Unit1

Topic: Data, Database and Database System

Learning Objectives:

1. Data and Database.(Lecture-1)
2. Introduction to Data Base Systems.(Lecture-2)
3. Flipped Classroom.
4. Homework.

Data & Database:

• Data: Collection of raw facts.
• Database: Organized collection of data for easy access & management.
• DBMS: Software for storing, managing, and retrieving data efficiently.

A Database Management System (DBMS) comprises several key components:

• Developer: Designs and maintains the database, ensuring efficiency and security.
• User: Interacts with the database to retrieve or input data.
• Application: Provides an interface between the user and the database, simplifying access to information.

file-processing system(Traditional method)

• Stores data in separate files, lacks integration & security.

Disadvantages of file-processing systems:

1. Data Redundancy & Inconsistency: Duplicate data wastes storage and causes inconsistencies.
2. Difficulty in Accessing Data: Lacks efficient querying and indexing.
3. Data Isolation: Separate files hinder data integration and sharing.
4. Integrity Issues: No validation, leading to incorrect or inconsistent data.
5. Atomicity Issues: Incomplete transactions may leave data in an inconsistent state.
6. Concurrent Access Anomalies: Multiple users can cause conflicts and data corruption.
7. Security Risks: Weak access controls, encryption, and auditing make data vulnerable.

Advantages of DBMS:

1. Data Consistency:
   • Centralized updates prevent inconsistencies.
2. Scalability:
   • Handles large data & multiple users efficiently.
3. Security:
   • Controls access & protects data.
4. Reduces Redundancy:
   • Prevents duplicate storage using Normalization.
5. Data Abstraction:
   • Hides complexity, simplifies access..

Why Use a Database System?

Centralized Data Management: Reduces redundancy & inconsistency.
Improved Security: Protects sensitive data.
Efficient Access & Processing: Fast retrieval & updates.
Supports Multiple Users: Allows concurrent data access.
Scalable & Flexible: Can grow with the organization.

Data is the New Gold**

• In the digital era, data is as valuable as gold because it drives decision-making, innovation, and business success.
• Database systems help manage and organize vast amounts of data efficiently.

Brief History of Database Evolution

https://palden518.github.io/DBS101.github.io/2024/02/01/unit1.html

1. Introduction to database system:

View of Data

• A DBMS supports database creation, querying, updating, and management based on a data model.
• Data Model: Defines the structure, relationships, meaning, and constraints of data.

Types of Data Models

1. Entity-Relationship (ER) Model
   • Represents data as entities (objects) and relationships between them.
   • Used in designing database structures before implementation.
2. Semi-structured Data Model
   • Stores irregular or flexible data formats (e.g., XML, JSON).
   • Does not follow a strict schema like relational databases.
3. Object-Based Data Model
   • Uses objects (like in OOP) to store data along with behaviors (methods).
   • Suitable for complex applications like CAD and multimedia databases.
4. Relational Model
   • Organizes data in tables (relations) with rows and columns.
   • Uses SQL for querying and managing data.
   • data are represented in the form of tables/relations.
     

Data Abstraction

• It hides the complexity of data structures from users, allowing easier interaction with the database.
  1. Physical Level: Defines how data is stored internally (e.g., file structures, indexing).
  2. Logical Level: Describes what data is stored and the relationships between them.
  3. View Level: Shows only a part of the database, customized for different users.

Database Design Steps

1. Select a Data Model: Choose the structure for organizing data.
2. Conceptual Design: Define functional requirements and high-level schema.
3. Logical Design: Map the conceptual schema to the database system.
4. Physical Design: Optimize storage and access methods for performance.

Database Schema vs. Instance.

• Schema: Blueprint/structure of a database.
• Instance: Current data in the database.

Database Languages

• DDL (Data Definition Language): Defines schema (CREATE, ALTER, DROP).
• DML (Data Manipulation Language): Manages data (SELECT, INSERT, UPDATE, DELETE).
• Query Language: Retrieves data (SQL).

Database Engine

• A database engine is software in a DBMS that manages storage and handles CRUD operations (Create, Read, Update, Delete).
• It is also called a storage engine or embedded database.
• MySQL & MariaDB allow switching engines.
• PostgreSQL has a fixed built-in engine.

Database Engine Components

1. Storage Manager: Manages data storage and connects low-level data with applications and queries.
2. Query Processor: Includes DDL interpreter, DML compiler, and query evaluation engine to process queries.
3. Transaction Management: Ensures safe and consistent execution of database transactions.

Database System Architecture:

Architecture of Applications that use Databases as Backend:

Flipped Classroom:

What are the different types of database users and administrators

Types of Database users are:

1. Database administrators(DBA):
   • Manage the database system, set permissions, and ensure security.
   • Example: A DBA who manages MySQL databases in an enterprise system.
2. Appplication Programmers:
   • Developers who write applications that interact with the database.
   • Example: A software engineer coding a web app that fetches user data from a database.
3. End Users:
   • Regular users who interact with the database through applications.
   • Consist of Naive and sophisticated User.
   • Example: A student using a university portal to check grades.
4. System Analysts:
   • Design database structures and recommend database technologies.
   • Example: An IT specialist designing an inventory database for a company.
5. Database Designers:
   • Define the database schema (tables, relationships, constraints).
   • Example: A designer creating an ER model for a library system.
6. Data Scientist And Analysts:
   • Extract insights from data stored in databases using SQL queries or analytics tools.
   • Example: A data scientist using PostgreSQL to analyze sales trends.
7. Casual/Temporary Users:
   • Users who occasionally run simple queries without deep technical knowledge.
   • Example: A manager checking monthly sales reports in a business database
8. Specialized Users:
   • They interact with databases using automation, scripts, AI models, or ETL processes instead of manual queries, enabling advanced data processing and analytics.

Key Responsibilities of Database Administrators:

1. Database Setup & Configuration:
   • Install, configure, and maintain DBMS.
2. Security & Access Control:
   • Manage user permissions and protect data.
3. Backup & Recovery:
   • Ensure regular backups and disaster recovery plans.
4. Performance Optimization:
   • Tune queries, indexing, and caching for efficiency.
5. Data Integrity & Consistency:
   • Maintain accurate and consistent data.
6. Database Maintenance:
   • Apply updates, patches, and archive old data.
7. User Management:
   • Create and manage database users and roles.
8. Troubleshooting:
   • Identify and resolve database errors and slow queries.
9. Automation & Scripting:
   • Write scripts for backups and performance monitoring.
10. Collaboration:
    • Work with developers and analysts to optimize databases.

Home work:

• what are the difference between the Database and Database Management System(DBMS)?