Database Management Systems (DBMS) can be classified based on various criteria, including data models, number of users, database distribution, and use case scenarios.
Here are the main classifications:
Based on Data Models
1.) Hierarchical DBMS:
- Organizes data in a tree-like structure.
- Each record has a single parent and multiple children.
- Example: IBM Information Management System (IMS).
2.) Network DBMS:
- Organizes data in a graph structure allowing many-to-many relationships.
- More flexible than hierarchical DBMS.
- Example: Integrated Data Store (IDS).
3.) Relational DBMS (RDBMS):
- Organizes data in tables (relations) with rows and columns.
- Uses Structured Query Language (SQL) for data manipulation.
- Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL Server.
4.) Object-oriented DBMS (OODBMS):
- Stores data as objects, similar to object-oriented programming.
- Supports complex data types and inheritance.
- Examples: db4o, ObjectDB.
5.) Object-relational DBMS (ORDBMS):
- Combines features of RDBMS and OODBMS.
- Supports both relational tables and object-oriented features.
- Examples: PostgreSQL, Oracle.
6.) NoSQL DBMS:
- Designed for unstructured or semi-structured data.
- Examples include document stores (MongoDB), key-value stores (Redis), column-family stores (Cassandra), and graph databases (Neo4j).
Based on Number of Users
1.) Single-user DBMS:
- Supports one user at a time.
- Typically used on personal computers.
- Example: Microsoft Access.
2.) Multi-user DBMS:
- Supports multiple users simultaneously.
- Manages concurrent access and maintains data integrity.
- Examples: MySQL, PostgreSQL, Oracle.
Based on Database Distribution
1.) Centralized DBMS:
- Data is stored and managed on a single central server.
- Users connect to the central server to access data.
- Example: Mainframe DBMS.
2.) Distributed DBMS (DDBMS):
- Data is distributed across multiple sites or servers.
- Ensures data consistency and coordination among sites.
- Examples: Google Spanner, Amazon Aurora.
3.) Federated DBMS:
- Integrates multiple autonomous databases into a single federated database.
- Each database maintains its autonomy.
- Example: Microsoft SQL Server with Linked Servers.
Based on Use Case Scenarios
1.) OLTP (Online Transaction Processing) DBMS:
- Optimized for transaction-oriented applications.
- Ensures ACID (Atomicity, Consistency, Isolation, Durability) properties.
- Examples: MySQL, PostgreSQL, Oracle.
2.) OLAP (Online Analytical Processing) DBMS:
- Optimized for read-heavy workloads and complex queries.
- Used in data warehousing and business intelligence applications.
- Examples: Amazon Redshift, Microsoft SQL Server Analysis Services (SSAS).
