SQL databases are relational and use structured query language for defining and manipulating data, while NoSQL databases are non-relational and use various data models such as document, key-value, graph, and column-family stores.
SQL databases are relational databases that use structured query language (SQL) for defining and manipulating data. They have a predefined schema and are table-based, meaning data is organized in rows and columns. Examples of SQL databases include MySQL, PostgreSQL, Oracle, and SQL Server.
NoSQL databases, on the other hand, are non-relational and can store and manage data in a variety of ways other than the tabular relations used in relational databases. They are schema-less and can handle unstructured data. Examples of NoSQL databases include MongoDB, Cassandra, Redis, and Couchbase.
One of the primary differences is the way data is stored. SQL databases store data in tables with rows and columns, making them ideal for complex queries and transactions. NoSQL databases store data in various formats, such as key-value pairs, documents, graphs, or wide-column stores, allowing for more flexibility in handling different types of data.
SQL databases are vertically scalable, meaning they can be scaled up by increasing the hardware capacity (CPU, RAM, SSD) of a single server. NoSQL databases are horizontally scalable, meaning they can be scaled out by adding more servers to the database cluster.
Transactions in SQL databases are ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring reliable transactions and data integrity. NoSQL databases often follow the BASE (Basically Available, Soft state, Eventual consistency) model, providing high availability and scalability at the cost of immediate consistency.
SQL databases are suitable for applications that require complex queries, multi-row transactions, and consistency, such as banking systems, CRM, and ERP systems. NoSQL databases are ideal for applications that handle large volumes of unstructured data, need scalability, and can tolerate eventual consistency, such as social networks, real-time analytics, and IoT applications.
SQL databases use a fixed schema, which means the structure of the data is defined before any data can be inserted. Changes to the schema can be complex and disruptive. NoSQL databases offer a dynamic schema, allowing for easy modifications and accommodating evolving data structures.
The query language is another differentiator. SQL databases use SQL for querying and managing data, which is standardized and widely known. NoSQL databases may use different query languages or APIs specific to the database type, such as MongoDB's query language for document-based queries or Cassandra Query Language (CQL) for wide-column stores.
Data integrity and normalization are central to SQL databases, which emphasize eliminating data redundancy and ensuring data relationships through foreign keys. NoSQL databases often denormalize data to optimize read performance and speed, which can lead to data duplication.
In summary, SQL and NoSQL databases serve different purposes and are chosen based on the specific needs of the application. SQL databases are preferred for structured data and complex queries, while NoSQL databases are favored for their flexibility, scalability, and ability to handle unstructured data.