SQL vs NoSQL Databases

Data storage isn't one-size-fits-all. The industry is split between Relational (SQL) and Non-Relational (NoSQL) databases.

SQL (Relational)

Examples: PostgreSQL, MySQL, SQLite.

• Structure: Tables with Rows and Columns. Like Excel.
• Schema: Strict. You must define columns (Name, Age, Email) before adding data.
• Relations: Great at joining data. "Find all Orders belonging to Users who live in New York."
• ACID: Guaranteed data consistency. If a payment fails halfway, the whole transaction rolls back.

Best for: Financial systems, E-commerce, structured business data.

NoSQL (Non-Relational)

Examples: MongoDB, Redis, Cassandra.

• Structure: Documents (JSON-like).
• Schema: Flexible. One user can have an Address, another can have GPS_Coordinates. You can add fields anytime.
• Scaling: Designed to scale Horizontally (add more cheap servers) rather than Vertically (buy a bigger server).

Best for: Social media feeds, Real-time analytics, Content Management Systems, Caching.

The CAP Theorem

You can't have it all.

• SQL usually prioritizes Consistency (Data is always correct).
• NoSQL usually prioritizes Availability (The system never goes down, even if data is slightly old).