Overview of DBMS Quiz

Definition: A DBMS is software that enables users to create, manage, and manipulate databases.
Purpose: Facilitates data storage, retrieval, and updating while ensuring data integrity and security.

Hierarchical DBMS:
- Organizes data in a tree-like structure.
- Each record has a single parent and potentially multiple children.
Network DBMS:
- Allows multiple parent-child relationships (more flexible than hierarchical).
- Data is linked in a graph structure.
Relational DBMS (RDBMS):
- Stores data in tables (relations).
- Uses Structured Query Language (SQL) for data manipulation.
- Examples: MySQL, PostgreSQL, Oracle, SQL Server.
Object-oriented DBMS:
- Integrates object-oriented programming concepts with database technology.
- Stores complex data types and supports inheritance.
NoSQL DBMS:
- Designed for unstructured or semi-structured data.
- Types include:
  - Document databases (e.g., MongoDB)
  - Key-value stores (e.g., Redis)
  - Column-family stores (e.g., Cassandra)
  - Graph databases (e.g., Neo4j)

Database Engine:
- Core service for storing, processing, and securing data.
Database Schema:
- Defines the structure of the database including tables, fields, relationships.
Query Processor:
- Translates queries into actions to be performed on the database.
Transaction Management:
- Ensures data integrity by managing concurrent transactions and rolling back when necessary.
Database Security:
- Involves user authentication, access controls, and data encryption.

Data Definition:
- Define the database structure using Data Definition Language (DDL).
Data Manipulation:
- CRUD operations (Create, Read, Update, Delete) performed via Data Manipulation Language (DML).
Data Security:
- Controls user access and protects data from unauthorized access.
Data Backup and Recovery:
- Provides mechanisms to back up data and restore it after loss or corruption.
Concurrency Control:
- Manages simultaneous access to the database to avoid conflicts.

Data Integrity: Enforces data consistency and accuracy through constraints.
Efficient Data Management: Centralizes data management, reducing redundancy.
Improved Data Sharing: Facilitates sharing across users and applications.
Data Security: Implements authentication and authorization measures.
Data Backup: Simplifies data recovery processes.

Cost: Initial setup and ongoing maintenance can be expensive.
Complexity: Requires skilled personnel for management and optimization.
Performance Issues: May face challenges with scalability and performance under high load.
Data Migration: Difficulties can arise when moving data between systems.

A software application used to create, manage, and manipulate databases.
Facilitates various functionalities, including data storage, retrieval, and updating.
Ensures data integrity and security.

Hierarchical DBMS: data organized in a tree-like structure, with a single parent and multiple children.
Network DBMS: extends hierarchical by permitting multiple parent-child relationships, allowing data connections in a graph structure.
Relational DBMS (RDBMS): stores data in tables, employs SQL for data manipulation, examples include MySQL, PostgreSQL, Oracle, SQL Server.
Object-oriented DBMS: combines object-oriented programming concepts with database technology, storing complex data types and supporting inheritance.
NoSQL DBMS: tailored for unstructured or semi-structured data, includes document databases (MongoDB), key-value stores (Redis), column-family stores (Cassandra), and graph databases (Neo4j).

Database Engine: core component responsible for storing, processing, and securing data.
Database Schema: defines the database structure, including tables, fields, and relationships.
Query Processor: translates user queries into actions executed on the database.
Transaction Management: ensures data integrity by managing concurrent transactions and rolling back errors.
Database Security: implements user authentication, access controls, and data encryption.

Data Definition: defines the database structure using Data Definition Language (DDL).
Data Manipulation: performs Create, Read, Update, Delete operations (CRUD) using Data Manipulation Language (DML).
Data Security: controls user access and protects data from unauthorized access.
Data Backup and Recovery: provides mechanisms to back up data and restore it after loss.
Concurrency Control: manages simultaneous access to the database to avoid conflicts.

Data Integrity: enforces data consistency and accuracy through constraints.
Efficient Data Management: centralizes data management, minimizing redundancy.
Improved Data Sharing: facilitates data sharing across users and applications.
Data Security: implements authentication and authorization measures.
Data Backup: simplifies data recovery processes.

Cost: initial setup and ongoing maintenance can be expensive.
Complexity: requires skilled personnel for management and optimization.
Performance Issues: may encounter scalability and performance challenges under high load.
Data Migration: difficulties can arise when moving data between systems.