SQL vs NoSQL

Feature	SQL (Relational Databases)	NoSQL (Non-Relational Databases)
Data Model	Structured, tabular format with rows and columns.	Flexible, schema-less data models (Document, Key-Value, Columnar, Graph).
Schema	Predefined schema with strict relationships.	Dynamic schema, allows unstructured or semi-structured data.
Scalability	Vertical scaling (adding more CPU, RAM, storage to a single server).	Horizontal scaling (distributing data across multiple servers).
Query Language	Uses SQL (Structured Query Language) for queries.	Uses various query languages (e.g., JSON-like queries for MongoDB, CQL for Cassandra).
Transactions	Supports ACID (Atomicity, Consistency, Isolation, Durability) for strong consistency.	Supports BASE (Basically Available, Soft state, Eventually consistent) for scalability and performance.
Use Cases	Best for structured data, complex queries, and transactional systems (e.g., banking, ERP, CRM).	Best for handling large-scale, unstructured, and semi-structured data (e.g., big data, IoT, real-time analytics).
Examples	MySQL, PostgreSQL, Oracle, SQL Server.	MongoDB, Cassandra, Redis, Neo4j, DynamoDB.
Performance	Optimized for complex queries with JOINs and aggregations.	Optimized for high-speed reads/writes and distributed storage.
Flexibility	Rigid structure; changes require altering schema and migrations.	Highly flexible; allows dynamic changes without schema migrations.