Database Design Best Practices – Complete Cheat Sheet

Introduction

Database design is the process of organizing data to store information efficiently while ensuring data integrity, accessibility, and scalability. Good database design is crucial for application performance, data consistency, and long-term maintainability. Poor design leads to data redundancy, inconsistencies, slow queries, and difficult maintenance.

Core Concepts & Principles

Fundamental Principles

• Data Integrity: Ensure accuracy and consistency of data
• Normalization: Eliminate redundancy and dependency issues
• Performance Optimization: Design for efficient data retrieval and storage
• Scalability: Plan for future growth and changing requirements
• Security: Protect sensitive data through proper access controls

Key Design Goals

• Minimize data redundancy
• Maximize data consistency
• Optimize query performance
• Ensure data security
• Maintain referential integrity
• Support business requirements

Database Design Process

Phase 1: Requirements Analysis

1. Identify Business Requirements

   • Understand what data needs to be stored
   • Determine how data will be used
   • Identify reporting and analytics needs

2. Define Data Sources

   • List all data inputs
   • Identify data relationships
   • Document data constraints

3. Performance Requirements

   • Expected transaction volume
   • Query response time requirements
   • Concurrent user load

Phase 2: Conceptual Design

1. Create Entity-Relationship Diagram (ERD)

   • Identify entities (objects/concepts)
   • Define relationships between entities
   • Specify cardinality and participation

2. Define Attributes

   • List properties for each entity
   • Identify primary and foreign keys
   • Specify data types and constraints

Phase 3: Logical Design

1. Apply Normalization Rules

   • First Normal Form (1NF): Eliminate repeating groups
   • Second Normal Form (2NF): Remove partial dependencies
   • Third Normal Form (3NF): Eliminate transitive dependencies

2. Optimize for Performance

   • Consider denormalization where appropriate
   • Plan indexing strategy
   • Design for common query patterns

Phase 4: Physical Design

1. Choose Storage Engine

   • Consider ACID properties requirements
   • Evaluate performance characteristics
   • Plan for backup and recovery

2. Implement Security Measures

   • Design user roles and permissions
   • Plan data encryption strategy
   • Implement audit trails

Normalization Forms

Data Types & Constraints

Choosing Data Types

• Text Fields: Use appropriate length limits (VARCHAR vs TEXT)
• Numbers: Choose precise types (INT, DECIMAL, FLOAT)
• Dates: Use proper date/time types, consider time zones
• Boolean: Use BOOLEAN type for true/false values
• Large Objects: Handle BLOBs and CLOBs carefully

Essential Constraints

• Primary Key: Unique identifier for each row
• Foreign Key: Maintains referential integrity
• NOT NULL: Prevents empty critical fields
• UNIQUE: Ensures uniqueness across columns
• CHECK: Validates data against business rules

Indexing Strategies

Types of Indexes

Indexing Best Practices

• Index frequently queried columns
• Avoid over-indexing (impacts INSERT/UPDATE performance)
• Consider composite indexes for multi-column queries
• Monitor and maintain index usage statistics
• Remove unused indexes

Relationship Design

One-to-One (1:1)

• Use when splitting large tables
• Consider merging tables if possible
• Foreign key can be in either table

One-to-Many (1:M)

• Most common relationship type
• Foreign key goes in the “many” table
• Use for hierarchical data structures

Many-to-Many (M:M)

• Requires junction/bridge table
• Store additional relationship data in junction table
• Consider performance implications

Performance Optimization Techniques

Query Optimization

• Use Appropriate Joins: Understand INNER, LEFT, RIGHT, FULL joins
• Limit Result Sets: Use WHERE clauses effectively
• **Avoid SELECT ***: Specify needed columns only
• Use Subqueries Wisely: Sometimes JOINs are more efficient

Table Design for Performance

• Partitioning: Split large tables horizontally or vertically
• Archiving: Move old data to separate tables
• Caching: Implement application-level caching
• Read Replicas: Separate read and write operations

Security Best Practices

Access Control

• Implement least privilege principle
• Use role-based access control (RBAC)
• Regularly audit user permissions
• Remove unused accounts promptly

Data Protection

• Encrypt sensitive data at rest and in transit
• Use strong authentication mechanisms
• Implement data masking for non-production environments
• Plan for data retention and deletion policies

Common Challenges & Solutions

Challenge: Over-Normalization

Problem: Too many joins slow down queries Solution: Strategic denormalization for frequently accessed data

Challenge: Under-Normalization

Problem: Data redundancy and inconsistency Solution: Apply normalization rules systematically

Challenge: Poor Indexing

Problem: Slow query performance Solution: Analyze query patterns and create targeted indexes

Challenge: Scalability Issues

Problem: Database can’t handle growth Solution: Plan for horizontal/vertical scaling from the start

Challenge: Data Integrity Issues

Problem: Inconsistent or invalid data Solution: Implement proper constraints and validation rules

Database-Specific Considerations

Relational Databases (MySQL, PostgreSQL, SQL Server)

• ACID compliance is standard
• Strong consistency guarantees
• Mature tooling and documentation
• Good for complex relationships

NoSQL Databases (MongoDB, Cassandra, DynamoDB)

• Flexible schema design
• Horizontal scalability
• Eventually consistent models
• Good for large-scale, distributed applications

Best Practices Checklist

Design Phase

• [ ] Document all business requirements thoroughly
• [ ] Create comprehensive ERD before implementation
• [ ] Apply normalization rules systematically
• [ ] Plan for future scalability needs
• [ ] Design security measures from the start

Implementation Phase

• [ ] Use appropriate data types and constraints
• [ ] Implement proper indexing strategy
• [ ] Set up referential integrity constraints
• [ ] Create meaningful naming conventions
• [ ] Document schema changes and decisions

Maintenance Phase

• [ ] Monitor query performance regularly
• [ ] Update statistics and rebuild indexes
• [ ] Review and optimize slow queries
• [ ] Backup and test recovery procedures
• [ ] Audit security permissions periodically

Naming Conventions

Tables

• Use singular nouns (Customer, not Customers)
• Use clear, descriptive names
• Avoid abbreviations when possible
• Use consistent casing (snake_case or PascalCase)

Columns

• Use descriptive names
• Include data type hints when helpful
• Avoid reserved keywords
• Use consistent prefixes for related columns

Indexes

• Include table name and column(s)
• Use descriptive suffixes (_idx, _pk, _fk)
• Follow consistent naming pattern

Tools & Resources

Design Tools

• ERD Tools: Lucidchart, draw.io, MySQL Workbench
• Database Modeling: ERwin, PowerDesigner, DbSchema
• Version Control: Flyway, Liquibase for schema migrations

Performance Tools

• Query Analyzers: Built-in EXPLAIN commands
• Monitoring: Database-specific monitoring tools
• Profiling: Application-level database profilers

Learning Resources

• Books: “Database Design for Mere Mortals” by Michael Hernandez
• Online Courses: Database design courses on Coursera, Udemy
• Documentation: Official database vendor documentation
• Communities: Stack Overflow, Reddit r/database
• Blogs: Use The Index Luke, High Scalability

Quick Reference Commands

SQL DDL Examples

-- Create table with constraints
CREATE TABLE customers (
    customer_id INT PRIMARY KEY AUTO_INCREMENT,
    email VARCHAR(255) NOT NULL UNIQUE,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Add foreign key constraint
ALTER TABLE orders 
ADD CONSTRAINT fk_customer 
FOREIGN KEY (customer_id) REFERENCES customers(customer_id);

-- Create index
CREATE INDEX idx_customer_email ON customers(email);

Performance Analysis

-- Analyze query performance
EXPLAIN SELECT * FROM customers WHERE email = 'example@email.com';

-- Check index usage
SHOW INDEX FROM customers;

This cheatsheet provides a comprehensive foundation for database design. Remember that specific implementations may vary depending on your chosen database system and unique business requirements.