1.Data Model:
Relational Databases (RDBMS):Organize data into structured tables with rows and columns. Data relationships are established through keys.
Non-relational Databases (NoSQL):Use various data models, such as document-oriented (like MongoDB), key-value pairs (like Redis), wide-column stores, or graph databases. The structure is more flexible and can adapt to different types of data.
2.Schema:
Relational Databases:Require a predefined schema where the structure of tables and relationships between them is defined before inserting data.
Non-relational Databases:Typically have a dynamic or schema-less approach, allowing flexibility to add fields on the fly.
3.Scalability:
Relational Databases:Traditionally scale vertically by adding more power to a single server (vertical scaling). Some modern relational databases also support horizontal scaling across multiple servers.
Non-relational Databases:Primarily designed for horizontal scaling, allowing the distribution of data across multiple servers or nodes.
4.Use Cases:
Relational Databases: Well-suited for complex queries and transactions where data integrity is critical. Commonly used in applications with well-defined structures like financial systems.
Non-relational Databases:Ideal for scenarios with dynamic and evolving data, high scalability requirements, and where a quick and flexible data model is essential, such as in web applications with varying data types.
5.Examples:
Relational Databases:MySQL, PostgreSQL, Oracle, Microsoft SQL Server.
Non-relational Databases:MongoDB, Cassandra, Redis, CouchDB.
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