NoSQL vs SQL Databases

Feature	SQL (Relational)	NoSQL (Non-Relational)
Schema	Fixed, predefined schema	Flexible, schema-less
Scalability	Vertical (adding more CPU/RAM)	Horizontal (adding more servers)
Data Model	Tables, rows, columns	Key-value, document, column-family, graph
Query Language	SQL (Structured Query Language)	Varies (APIs, JSON queries, etc.)
Joins & Relationships	Strong support for relational joins	Not designed for complex joins
Performance	Optimized for structured data	Optimized for unstructured, high-speed data

When to Use a Relational Database

a. Structured and Consistent Data

When data follows a well-defined schema and relationships between entities are important.

Example:

Banking system: Customers, Accounts, Transactions
E-commerce: Orders, Products, Users

These systems need ACID (Atomicity, Consistency, Isolation, Durability) guarantees to ensure data integrity.

CREATE TABLE orders (
    order_id SERIAL PRIMARY KEY,
    user_id INT REFERENCES users(id),
    total_amount DECIMAL(10,2),
    order_date TIMESTAMP
);

b. Complex Queries and Relationships

Use SQL for joins, aggregations, and transactions across multiple tables.

Example:
A logistics app querying shipments by region, customer, and delivery status.

c. Strong Consistency is Required

In financial or transactional applications, data must always be consistent.
Example: updating an account balance must happen atomically.

d. Mature Tooling and Standardization

Relational databases have decades of development, robust security, and strong support for reporting, analytics, and migrations.

When to Use a NoSQL Database

a. Unstructured or Semi-Structured Data

When data varies in format or evolves frequently.

Example:

A social media app where posts can include text, images, or videos.
MongoDB document for a post:

{
  "user": "alice",
  "content": "Hello world!",
  "likes": 25,
  "comments": [
    {"user": "bob", "text": "Hi!"},
    {"user": "eve", "text": "Welcome!"}
  ]
}

b. High Scalability and Availability

When you need to scale horizontally across many servers or regions. Example:

A large-scale messaging platform using Cassandra or DynamoDB for fast distributed writes.
A caching layer using Redis to handle millions of requests per second.

c. Flexible or Rapidly Changing Schema

When the application evolves quickly and schema updates are frequent. Example:

A startup experimenting with product data structures.
A content management system with different fields per document.

d. Specific Data Models

NoSQL includes several types optimized for different data relationships:

Key-Value Stores: Redis, DynamoDB ⇒ caching, sessions.
Document Stores: MongoDB, CouchDB ⇒ flexible JSON-like data.
Column Stores: Cassandra, HBase ⇒ analytical queries on large datasets.
Graph Databases: Neo4j ⇒ complex relationships (e.g., social networks, recommendation engines).

Hybrid Approach

Many modern systems use both RDBMS and NoSQL depending on the component:

Use PostgreSQL for transactional data (user accounts, payments).
Use MongoDB for content and activity feeds.
Use Redis for caching and session management

My Obsidian Notes

Explorer