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BhanuHarshaY/restaurant-database-management-system

Production-grade normalized database (3NF) for restaurant analytics with cloud deployment. ETL pipeline processes 139K+ records, automated reporting with revenue trend analysis, and stored procedures for business logic. Built with R and MySQL on Aiven.

aivenbusiness-intelligencecloud-databasedata-analyticsdata-engineeringdatabase-managementetl-pipelinemysqlnormalizationr-programmingrstudiosql

Restaurant Database Management System

R
MySQL
License: MIT

A comprehensive normalized relational database system for analyzing restaurant visits, revenue, and sales transactions. Built as part of CS5200 Database Management Systems coursework at Northeastern University.

Table of Contents

Overview

This project transforms a denormalized CSV dataset containing 139,874 restaurant visit records into a fully normalized (3NF) relational database. The system enables efficient data storage, retrieval, and analysis of restaurant operations including customer behavior, server performance, and revenue trends.

Key Objectives:

  • Design and implement a normalized database schema (3NF)
  • Deploy database on cloud infrastructure (Aiven MySQL)
  • Develop ETL pipeline for data migration from CSV to relational tables
  • Create comprehensive SQL queries for business intelligence
  • Build automated reporting systems with visualizations
  • Implement business logic through stored procedures

Key Features

Database Design

  • Normalization to 3NF: Eliminates data redundancy and ensures data integrity
  • 7 Normalized Tables: CustomerDetails, RestaurantDetails, ServerDetails, VisitDetails, BillDetails, MealType, PaymentMethod
  • Referential Integrity: Complete foreign key constraints and relationships
  • Cloud Deployment: Hosted on Aiven MySQL for universal accessibility

Data Pipeline

  • Automated ETL Process: Batch processing of 139,874+ records
  • Data Validation: Handles missing values and sentinel values (e.g., 99, "0000-00-00")
  • Batch Insertion: Optimized bulk loading with transaction management
  • Error Handling: Robust error detection and logging

Analytics & Reporting

  • Restaurant Performance Analysis: Visits, unique customers, loyalty metrics, revenue
  • Year-over-Year Trends: Revenue growth analysis with visualizations
  • Custom SQL Queries: Complex joins and aggregations
  • Automated PDF Reports: Professional kableExtra formatted tables

Business Logic

  • Stored Procedures: StoreVisit and StoreNewVisit for transactional operations
  • Data Integrity Checks: Automated validation during data loading
  • Comprehensive Testing: Validation scripts for data accuracy

Database Architecture

Entity-Relationship Diagram

ERD Diagram

Normalized Schema (3NF)

The database consists of 7 tables designed to eliminate redundancy:

  1. CustomerDetails: Customer information and loyalty status
  2. RestaurantDetails: Restaurant master data
  3. ServerDetails: Server employment and demographic information
  4. VisitDetails: Central fact table linking customers, restaurants, and servers
  5. BillDetails: Financial transaction data
  6. MealType: Lookup table for meal categories
  7. PaymentMethod: Lookup table for payment types

Technologies Used

  • Database: MySQL 8.0+ (Cloud-hosted on Aiven)
  • Programming Language: R 4.0+
  • Key R Packages:
    • DBI - Database connectivity
    • RMySQL - MySQL driver
    • kableExtra - Professional table formatting
    • tinytex - PDF generation
  • Development Tools:
    • RStudio
    • R Markdown for literate programming
    • Git for version control

Project Structure

restaurant-database-management-system/
│
├── README.md                          # Project documentation
├── LICENSE                            # MIT License
├── .gitignore                         # Git ignore rules
│
├── data/                              # Dataset directory
│   └── restaurantvisits139874.csv     # Original dataset (139,874 records)
│
├── docs/                              # Documentation
│   ├── ERD/
│   │   └── RestaurantDB-ERD.png       # Entity-Relationship Diagram
│   
│
├── scripts/                           # R Scripts
│   ├── createDB.PractI.YanamadalaB.R          # Creates database schema
│   ├── deleteDB.PractI.YanamadalaB.R          # Drops all tables (cleanup)
│   ├── loadDB.PractI.YanamadalaB.R            # ETL pipeline for data loading
│   ├── testDBLoading.PractI.YanamadalaB.R     # Validation tests
│   └── configBusinessLogic.PractI.YanamadalaB.R # Stored procedures
│
└── notebooks/                         # R Markdown Notebooks
|    ├── designDBSchema.PractI.YanamadalaB.Rmd  # Normalization documentation
|    └── RevenueReport.PractI.YanamadalaB.Rmd   # Analytics report
|
|
└── outputs/
    ├── sample_reports/
              └── RevenueReport.PractI.YanamadalaB.pdf
              └── designDBSchema.PractI.YanamadalaB.pdf

Installation & Setup

Prerequisites

# Required R packages
install.packages(c("DBI", "RMySQL", "kableExtra", "tinytex"))

Database Configuration

  1. Set up Aiven MySQL Database (or alternative cloud provider)
  2. Update connection parameters in each R script:
dbConnection <- function() {
  conn <- dbConnect(
    RMySQL::MySQL(),
    user = "YOUR_USERNAME",
    password = "YOUR_PASSWORD",
    dbname = "YOUR_DATABASE",
    host = "YOUR_HOST",
    port = YOUR_PORT
  )
  return(conn)
}

Clone Repository

git clone https://github.com/yourusername/restaurant-database-management-system.git
cd restaurant-database-management-system

Usage

1. Create Database Schema

source("scripts/createDB.PractI.YanamadalaB.R")

Creates all 7 normalized tables with appropriate constraints, primary keys, and foreign keys.

2. Load Data into Database

source("scripts/loadDB.PractI.YanamadalaB.R")

Executes ETL pipeline:

  • Reads CSV from URL
  • Cleans and validates data
  • Performs batch insertions
  • Handles missing/sentinel values

3. Validate Data Loading

source("scripts/testDBLoading.PractI.YanamadalaB.R")

Runs comprehensive tests:

  • Counts unique restaurants, customers, servers, visits
  • Validates total revenue calculations
  • Compares CSV data against database

4. Generate Revenue Report

rmarkdown::render("notebooks/RevenueReport.PractI.YanamadalaB.Rmd")

Produces PDF report with:

  • Restaurant performance analysis
  • Year-over-year revenue trends
  • Professional visualizations

5. Reset Database (Optional)

source("scripts/deleteDB.PractI.YanamadalaB.R")

Drops all tables for clean reinitialization.

Database Schema

Functional Dependencies (3NF Compliant)

CustomerDetails:

CustomerID → {CustomerName, CustomerPhone, CustomerEmail, LoyaltyMember}

RestaurantDetails:

RestaurantID → {RestaurantName}

ServerDetails:

ServerEmpID → {ServerName, ServerBirthDate, ServerTIN, StartDateHired, EndDateHired, HourlyRate}

VisitDetails:

VisitID → {VisitDate, VisitTime, CustomerID, MealTypeID, RestaurantID, PartySize, Genders, WaitTime, ServerEmpID}

BillDetails:

BillID → {VisitID, FoodBill, TipAmount, DiscountApplied, PaymentID, orderedAlcohol, AlcoholBill}

MealType:

MealTypeID → {MealType}

PaymentMethod:

PaymentID → {PaymentMethod}

Sample Table Creation (CustomerDetails)

CREATE TABLE IF NOT EXISTS CustomerDetails (
    CustomerID INT PRIMARY KEY AUTO_INCREMENT,
    CustomerName TEXT NOT NULL,
    CustomerPhone TEXT,
    CustomerEmail TEXT,
    LoyaltyMember BOOLEAN DEFAULT FALSE
);

Analysis Results

Top Performing Restaurants

Restaurant Name Total Visits Unique Customers Loyalty Customers Total Revenue
Bite & Bun 15,961 6 5 $633,429.10
The Burger Joint 15,684 5 4 $622,693.20
Grill & Thrill 15,484 3 2 $614,597.40

Revenue Trend Analysis

The system tracks year-over-year revenue trends from 2018-2024, showing:

  • Peak Year: 2024 with $1,264,824.71
  • Growth Rate: Consistent upward trajectory through 2022
  • Visualization: Automated line charts with data labels

Stored Procedures

StoreVisit

Adds new visit when customer, server, and restaurant already exist.

CALL StoreVisit(
    pRestaurantID, pCustomerID, pVisitDate, pVisitTime,
    pMealTypeID, pPartySize, pGenders, pWaitTime,
    pFoodBill, pAlcoholBill, pTipAmount, pDiscountApplied,
    pOrderedAlcohol, pPaymentID, pServerEmpID
);

StoreNewVisit

Comprehensive procedure that creates new customer/server/restaurant records if they don't exist before inserting visit.

CALL StoreNewVisit(
    -- Same parameters as StoreVisit
);

Future Enhancements

  • Implement additional stored procedures for complex business logic
  • Add triggers for audit logging
  • Create views for common query patterns
  • Develop web-based dashboard using Shiny
  • Implement advanced analytics (customer segmentation, predictive modeling)
  • Add data warehouse layer for OLAP operations
  • Integrate with visualization tools (Tableau, Power BI)
  • Implement data versioning and temporal queries

Author

Bhanu Harsha Yanamadala

  • Course: CS5200 Database Management Systems
  • Institution: Northeastern University
  • Semester: Spring 2025

Acknowledgments

  • Course Instructor Prof. Martin and TAs for guidance on database design principles
  • Aiven for cloud MySQL hosting infrastructure
  • R community for excellent database connectivity packages
  • Northeastern University CS5200 coursework framework

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or collaboration opportunities, please open an issue in this repository.

⭐ If you find this project useful, please consider giving it a star!

Contributors

BH
MIT License
Created October 14, 2025
Updated November 6, 2025