SQL (Structured Query Language) and NoSQL (Not Only SQL) databases differ significantly in their approach to data storage, design, and use cases:

Data Storage:
• SQL: Uses tables with predefined schemas, enforcing a rigid structure.
• NoSQL: Employs various models like key-value, document, column-family, or graph, offering flexible schemas.

Design:
• SQL: Relies on relational model, emphasizing data normalization and relationships between tables.
• NoSQL: Focuses on denormalization and nested data structures, prioritizing scalability and performance.

Use Cases:
• SQL: Ideal for complex queries, transactions, and applications requiring strong consistency (e.g., financial systems, ERP).
• NoSQL: Suited for handling large volumes of unstructured data, real-time web applications, and scenarios requiring high scalability (e.g., social media, IoT).

Key Differences:
1. Scalability: NoSQL typically offers better horizontal scalability.
2. Consistency: SQL provides strong consistency, while NoSQL often uses eventual consistency.
3. Query Language: SQL uses standardized SQL; NoSQL may use proprietary query languages.
4. ACID Compliance: SQL databases are typically ACID compliant; NoSQL may sacrifice some ACID properties for performance and scalability.
5. Schema Flexibility: NoSQL allows for dynamic schemas, while SQL requires predefined schemas.
6. Join Operations: SQL excels at join operations; NoSQL often denormalizes data to avoid complex joins.

The choice between SQL and NoSQL depends on specific project requirements, considering factors like data structure, scalability needs, consistency requirements, and query complexity.