A database is a structured collection of data organised in a way that allows efficient retrieval, storage, and management of information. Databases are a fundamental component of many software applications, providing a systematic and organised approach to storing and retrieving data.
Databases can be categorised based on various criteria, including their data model, architecture, and use cases.
Here's a list of some common categories of databases:
1. Relational Databases (RDBMS):
Organises data into tables with rows and columns, enforces relationships between tables, and uses SQL for querying and managing data.
Examples: MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server.
More details about RDBMS
2. NoSQL Databases:
a. Document-Oriented Databases:
Stores data in flexible, JSON-like documents.
b. Key-Value Stores:
Stores data as key-value pairs.
Examples: Redis, Amazon DynamoDB.
c. Wide-Column Stores:
Organises data in columns and is used in distributed and scalable environments.
Examples: Apache Cassandra, HBase.
d. Graph Databases
Stores and queries data with complex relationships using graph structures.
Examples: Neo4j, Amazon Neptune.
3. In-Memory Databases:
Stores data primarily in RAM for fast data access.
4. NewSQL Databases:
Aims to combine the scalability of NoSQL databases with ACID compliance for distributed transactions.
Examples: Google Spanner, CockroachDB.
5. Columnar Databases:
Stores data in columns, optimised for analytical queries and data warehousing.
Examples: Amazon Redshift, Google Bigtable.
6. Time-Series Databases:
Designed to efficiently handle and query data with timestamp information, commonly used in monitoring and IoT applications.
Examples: InfluxDB, Prometheus.
7. Spatial Databases:
Manages and queries spatial or geographic data, supporting features like points, lines, and polygons.
Examples: PostGIS (extension for PostgreSQL), Oracle Spatial.
8. Object-Oriented Databases:
Stores data in the form of objects, allowing for a more natural representation of complex data structures and relationships.
9. Multimodel Databases:
Supports multiple data models (e.g., document, graph, key-value) within a single database engine.
10. Blockchain Databases:
Stores data in a decentralised and tamper-resistant manner using blockchain technology.
Examples: Hyperledger Fabric, Ethereum.
11. Embedded Databases:
Lightweight databases integrated directly into an application.
Examples: SQLite, H2 Database.
12. Cloud-Based Databases:
Hosted and managed in the cloud, providing scalability, automated backups, and easy integration with other cloud services.
Examples: Amazon RDS, Azure SQL Database, Google Cloud Firestore.
This list provides an overview of different database categories, and each category serves specific use cases and requirements. The choice of a database type depends on factors such as data model, scalability, performance, and the nature of the application being developed.
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