Basic Definitions
Data:
Raw facts and figures, such as numbers, text, or other observations, that can be collected and stored.
Information:
Data that has been processed and organized in a way that is meaningful and useful for decision-making.
Databases:
Databases are organized collections of data that are structured in a way that facilitates efficient storage, retrieval, and management. They serve as the backbone of various applications, enabling users to store and manipulate large amounts of data systematically.
Types of Databases
Databases can be of different types, including relational databases, and NoSQL databases, each suited for specific types of applications.
1. Relational Databases:
These are the most common type of databases, where data is stored in tables with rows and columns. Examples include MySQL, Oracle, and SQL Server.
2. NoSQL Databases:
Designed for unstructured data, NoSQL databases are highly scalable and are used in applications like big data and real-time web apps. Examples include MongoDB and Cassandra.
Database Applications
1. Inventory Systems: These applications manage stock levels, orders, and supply chain operations by interacting with the database to store and retrieve relevant data.
2. Academic System: An academic system is a software application designed to manage and streamline the various processes within educational institutions. These systems handle tasks ranging from student enrollment and course management to grading and reporting, providing a centralized platform for administrators, teachers, students, and parents.
Database Management System
A Database Management System (DBMS) is a software system that provides essential functionalities for managing databases:
1. Define:
The process of specifying the data types, structures, and constraints for the data to be stored in the database.
2. Construct:
Involves storing data on a storage medium, organizing it according to the defined structures.
3. Manipulate:
Refers to querying the database to retrieve specific data, updating the database as needed, and generating reports based on the data.
4. Share:
Allows multiple users and programs to access the database concurrently, ensuring efficient data sharing and management.
DB System Environment
The database system environment includes various components such as hardware, software, data, procedures, and people that interact with the database to perform different tasks. These components work together to ensure that the database operates efficiently and effectively.
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Main Characteristics of the Database Approach
1. Integration: Centralizing data in one place to facilitate easy access and management.
2. Non-redundancy: Minimizing data duplication to save storage space and reduce inconsistencies.
3. Data Integrity: Ensuring that the data is accurate, consistent, and reliable.
4. Multiple Views: Supporting different perspectives of the data for various users or applications.
5. Multiuser Transaction: Allowing multiple users to access and manipulate the data concurrently without conflicts.
Types of Database Users
1. Actors on the Scene:
Database Administrators (DBAs): Responsible for managing and maintaining the database system, ensuring its efficiency, security, and reliability.
Database Designers: Plan and create the database structure, determining how data is organized and stored.
End Users: Vary in their interaction with the database, from casual users who need basic information access to sophisticated users who utilize advanced features.
System Analysts & Application Programmers: Analyze information systems and develop software applications that interact with the database.
2. Workers behind the Scene:
System Designers & Implementers: Develop the overall structure and functionality of the information system, including the database.
Tool Developers: Create tools and utilities for database design, development, and maintenance.
Operators & Maintenance Personnel: Handle the day-to-day operation of the database system, ensuring smooth and efficient functioning.
Database System Concepts and Architecture
Database Schema:
The database schema is a description of the database structure, outlining the types of data it holds and the rules it follows. A schema diagram visually represents the schema, showing how different parts of the database are connected.
Database State:
The database state, also known as a database instance, refers to the actual data stored in the database at a specific moment. It can change frequently as new data is added or existing data is updated.
The initial database state is the state when the database is first set up, while a valid state adheres to the rules defined in the schema.
Three-Schema Architecture:
The three-schema architecture is designed to fulfill the characteristics of a DBMS, providing program-data independence and support for multiple views of data:
1. Internal Schema: Describes the physical storage structures and access paths.
2. Conceptual Schema: Describes the overall structure and constraints of the database.
3. External Schemas: Describe various user views, allowing different users to interact with the data in ways that suit their needs.
Data Independence:
Logical Data Independence: The ability to modify the conceptual schema without affecting external schemas or application programs.
Example: You can change how data is structured or organized in the database without needing to update all the applications that use that data.
Physical Data Independence: The ability to change the internal schema without affecting the conceptual schema or higher-level schemas.
Example: You can rearrange how data is stored on the disk or add new indexes to improve performance without affecting how users interact with the data.
DBMS Languages:
Data Definition Language (DDL): Used to define the structure of the database, including tables, columns, and constraints.
Data Manipulation Language (DML): Used for specifying database retrievals and updates, allowing users to interact with the data stored in the database.
