SQL vs NoSQL

1. Definition

• SQL (Relational Databases) :
  • Uses structured tables with rows and columns.
  • Data is structured and relationships between tables are well-defined.
  • Examples: MySQL, Postgres, Oracle, SQL Server
• NoSQL (Non-Relational Databases) :
  • Stores data in non-tabular format: documents, key-value pairs, graphs or wide-columns.
  • Flexible schema: data can be semi-structured or unstructured.
  • Examples: MongoDB, Cassandra, Redis, DynamoDB.

2. Schema

Feature	SQL	NoSQL
Schema	Fixed schema (columns and types defined beforehand)	Dynamic schema (fields can vary by record)
Flexibility	Less flexible	Highly flexible

3. Data Structure

• SQL : Table based -> rows and columns.
• NoSQL : Can be document-based(JSONs, key-value, graphs, or column-family).

4. Scalability

• SQL : Vertically Scalable -> scale by upgrading hardware(CPU, RAM).
• NoSQL : Horizontally Scalable -> scale by adding more servers/nodes.

5. Transactions & Consistency

• SQL :
  • ACID compliant (Atomicity, Consistency, Isolation, Durability)
  • Good for application where data integrity is critical (eg. banking, finance).
• NoSQL :
  • Often BASE(Basically Available, Soft State, Eventual Consistency).
  • Good for application needing high availability and big data handling(social media, real-time analytics).

6. Query Language

• SQL : Structured Query Language (SELECT, INSERT, UPDATE, DELETE).
• NoSQL : Query depends on database type (MongoDB uses JSON-like queries, Redis uses key commands).

7. When to Use

Use Case	SQL	NoSQL
Structured data with relationships	✅	❌
Complex queries & transactions	✅	❌
Rapid development with evolving schema	❌	✅
Large-scale, distributed data	❌	✅
Real-time analytics	❌	✅

Quick Analogy

• SQL: Like a well-organized library → every book has a fixed shelf, category, and code.
• NoSQL: Like a digital warehouse → items can be stored anywhere, in any format, and retrieved quickly.