Designing a database schema for brand ratings involves creating tables that store information about brands, users, and their ratings. Here’s a simplified example of how you could structure the schema using SQL:

Brand Table:

This table stores information about different brands.

    brand_id INT PRIMARY KEY,
    brand_name VARCHAR(100) NOT NULL,
    industry VARCHAR(50),
    website VARCHAR(200),
    founding_year INT,
    headquarters VARCHAR(100)

User Table:

This table stores information about users who rate brands.

    user_id INT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL,
    registration_date DATE

Rating Table:

This table stores the actual ratings given by users to different brands.

    rating_id INT PRIMARY KEY,
    user_id INT,
    brand_id INT,
    rating DECIMAL(3, 2) NOT NULL,
    comment TEXT,
    rating_date TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES User(user_id),
    FOREIGN KEY (brand_id) REFERENCES Brand(brand_id)

In this schema, the Brand table stores information about different brands, the User table stores information about users, and the Rating table captures the ratings and comments provided by users for various brands. Here’s a brief explanation of each table:

The Brand table contains details about brands, such as their ID, name, industry, website, founding year, and headquarters.

The User table stores information about users, including their ID, username, email, and registration date.

The Rating table holds the actual ratings and comments. It includes a unique rating ID, the ID of the user who provided the rating (user_id), the ID of the brand being rated (brand_id), the numerical rating itself (rating), an optional comment (comment), and the date of the rating (rating_date). The user_id and brand_id columns in the Rating table are foreign keys that reference the corresponding IDs in the User and Brand tables.

Remember, this is a simplified example, and in a real-world scenario, you might need additional tables for more complex features like tracking user interactions, handling authentication and authorization, or capturing historical changes in brand information. Additionally, you might want to consider indexes and constraints to optimize performance and maintain data integrity.

Commissioned by Brands To Shop