Food Delivery Database Management System

Skills Demonstrated

Relational database design
SQL schema creation and foreign key management
Data modeling and normalization
Problem-solving and system structuring

Project Details

This project involves the design and implementation of a relational database system to manage key operations for a food delivery platform, including customer management, restaurant listings, order tracking, and delivery coordination. The goal was to create a scalable, normalized, and query-efficient database that supports operational insights and future analytics.

Using MySQL Workbench, I designed a conceptual and logical Entity-Relationship Diagram (ERD) with five interconnected tables:

Customers – Stores customer information and registration details.
Restaurants – Contains restaurant names, locations, and operating hours.
Orders – Tracks customer orders, items, dates, status, and total amounts.
Deliveries – Manages delivery assignments, statuses, and completion times.
Riders – Maintains rider profiles and sign-up data.

Each entity was linked using primary and foreign key relationships to ensure referential integrity. Data types such as INT, VARCHAR, DATE, TIME, and FLOAT were selected carefully for performance optimization. The schema was normalized up to the third normal form (3NF) to minimize redundancy and enhance data consistency.

After implementation, I tested the database with sample datasets and SQL queries to validate relationships and generate insights such as delivery performance, customer order frequency, and restaurant activity. The system enables accurate tracking of end-to-end operations — from order placement to delivery completion — while maintaining data accuracy and flexibility for future scalability.

This project demonstrates strong skills in relational database design, SQL schema creation, and data modeling, showcasing the ability to translate real-world business requirements into a robust and efficient data infrastructure.

Steps

Step 1: Database Conceptualization and Initial Schema Design

The first step in the project involved conceptualizing and developing the initial relational database schema using MySQL Workbench to define the structural foundation of the food delivery management system. At this stage, the primary objective was to identify and represent the key entities that reflect the system’s operational workflow — Customers, Restaurants, Orders, Deliveries, and Riders — and to specify their fundamental attributes and data types. Each table was created with a primary key (PK) to ensure unique record identification and facilitate future relationship mapping.

During this phase, the design focus was on data organization and entity definition rather than relational constraints. Tables such as Customers and Restaurants were structured to capture essential business information including customer names, registration dates, restaurant details, and operating hours. The Orders table was designed to represent the transaction core of the system, containing attributes like Order_Item, Order_Date, Order_Time, Order_Status, and Total_Amount to track each customer’s purchase details. Similarly, Deliveries and Riders were introduced to handle the logistics and workforce aspects of the platform, including delivery status, assigned riders, and sign-up dates.

This initial modeling phase established a clear logical framework for the system, laying the groundwork for normalization, foreign key integration, and referential integrity enforcement in later stages. The entity layout was carefully arranged to reflect real-world relationships while maintaining scalability for additional features, such as order tracking, delivery scheduling, and customer interaction analytics. Overall, this step transformed the project’s conceptual requirements into a concrete, data-driven architecture that would guide subsequent stages of relational modeling and implementation.

Step 2: Establishing Relationships and Enforcing Referential Integrity

Following the initial schema design, the next step in the project involved establishing logical relationships between the entities defined in the database model. Using MySQL Workbench, the tables created in the earlier phase were connected through foreign key constraints to accurately represent real-world dependencies and ensure referential integrity within the system. This relational mapping formed the backbone of the food delivery management database, allowing seamless interactions between customers, restaurants, orders, riders, and deliveries.

At this stage, one-to-many and one-to-one relationships were defined based on operational requirements. Each Customer and Restaurant was linked to multiple Orders, enabling the tracking of purchase histories and restaurant-specific transactions. The Orders table, acting as the central entity, was connected to Deliveries to represent the fulfillment process, while Riders were associated with deliveries to manage assignment and logistics tracking. These relationships established the foundation for transactional workflows such as placing an order, assigning a rider, and updating delivery status.

Technically, this step ensured that all dependent data adhered to integrity constraints, preventing orphaned records and maintaining data consistency across tables. Data types and constraints were refined for better precision, such as defining time-based fields (TIME, DATE) for scheduling and monetary fields (FLOAT) for order totals. By implementing these foreign key connections, the schema evolved from a static entity layout into a fully functional relational data model capable of supporting complex queries, joins, and transactional operations. This step marked the transition from logical design to a normalized and referentially sound database structure, forming the core framework for subsequent data population and testing phases.

Step 3: Forward Engineering

In this phase, the logical data model and schema, previously designed during conceptualization and relationship definition, are translated into a fully functional physical database. Forward engineering involves the systematic conversion of entity-relationship diagrams (ERDs) and normalized tables into actual SQL-based database structures. This includes generating CREATE TABLE statements for each entity, defining column data types, setting primary and foreign keys, and applying constraints such as NOT NULL, UNIQUE, CHECK, and DEFAULT values to ensure data integrity. Indexes are strategically created to optimize query performance, and storage parameters are configured in accordance with anticipated data volume and access patterns. During this process, database management system (DBMS)-specific syntax and features are considered, allowing the database to be deployed in the target environment. Forward engineering ensures that the abstract data model is implemented accurately, maintaining all defined relationships, integrity rules, and business logic while preparing the database for subsequent operations, including data population, querying, and application integration.

Step 4: ERD Visualization and Schema Verification

At this stage, the database schema is fully materialized, and the previously conceptualized entities are represented as concrete tables within the DBMS. Each table’s columns, along with their data types, constraints, and key definitions, are clearly visible, allowing for a comprehensive inspection of the schema structure. The Entity-Relationship Diagram (ERD) can now be viewed in its physical form, providing a visual confirmation that all entities, attributes, and relationships have been correctly implemented. Referential integrity is verifiable, with foreign key links accurately reflecting the established relationships. This phase enables database designers and stakeholders to validate the completeness and correctness of the schema, ensuring that all normalization rules, constraints, and indexing strategies are in place before proceeding to data population or application integration. The ERD in its table form serves as both a documentation tool and a blueprint for further database maintenance and optimization.

Conclusion

The database development process, from initial conceptualization to physical implementation, demonstrates a systematic approach to designing a robust and efficient data management system. By carefully modeling entities and relationships, enforcing referential integrity, and forward engineering the logical schema into a fully operational database, the project ensures both data accuracy and consistency. Visualizing the ERD as actual tables provides clarity and confirms the correct implementation of constraints, keys, and relationships. This structured methodology not only facilitates seamless data operations and querying but also lays a solid foundation for future scalability, optimization, and integration with applications, ensuring the database can effectively support organizational objectives and evolving business needs.