. Skipping Data Integrity Constraints
Mistake: Relying on your application to enforce business rules.
Example: A Payments table accepts negative values because amount isn’t validated, or transaction_id is allowed to be null. Eventually, someone inserts bad data and it causes issues downstream.
Fix: Let the database protect itself. Use NOT NULL, CHECK(amount > 0), UNIQUE, PRIMARY KEY, and FOREIGN KEY. Add constraints where they matter. It’s much easier to fix bugs before the data gets in.
6. Using Business Fields as Primary Keys
Mistake: Making columns like email, username, or product_code your primary key.
Example: In a Partners table, using company_name as the primary key seems natural until one rebrands. You’re now stuck updating every related record manually.
Fix: Always use synthetic keys like partner_id (integer or UUID) as your primary key. Then enforce uniqueness separately on business identifiers like email or company_name.
7. One Table Trying to Do It All
Mistake: Overloading a single table with too many unrelated responsibilities.
Example: You build a Notifications table that holds user alerts, system errors, and audit logs. Each has different behaviour, but now they all share the same schema.
Fix: Split them into separate tables - UserAlerts, SystemLogs, and AuditEvents. If they need to be queried together, use views or shared interfaces, but keep their data responsibilities clean.
8. Overloading Views or ETL Pipelines
Mistake: Nesting too many views or cramming too much logic into one transformation job.
Example: A dashboard pulls data from sales_view, which pulls from summary_view, which pulls from a 10-join monster view. One wrong assumption upstream breaks the entire chain.
Fix: Build layered views (raw_, stage_, final_) or modular ETL steps. Keep each layer focused and testable. If something breaks, you’ll know exactly where to look.
9. Treating Schema as Final
Mistake: Designing as if your schema won’t ever need to change.
Example: Your Profiles table has custom_field1, flag_x, misc_data, and other ambiguous fields. Over time, no one knows what’s used where.
Fix: Review your schema regularly. Refactor early. Use tools like ChartDB to identify unused fields or messy relationships. Schemas evolve, your system should too.
10. No Documentation or Testing
Mistake: When you skip documenting your schema or testing migrations, things fall apart silently.
Example: A status_code column uses numeric values from 1 to 3, but someone adds 4 and the entire billing report stops working.
Fix: Use COMMENT ON COLUMN to explain intent. Write tests for your migrations. Keep ER diagrams or schema markdowns up to date. Even light docs make a big difference when onboarding someone or debugging a bug.
How ChartDB Helps You Avoid These Mistakes
I co-founded ChartDB after seeing these problems repeat across teams and companies. So we built it to solve them directly:
Schema visualization with one query helps you catch problems before they get serious
Visual editing makes renaming, restructuring, and refactoring intuitive
Relationship detection helps identify missing foreign keys or constraints
Export options (SQL, DBML, JSON) streamline your documentation and migration process
Live diagram embedding in docs and Notion keeps everyone aligned and informed
Conclusion
Most of these mistakes are easy to make, especially when you're moving fast. I’ve made most of them myself. But with the right mindset and tools, they’re just as easy to avoid.
If you want to design better schemas, collaborate faster, and avoid those painful refactors later, give ChartDB a try. It’s free to start and takes just one query to get going.
Thanks for reading, and happy databasing.
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© 2025 ChartDB
© 2025 ChartDB
© 2025 ChartDB