What are the differences between NoSQL and relational databases?

NoSQL and relational databases differ primarily in how they structure, store, and manage data. Relational databases, like MySQL or PostgreSQL, organize data into tables with rows and columns, requiring a fixed schema defined before inserting data. Each table relates to others through keys (e.g., foreign keys), enabling complex joins for querying connected data. In contrast, NoSQL databases (e.g., MongoDB, Cassandra) use flexible schemas and support various data models like documents, key-value pairs, wide-column stores, or graphs. For example, a document database might store JSON-like records with nested data, avoiding the need for predefined tables. This flexibility makes NoSQL better suited for unstructured or rapidly changing data.

Another key difference is scalability. Relational databases typically scale vertically by upgrading hardware (e.g., adding more CPU or RAM), which can become costly and hit physical limits. NoSQL databases are designed for horizontal scaling, meaning they distribute data across multiple servers (sharding) to handle large volumes of traffic or data. For instance, Cassandra uses a peer-to-peer architecture to spread data across clusters, making it ideal for high-write workloads like IoT sensor data. However, horizontal scaling in NoSQL often sacrifices strict consistency for availability, following the CAP theorem. Relational databases prioritize ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring data integrity but limiting scalability for globally distributed systems.

The choice between the two also depends on use cases. Relational databases excel in applications requiring complex queries, transactions (e.g., banking systems), or structured data with clear relationships. SQL’s standardized language simplifies writing joins and aggregations. NoSQL is better for scenarios needing rapid iteration, such as real-time analytics, content management, or handling semi-structured data (e.g., social media posts with varying fields). For example, MongoDB’s schema-less design allows developers to modify document structures without downtime. However, NoSQL may require denormalizing data or managing consistency manually, increasing application-side complexity. Developers must weigh trade-offs between structure, scalability, and query needs when choosing a database type.