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Relational Databases (RDBMS)
Relational databases organize data into structured tables with rows and columns. Each table represents an entity, and tables can be linked through relationships defined by primary and foreign keys. Relational databases use Structured Query Language (SQL) for defining, querying, and managing data.
Key Features:
- Predefined schema
- ACID compliance (ensures data reliability)
- Strong data integrity and consistency
Examples: MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server.
Non-Relational Databases (NoSQL)
Non-relational databases store data in a flexible format that can handle structured, semi-structured, or unstructured data. They are designed for scalability and performance in handling large volumes of data and can use formats such as documents, key-value pairs, graphs, or wide columns.
Key Features:
- Flexible schema
- High scalability
- Optimized for large-scale data and fast read/write operations
Examples: MongoDB, Cassandra, Redis, Neo4j.
Relational vs. Non-Relational Databases
| Feature | Relational Databases (RDBMS) | Non-Relational Databases (NoSQL) |
|---|---|---|
| Schema | Fixed, predefined schema | Flexible schema |
| Data Model | Tables with rows and columns | Various (document, key-value, graph, columnar) |
| Query Language | SQL | No fixed query language (varies by type) |
| Scalability | Vertical (adding more power to one server) | Horizontal (adding more servers) |
| ACID Compliance | Strong ACID compliance | Typically supports BASE properties |
| Data Integrity | High data integrity with strict relationships | Data integrity varies, often application-managed |
| Best Use Cases | Financial systems, CRM, ERP | Real-time analytics, content management, IoT |
Table of Contents
Relational Databases (RDBMS)
Non-Relational Databases (NoSQL)
Relational vs. Non-Relational Databases