Databases

SQL vs NoSQL: When to Use Each

SQL Databases

SQL (Structured Query Language) databases are relational databases that store data in structured tables with predefined schemas.

Characteristics:

• Data stored in tables with rows and columns
• ACID compliance (Atomicity, Consistency, Isolation, Durability)
• Predefined schema with strict data types
• Strong consistency guarantees
• Mature ecosystem with standardized query language

When to Use SQL:

• Complex queries with multiple joins are required
• Strong consistency and ACID properties are critical
• Well-defined, stable data structure
• Financial systems, banking, or accounting applications
• Applications requiring complex transactions
• When you need standardized query language (SQL)
• Regulatory compliance requirements
• Small to medium-scale applications with predictable growth

Advantages:

• ACID compliance ensures data integrity
• Mature technology with extensive tooling
• Standardized SQL query language
• Strong consistency guarantees
• Excellent for complex queries and joins
• Well-established best practices
• Strong community support and documentation

Disadvantages:

• Rigid schema makes changes difficult
• Vertical scaling can be expensive
• Performance can degrade with very large datasets
• Less suitable for unstructured data
• Can become bottleneck in distributed systems

NoSQL Databases

NoSQL databases are non-relational databases designed for flexibility, scalability, and handling unstructured data.

Types of NoSQL:

• Document (MongoDB, CouchDB): JSON-like documents
• Key-Value (Redis, DynamoDB): Simple key-value pairs
• Column-family (Cassandra, HBase): Wide column stores
• Graph (Neo4j, Amazon Neptune): Node and edge relationships

When to Use NoSQL:

• Rapid development with changing requirements
• Large-scale applications requiring horizontal scaling
• Unstructured or semi-structured data
• Real-time applications (gaming, IoT, social media)
• Big data analytics and data warehousing
• Content management systems
• Caching and session storage
• When eventual consistency is acceptable

Advantages:

• Flexible schema allows easy data model changes
• Excellent horizontal scalability
• High performance for simple queries
• Better suited for distributed systems
• Can handle various data types (JSON, binary, etc.)
• Often more cost-effective for large-scale applications
• Built for modern web applications

Disadvantages:

• Limited query capabilities compared to SQL
• Eventual consistency can lead to data inconsistencies
• Less mature ecosystem and tooling
• Lack of standardization across different NoSQL databases
• Limited support for complex transactions
• Potential for data duplication

Decision Matrix

Factor	SQL	NoSQL
Data Structure	Structured, relational	Flexible, various formats
Scalability	Vertical (expensive)	Horizontal (cost-effective)
Consistency	Strong (ACID)	Eventual (BASE)
Query Complexity	Complex joins, aggregations	Simple queries, limited joins
Schema Changes	Difficult, requires migrations	Easy, flexible
Performance	Excellent for complex queries	Excellent for simple operations
Use Cases	Financial, ERP, CRM	Social media, IoT, gaming

ACID

Atomicity

Series of Database operation such that all operations occur at the same time or none occur at all. At end of making a transaction either the changes Abort or Commits.

Consistency

Before and after Transaction the DB should be consistent.

Isolation

Means multiple transactions can happen without interrupting each other.

Durability

Means even if DB crashes the Data should not die. THey should still be retrievable. More keywords similar to this: Persistent Database.