The difference between SQL (Structured Query Language) and NoSQL (Not Only SQL) databases lies in their structure, scalability, and the way data is stored and queried.

SQL Databases:

1. Structured Data: SQL databases use a fixed schema, where data is stored in tables with rows and columns.
2. ACID Compliance: Ensures reliability with Atomicity, Consistency, Isolation, and Durability, making SQL databases a good choice for critical transactions.
3. Joins & Relations: Data in SQL databases can be related using foreign keys, which allow complex queries across multiple tables (joins).
4. Scalability: Typically, SQL databases are vertically scalable (upgrading the hardware), though some modern SQL databases also support horizontal scaling.

NoSQL Databases:

1. Flexible Data Models: NoSQL databases store data in a variety of formats, including documents (e.g., MongoDB), key-value pairs (e.g., Redis), graphs (e.g., Neo4j), or column-based (e.g., Cassandra). These allow for schema-less structures.
2. Eventual Consistency: Many NoSQL databases prioritize availability and partition tolerance over consistency (CAP theorem), providing eventual consistency instead of strong consistency.
3. Horizontal Scalability: NoSQL databases are designed to scale horizontally across distributed servers, making them better for large-scale, distributed systems.
4. Faster for Certain Operations: NoSQL databases often provide faster read and write operations for specific tasks, especially in high-throughput systems.

When to Use SQL:

• Structured Data: When your data is highly structured and the relationships between data are well-defined.
• Consistency is Critical: When ACID transactions are required (e.g., financial applications).
• Complex Queries: When you need to perform complex queries, such as joins between multiple tables.

When to Use NoSQL:

• Unstructured or Semi-Structured Data: Ideal when dealing with large volumes of unstructured data (e.g., logs, social media data).
• High-Speed, High-Volume Operations: For scenarios where speed is crucial and the data structure is flexible, such as in real-time analytics or caching systems.
• Scalability: When you need to handle massive data loads across distributed systems (e.g., large-scale applications like social networks).