Computer Science Basics Course (CS101) – Module 6
Module 6: Databases
- Introduction to databases and their importance in software development
Introduction:
Databases are foundational components of software development, serving as organized collections of data that can be easily accessed, managed, and updated. In this lesson, we will explore the fundamentals of databases, their importance in software development, and the various types of databases commonly used in practice.
- What are Databases?
Definition: A database is a structured collection of data that is organized in a way that facilitates efficient storage, retrieval, and manipulation of data.
Components: Databases consist of tables (or relations) that store data in rows and columns, along with relationships between tables.
Key Concepts:
Tables: Structures that store data organized into rows and columns.
Rows: Individual records or entries within a table.
Columns: Fields or attributes that define the characteristics of the data.
Relationships: Associations between tables that represent connections between related data entities.
- Importance of Databases in Software Development:
Data Storage: Databases provide a centralized and structured way to store large volumes of data efficiently.
Data Retrieval: They enable fast and flexible retrieval of data based on various criteria, facilitating data analysis and decision-making.
Data Integrity: Databases enforce data integrity constraints to ensure that data remains accurate, consistent, and reliable.
Concurrency Control: They manage concurrent access to data by multiple users or applications, ensuring data consistency and preventing conflicts.
Scalability: Databases can scale to accommodate growing data volumes and increasing user demands without compromising performance.
Security: They offer mechanisms for controlling access to data, implementing authentication, authorization, and encryption to protect sensitive information.
Backup and Recovery: Databases support backup and recovery mechanisms to safeguard data against loss or corruption, ensuring data durability and availability.
- Types of Databases:
Relational Databases: Organize data into tables with predefined schemas and use SQL (Structured Query Language) for data manipulation and querying (e.g., MySQL, PostgreSQL, Oracle).
NoSQL Databases: Designed to handle unstructured or semi-structured data and provide flexible schema designs (e.g., MongoDB, Cassandra, Redis).
NewSQL Databases: Combine features of both relational and NoSQL databases, offering scalability and ACID (Atomicity, Consistency, Isolation, Durability) compliance (e.g., Google Spanner, CockroachDB).
Graph Databases: Optimize for managing and querying highly interconnected data, representing relationships between entities as edges in a graph structure (e.g., Neo4j, Amazon Neptune).
Document Databases: Store data in flexible, JSON-like documents, making them suitable for handling complex and hierarchical data structures (e.g., MongoDB, Couchbase).
- Practice Exercise:
Problem: Design a simple relational database schema to manage student information, including tables for students, courses, and enrollments.
Solution: Define tables for Students, Courses, and Enrollments, establishing relationships between them using foreign keys.
- Relational database concepts (tables, rows, columns, primary keys, foreign keys)
Introduction:
Relational databases are a fundamental component of modern software systems, providing a structured and efficient way to organize and manage data. Understanding the core concepts of relational databases is essential for designing and interacting with database systems effectively. In this lesson, we will explore the key concepts of relational databases and their significance in data management.
- Tables:
Definition: A table is a collection of related data organized into rows and columns.
Characteristics:
Each table represents a specific entity or concept within the database.
Tables consist of columns (attributes) and rows (records) that contain the actual data.
Example:
In a university database, you might have tables for students, courses, instructors, and enrollments, each storing relevant information about the corresponding entity.
- Rows and Columns:
Rows: Also known as records or tuples, rows represent individual instances or entries within a table.
Columns: Also known as fields or attributes, columns define the characteristics or properties of the data stored in the table.
Example:
In a Students table, each row represents a unique student, with columns such as student_id, name, email, and birthdate storing specific information about each student.
- Primary Keys:
Definition: A primary key is a unique identifier for each row in a table. It ensures that each row can be uniquely identified and retrieved.
Characteristics:
Primary keys must contain unique values for each row in the table.
Primary keys cannot contain null values.
Primary keys can consist of one or multiple columns.
Example:
In the Students table, the student_id column can serve as the primary key, uniquely identifying each student record.
- Foreign Keys:
Definition: A foreign key is a column (or set of columns) in a table that establishes a link or relationship between two tables.
Characteristics:
Foreign keys reference the primary key of another table, establishing a parent-child relationship.
Foreign keys ensure referential integrity, enforcing constraints on data integrity.
Example:
In the Enrollments table, the student_id column may serve as a foreign key referencing the student_id column in the Students table, linking each enrollment record to a specific student.
- Relationships between Tables:
One-to-One: Each record in one table corresponds to exactly one record in another table.
One-to-Many: Each record in one table can correspond to multiple records in another table.
Many-to-Many: Multiple records in one table can correspond to multiple records in another table.
Example:
In a university database, the relationship between students and courses would typically be many-to-many, as each student can enroll in multiple courses, and each course can have multiple enrolled students.
- Practice Exercise:
Problem: Design a relational database schema for a library management system, including tables for books, authors, publishers, and book copies.
Solution: Define tables for Books, Authors, Publishers, and BookCopies, establishing appropriate relationships between them using primary and foreign keys.
- SQL fundamentals (SELECT, INSERT, UPDATE, DELETE statements)
Introduction:
Structured Query Language (SQL) is a powerful language used for managing and manipulating data in relational database management systems (RDBMS). SQL provides a standardized syntax for performing various operations on databases, including querying, inserting, updating, and deleting data. In this lesson, we will explore the fundamentals of SQL commands and their usage in database operations.
- SELECT Statement:
Purpose: The SELECT statement retrieves data from one or more tables in a database.
Syntax:
SELECT column1, column2, …
FROM table_name
WHERE condition;
Example:
SELECT first_name, last_name
FROM employees
WHERE department = ‘IT’;
Explanation: This query retrieves the first name and last name of employees from the “employees” table where the department is “IT”.
- INSERT Statement:
Purpose: The INSERT statement adds new records (rows) to a table.
Syntax:
INSERT INTO table_name (column1, column2, …)
VALUES (value1, value2, …);
Example:
INSERT INTO customers (first_name, last_name, email)
VALUES (‘John’, ‘Doe’, ‘john.doe@example.com’);
Explanation: This query inserts a new customer record into the “customers” table with the specified values for the first name, last name, and email.
- UPDATE Statement:
Purpose: The UPDATE statement modifies existing records in a table.
Syntax:
UPDATE table_name
SET column1 = value1, column2 = value2, …
WHERE condition;
Example:
UPDATE products
SET price = 29.99
WHERE category = ‘Electronics’;
Explanation: This query updates the price of products in the “Electronics” category to $29.99 in the “products” table.
- DELETE Statement:
Purpose: The DELETE statement removes one or more records from a table.
Syntax:
DELETE FROM table_name
WHERE condition;
Example:
sql
DELETE FROM orders
WHERE order_date < ‘2023-01-01’;
Explanation: This query deletes all orders placed before January 1, 2023, from the “orders” table.
- Practice Exercise:
Problem: Write SQL queries to perform the following operations:
Retrieve the names of all customers from the “customers” table.
Insert a new employee record with the following details into the “employees” table: First Name: Alice, Last Name: Smith, Department: HR.
Update the quantity of a product with ID 101 to 50 in the “products” table.
Delete all inactive users from the “users” table.
Solution:
— Retrieve the names of all customers
SELECT first_name, last_name FROM customers;
— Insert a new employee record
INSERT INTO employees (first_name, last_name, department)
VALUES (‘Alice’, ‘Smith’, ‘HR’);
— Update the quantity of a product
UPDATE products
SET quantity = 50
WHERE product_id = 101;
— Delete inactive users
DELETE FROM users
WHERE status = ‘inactive’;
- Database design and normalization
Introduction:
Database design plays a crucial role in ensuring the efficiency, scalability, and maintainability of relational database systems. Normalization is a process used to organize data in a relational database to minimize redundancy and dependency, thereby improving data integrity and reducing anomalies. In this lesson, we will explore the concepts of database design and normalization and understand their significance in creating robust database schemas.
- Database Design Principles:
Identify Entities and Attributes: Identify the entities (objects or concepts) within the database and their attributes (properties or characteristics).
Define Relationships: Establish relationships between entities to represent associations and dependencies between data elements.
Normalize Data: Organize data into tables and apply normalization techniques to minimize redundancy and ensure data integrity.
Optimize Performance: Design database schemas and indexes to optimize query performance and data retrieval efficiency.
Enforce Constraints: Define constraints such as primary keys, foreign keys, and check constraints to maintain data integrity and consistency.
- Normalization:
Definition: Normalization is the process of organizing data in a relational database to eliminate redundancy and reduce data anomalies.
Goals:
Minimize data redundancy: Store each piece of data in only one place to avoid duplication.
Reduce data dependency: Ensure that data is stored logically and efficiently to prevent update anomalies.
Maintain data integrity: Enforce constraints to ensure that data remains accurate and consistent.
Levels of Normalization:
First Normal Form (1NF): Eliminate repeating groups and ensure atomicity of data.
Second Normal Form (2NF): Remove partial dependencies by ensuring that non-key attributes depend on the entire primary key.
Third Normal Form (3NF): Eliminate transitive dependencies by removing attributes that are not directly dependent on the primary key.
Boyce-Codd Normal Form (BCNF): Address additional anomalies by further refining dependencies between attributes.
Example:
Consider a denormalized table storing both customer information and orders. Normalization would involve splitting this into separate tables for customers and orders, linking them through a foreign key relationship.
- Benefits of Normalization:
Data Integrity: Normalization reduces the risk of data anomalies such as insertion, update, and deletion anomalies, ensuring data integrity.
Efficient Storage: By minimizing redundancy, normalization reduces storage requirements and improves disk space utilization.
Simplified Updates: Normalized databases facilitate easier and more efficient updates, as changes only need to be made in one place.
Improved Query Performance: Normalized schemas often lead to better query performance due to reduced data redundancy and more efficient data retrieval.
- Practice Exercise:
Problem: Design a normalized database schema for a library management system, including tables for books, authors, publishers, and library branches.
Solution: Apply normalization techniques to decompose the initial schema into separate tables, ensuring that each table represents a single entity and that relationships are established using appropriate foreign keys.