NoSQL (Not only SQL)
NoSQL refers to non-relational databases that store data differently than relational databases. NoSQL (not only SQL) means you can use more than SQL to work within a database.
The term “NoSQL” initially meant that SQL couldn't be used to extract data from the database. However, since SQL is a widely used and convenient language, NoSQL databases started to support it too. Therefore, it is now commonly understood that NoSQL means “Not Only SQL”.
What are NoSQL databases used for?
NoSQL databases are used for content management, real-time big data, mobile applications, IoT, and more. Because they can be used in many different ways, they are used in almost every industry.
In addition to being used for various purposes, many developers prefer to work with NoSQL databases. This is because data within these databases can be stored in an easier and more intuitive way.
Types of NoSQL databases
There are four popular types of NoSQL database systems: document databases, graph databases, key-value databases, and wide-column stores. Each uses a different type of data model.
Document databases - in these databases, semi-structured data and descriptions of that data are stored in document format.
Graph databases - graph databases organise data as nodes (similar to rows in a relational database) and edges, representing connections between nodes. Because the graph system stores the relationship between nodes, it can support richer views of data relationships.
Key-value databases - these databases implement a simple data model that links a unique key to an associated value. Because this model is so simple, it can be used to develop highly scalable and powerful applications.
Wide-column stores - these databases use tables, columns, and rows like relational database tables, but column names and formats can vary from row to row in a single table.
SQL vs. NoSQL
The main difference is that one is used for relational databases (SQL), and the other is used for non-relational databases (NoSQL). SQL databases are more for general use, while NoSQL databases are for specific purposes.
Advantages of a NoSQL database
Using NoSQL databases has several advantages, including:
Functional - NoSQL databases simplify application development, especially interactive real-time web apps that use a REST API and web services.
Flexible - With NoSQL, data can be stored in a free-form manner.
Scalable - They provide scalability for larger datasets or when more traffic needs to be supported.
Disadvantages of a NoSQL database
Some disadvantages of using a NoSQL database include:
Own syntax - Each NoSQL database has its own syntax for querying and managing data.
No standard - There are many NoSQL databases, and there is still no standard like there is with relational databases.
Difficult to ensure data integrity - The ability to ensure data integrity that is built into relational and SQL database systems is removed due to the lack of a rigid database schema and constraints.
NoSQL, or "not only SQL," is a type of database management system that does not use traditional relational database structures. Instead, it uses alternative data models, such as document-oriented, key-value, or graph databases.
NoSQL databases can handle large amounts of unstructured or semi-structured data, making them useful for applications that require high scalability and availability. They can also be more flexible and easier to set up and maintain than traditional relational databases.
NoSQL databases may not be appropriate for applications that require complex queries or transactions. They can also have less support and fewer tools than traditional relational databases.
Popular NoSQL databases include MongoDB, Cassandra, Redis, and Amazon DynamoDB.
NoSQL databases do not use the traditional relational database model, whereas SQL databases do. NoSQL databases can also be more flexible and scalable, but may lack some of the features and guarantees of SQL databases, such as transactional consistency.
It depends on the use case. NoSQL databases may be better suited for applications that require scalability and flexibility, while SQL databases may be better for applications that require strict data consistency and complex queries.
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