What are the differences between SQL and NoSQL?

SQL and NoSQL databases differ primarily in their data models, scalability approaches, and use cases. SQL databases are relational, using structured tables with predefined schemas, and rely on SQL (Structured Query Language) for managing and querying data. NoSQL databases are non-relational, support flexible data models (like documents or key-value pairs), and prioritize scalability and flexibility over rigid structure. These differences make each type suitable for distinct scenarios.

Data Structure and Schema: SQL databases organize data into tables with rows and columns, requiring a fixed schema. For example, a user table might define columns like id, name, and email, and every entry must conform to this structure. Changes to the schema, such as adding a new column, often require migrations. In contrast, NoSQL databases like MongoDB or Cassandra allow dynamic schemas. A MongoDB document could store user data as a JSON-like object with varying fields (e.g., {name: "Alice", age: 30} or {name: "Bob", phone: "123-456"}), making it easier to adapt to changing requirements. This flexibility is useful for unstructured or semi-structured data but can lead to inconsistencies if not managed carefully.

Query Capabilities and Transactions: SQL databases excel at complex queries involving joins, aggregations, and transactions. For example, combining data from orders and customers tables using SQL’s JOIN is straightforward. They also support ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring data integrity—critical for applications like banking systems. NoSQL databases often prioritize performance and scalability over complex query support. While some NoSQL systems offer limited transaction support (e.g., MongoDB’s multi-document transactions), they generally lack native joins, requiring developers to handle relationships in application code. This trade-off suits scenarios like real-time analytics or social media feeds, where speed and horizontal scaling matter more than complex queries.

Scalability and Use Cases: SQL databases typically scale vertically (upgrading server hardware), which can become costly for large datasets. NoSQL databases like Cassandra or DynamoDB scale horizontally by distributing data across clusters, making them better for handling massive workloads. For instance, a global e-commerce platform might use NoSQL to manage high-volume user activity data, while relying on SQL for transaction processing and inventory management. Choosing between SQL and NoSQL depends on the project’s needs: SQL is ideal for structured data with strict consistency, while NoSQL suits unstructured data, rapid iteration, and scalable distributed systems.