Fast-Food-Restaurant-JUNK--SQL-Data-Modeling

This project involves designing and implementing a data model for a fast-food restaurant business 'JUNK'.

JUNK: Relational Database for a Fast-Food Restaurant

1. Overview

Welcome to the JUNK project, a comprehensive database solution designed for a fast-food restaurant chain with five branches located in Madrid. This project aims to model the restaurant’s operations efficiently while enabling data-driven decision-making. By adhering to Third Normal Form (3NF), the database ensures data integrity, eliminates redundancy, and optimizes access to key insights.

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2. Features

Key Functionalities:

• Customer Orders: Tracks detailed information about orders, including menu items and quantities.
• Payment Details: Manages payment methods, including cash, credit card types, and amounts.
• Employee Management: Maintains staff details across branches to support operational efficiency.
• Menu Offerings: Categorizes menu items for easy inventory tracking and sales analysis.

Insights Provided:

• Identification of top-selling menu items.
• Revenue breakdown by branches and payment methods.
• Analysis of customer preferences and trends.
• Performance tracking for employee contributions and operations.

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3. Database Design

The database is structured in compliance with 3NF, ensuring:

• Elimination of Redundancy: Data is segmented across distinct tables, avoiding duplication.
• Dependency on Primary Keys: Non-key attributes depend solely on their table’s primary key.
• Removal of Transitive Dependency: Relationships between tables are direct, ensuring clean data linkage.

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4. Implementation

Files Included:

• Entity Relationship Diagram (ERD):

  • Visual representation of the database structure.
  • File: er_diagram.dbml

• Database Creation Script:

  • SQL script for creating tables and establishing relationships.
  • File: create_db.sql

• Data Insertion Script:

  • SQL script to populate tables with simulated data for meaningful analysis.
  • File: insert_data.sql

• Analytics Queries:

  • SQL script containing queries to answer business questions.
  • File: analytics.sql

• Project Report:

  • Detailed explanation of the database, assumptions, and findings.
  • File: JUNK-SQLII.pdf

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5. Some Business Questions Answered

• Top-Selling Menu Items: What are the most popular dishes across all branches?
• Branch Performance: Which branch generates the highest revenue?
• Customer Analysis: How can customer preferences be identified through order data?
• Revenue Insights: How does payment method usage impact revenue?

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6. How to Use

Set Up the Database:

• Run create_db.sql to establish the database structure.
• Execute insert_data.sql to populate tables with sample data.

Run Analytics Queries:

• Use analytics.sql to answer business questions and explore insights.

Visualize Results:

• Refer to the project report and ERD for an understanding of the database model.