Database Types Complete Reference Guide & Selection Cheatsheet

Introduction

Database types represent different approaches to storing, organizing, and retrieving data. Choosing the right database type is crucial for application performance, scalability, and development efficiency. With the explosion of data variety and volume, understanding when to use relational databases versus NoSQL alternatives can make or break your project’s success.

Core Concepts & Principles

ACID Properties (Relational Databases)

• Atomicity: Transactions are all-or-nothing
• Consistency: Data remains valid after transactions
• Isolation: Concurrent transactions don’t interfere
• Durability: Committed data persists through system failures

BASE Properties (NoSQL Databases)

• Basically Available: System remains operational
• Soft State: Data consistency isn’t guaranteed at all times
• Eventually Consistent: System will become consistent over time

CAP Theorem

You can only guarantee two of three properties:

• Consistency: All nodes see the same data simultaneously
• Availability: System remains operational
• Partition Tolerance: System continues despite network failures

Database Type Categories

1. Relational Databases (SQL)

Characteristics

• Structured data in tables with rows and columns
• ACID compliance
• SQL query language
• Predefined schema
• Strong consistency

Popular Systems

• MySQL: Web applications, e-commerce
• PostgreSQL: Complex queries, JSON support
• Oracle: Enterprise applications
• SQL Server: Microsoft ecosystem
• SQLite: Embedded applications

Best Use Cases

• Financial systems requiring ACID compliance
• Applications with complex relationships
• Reporting and analytics
• Traditional business applications
• Applications requiring strong consistency

2. Document Databases (NoSQL)

Characteristics

• Store semi-structured data as documents (JSON, BSON, XML)
• Flexible schema
• Horizontal scaling
• Query by document content

Popular Systems

• MongoDB: General-purpose document storage
• CouchDB: Offline-first applications
• Amazon DocumentDB: AWS-managed MongoDB alternative

Best Use Cases

• Content management systems
• Product catalogs
• User profiles and personalization
• Real-time web applications
• Applications with evolving data structures

3. Key-Value Databases (NoSQL)

Characteristics

• Simple key-value pairs
• Extremely fast lookups
• Minimal overhead
• Horizontal scaling

Popular Systems

• Redis: In-memory caching, session storage
• Amazon DynamoDB: Serverless applications
• Riak: Distributed systems
• Voldemort: LinkedIn’s distributed storage

Best Use Cases

• Caching layers
• Session management
• Shopping carts
• User preferences
• Real-time recommendations

4. Column-Family Databases (NoSQL)

Characteristics

• Data stored in column families (like tables)
• Optimized for write-heavy workloads
• Horizontal scaling across commodity hardware
• Eventual consistency

Popular Systems

• Cassandra: Large-scale distributed systems
• HBase: Hadoop ecosystem integration
• Amazon SimpleDB: AWS managed service

Best Use Cases

• Time-series data
• IoT sensor data
• Messaging systems
• Large-scale analytics
• Applications requiring high write throughput

5. Graph Databases (NoSQL)

Characteristics

• Data represented as nodes and relationships
• Optimized for traversing connections
• Flexible schema for relationships
• Complex relationship queries

Popular Systems

• Neo4j: Property graph database
• Amazon Neptune: AWS managed graph service
• ArangoDB: Multi-model database
• OrientDB: Document-graph hybrid

Best Use Cases

• Social networks
• Recommendation engines
• Fraud detection
• Network analysis
• Knowledge graphs

6. Time-Series Databases

Characteristics

• Optimized for time-stamped data
• Efficient storage and compression
• Built-in time-based operations
• High ingestion rates

Popular Systems

• InfluxDB: Monitoring and IoT
• TimescaleDB: PostgreSQL extension
• OpenTSDB: Built on HBase
• Prometheus: Monitoring and alerting

Best Use Cases

• System monitoring
• IoT sensor data
• Financial market data
• Application performance monitoring
• Industrial equipment tracking

7. Vector Databases

Characteristics

• Store and query high-dimensional vectors
• Similarity search capabilities
• Machine learning integration
• Semantic search support

Popular Systems

• Pinecone: Managed vector database
• Weaviate: Open-source vector search
• Chroma: AI-native database
• Milvus: Open-source vector database

Best Use Cases

• AI and machine learning applications
• Semantic search
• Recommendation systems
• Image and video search
• Natural language processing

Database Comparison Table

Step-by-Step Database Selection Process

Phase 1: Requirements Analysis

1. Define data structure requirements

   • Is your data highly structured or flexible?
   • Do you need complex relationships?
   • What’s your data volume and growth rate?

2. Identify access patterns

   • Read vs write ratio
   • Query complexity
   • Response time requirements
   • Concurrent user load

3. Determine consistency requirements

   • Do you need immediate consistency?
   • Can you accept eventual consistency?
   • What are the business implications of inconsistency?

Phase 2: Technical Evaluation

1. Assess scalability needs

   • Current data size
   • Expected growth rate
   • Geographic distribution requirements
   • Budget constraints

2. Evaluate team expertise

   • Existing database skills
   • Learning curve acceptance
   • Operational complexity tolerance
   • Available support resources

Phase 3: Decision Matrix

1. Create weighted criteria

   • Performance requirements (weight: high/medium/low)
   • Scalability needs (weight: high/medium/low)
   • Consistency requirements (weight: high/medium/low)
   • Team expertise (weight: high/medium/low)

2. Score each database type (1-10 scale)

3. Calculate weighted scores

4. Select top 2-3 candidates for prototyping

Common Challenges & Solutions

Challenge: Data Consistency Issues

Problem: Distributed systems struggle with maintaining consistency Solutions:

• Implement eventual consistency patterns
• Use distributed transaction protocols (2PC, Saga)
• Design for idempotent operations
• Implement conflict resolution strategies

Challenge: Query Performance Degradation

Problem: Queries become slow as data grows Solutions:

• Implement proper indexing strategies
• Use query optimization techniques
• Consider read replicas for read-heavy workloads
• Implement caching layers

Challenge: Vendor Lock-in

Problem: Difficulty migrating between database systems Solutions:

• Use database abstraction layers
• Implement standard query languages where possible
• Plan migration strategies upfront
• Use open-source alternatives when feasible

Challenge: Operational Complexity

Problem: Managing multiple database types increases complexity Solutions:

• Standardize on fewer database types
• Implement proper monitoring and alerting
• Use managed database services
• Invest in automation and Infrastructure as Code

Best Practices & Practical Tips

Database Design Best Practices

• Start with your access patterns: Design around how you’ll query the data
• Denormalize wisely: In NoSQL, some redundancy is acceptable for performance
• Plan for growth: Consider future scaling needs early
• Index strategically: Create indexes for your most common queries
• Monitor query performance: Set up alerts for slow queries

Operational Best Practices

• Backup regularly: Implement automated backup strategies
• Test disaster recovery: Regularly test your recovery procedures
• Monitor key metrics: Track performance, capacity, and error rates
• Use connection pooling: Optimize database connections
• Implement security measures: Use encryption, access controls, and auditing

Development Best Practices

• Use database migrations: Version control your schema changes
• Implement connection retry logic: Handle temporary connection failures
• Cache frequently accessed data: Reduce database load
• Batch operations when possible: Improve write performance
• Use prepared statements: Prevent SQL injection attacks

Migration Strategies

SQL to NoSQL Migration

1. Analysis phase: Map existing relationships to document/key-value structures
2. Dual-write approach: Write to both systems during transition
3. Gradual migration: Move features incrementally
4. Data validation: Ensure data consistency between systems

NoSQL to SQL Migration

1. Schema design: Create normalized tables from denormalized documents
2. Data transformation: Convert documents to relational format
3. Relationship reconstruction: Rebuild foreign key relationships
4. Query rewriting: Convert NoSQL queries to SQL

Performance Optimization Techniques

Relational Databases

• Create appropriate indexes
• Optimize query execution plans
• Use stored procedures for complex operations
• Implement database partitioning
• Configure connection pooling

Document Databases

• Design documents for query patterns
• Use compound indexes effectively
• Implement proper sharding strategies
• Optimize document size
• Use aggregation pipelines efficiently

Key-Value Stores

• Use consistent hashing for distribution
• Implement proper key naming conventions
• Batch operations when possible
• Use appropriate data serialization
• Configure memory settings optimally

Monitoring & Maintenance

Key Metrics to Monitor

• Query response time: Track slow queries
• Throughput: Monitor reads/writes per second
• Resource utilization: CPU, memory, disk usage
• Connection pool status: Active/idle connections
• Error rates: Failed queries and timeouts

Maintenance Tasks

• Regular backups: Automated and tested
• Index maintenance: Rebuild fragmented indexes
• Statistics updates: Keep query optimizer informed
• Log file management: Prevent disk space issues
• Security updates: Keep database software current

Resources for Further Learning

Official Documentation

• MySQL: https://dev.mysql.com/doc/
• PostgreSQL: https://www.postgresql.org/docs/
• MongoDB: https://docs.mongodb.com/
• Redis: https://redis.io/documentation
• Cassandra: https://cassandra.apache.org/doc/

Online Courses

• Database Systems (Stanford CS145)
• MongoDB University courses
• Redis University
• AWS Database Training
• Google Cloud Database courses

Books

• “Database System Concepts” by Silberschatz
• “NoSQL Distilled” by Martin Fowler
• “High Performance MySQL” by Baron Schwartz
• “MongoDB: The Definitive Guide” by Shannon Bradshaw
• “Redis in Action” by Josiah Carlson

Tools & Resources

• Database design tools: dbdiagram.io, Lucidchart
• Performance monitoring: New Relic, DataDog, Prometheus
• Benchmarking: sysbench, YCSB, TPC benchmarks
• Migration tools: AWS DMS, MongoDB Compass, phpMyAdmin
• Communities: Stack Overflow, Reddit (r/Database), Database communities on Discord

Last updated: May 2025 | This cheatsheet provides practical guidance for database selection and implementation. Always test thoroughly in your specific environment before making production decisions.