Overview

This project involved designing and specifying a robust database architecture for Runzy, a platform that connects runners, event organizers, and coaches through a streamlined event management system. The project focused on integrating multiple data sources, consolidating legacy and modern systems, and enhancing real-time interactions.

Key technical aspects included:

• Hybrid Database Architecture: Leveraging MySQL for structured, relational data (user profiles, registrations, transactions) and MongoDB for semi-structured data (race reviews, personalized recommendations, training analytics).

• Data Normalization & ERD Modeling: Designed 3NF-compliant relational schemas and document-based NoSQL collections for optimized data storage and querying.

• Cross-Database Integration: Implemented GUIDs (Globally Unique Identifiers) to ensure seamless interoperability across MySQL and MongoDB.

• Middleware & API Strategy: Proposed REST/GraphQL APIs for unified access to both databases, enabling real-time data synchronization.

• Data Aggregation for Analytics: Designed structured ETL pipelines to aggregate event registration trends, athlete performance metrics, and financial insights.

• Migration of Legacy Systems: Proposed ETL workflows for migrating data from a ColdFusion-based system into MySQL and MongoDB.

• Personalized Recommendation Engine: Suggested AI-driven race recommendations using training performance data and historical event participation.

This database solution aimed to improve data accessibility, enhance user experience, and support business intelligence capabilities for Runzy, positioning it as a scalable platform in the $30 billion global running event market.

Motivation Statement

The Runzy Database Architecture Project was an opportunity to apply learnings outside the classroom consulting for a real startup. The development of this project was informed my passion for data modeling, database optimization, and real-time analytics. The opportunity to design an enterprise-grade, scalable data solution allowed me to apply my expertise in SQL, NoSQL, and API-driven architectures to solve a real-world problem in event management.

This project highlighted the importance of data interoperability and seamless integration in multi-platform ecosystems. By combining relational and non-relational database strategies, I tackled challenges in legacy system migration, real-time synchronization, and performance scalability.

I was particularly drawn to the technical depth of this project—designing a hybrid SQL-NoSQL architecture, optimizing query performance, and implementing middleware solutions to support future AI-driven personalization features. This experience reinforced my commitment to building data-driven, scalable systems that enhance user experiences and business decision-making.

Reflections and Learnings

This project provided deep insights into database engineering, data governance, and system scalability, with key takeaways including:

1. Optimizing Hybrid Databases for Performance – Learning to balance MySQL’s relational constraints with MongoDB’s schema flexibility was essential for high-speed querying and analytics.

2. Cross-System Data Integration & API Design – The use of RESTful and GraphQL APIs for unified data access highlighted best practices in scalable API architecture for data-heavy applications.

3. Handling Legacy System Migrations – Designing an ETL migration plan for ColdFusion-based data introduced me to challenges in data transformation, cleaning, and structuring for modern databases.

4. Real-Time Data Synchronization & Middleware – Understanding how Apache Kafka or RabbitMQ can enable event-driven updates between MySQL and MongoDB was a crucial learning experience.

5. AI-Driven Personalization & Recommendation Systems – Exploring personalized race recommendations reinforced my interest in machine learning for user engagement and retention.

This project significantly expanded my skills in database engineering, system architecture, and real-time analytics, and I look forward to further applying these concepts in scalable AI-driven platforms.

Feedback Received

Graded 100/100

Excellent well-thought out hybrid proposal for Runzy. 

-C. Todd Lombardo, Professor & Product & Strategy Advisor @ Runzy

Liked that used both MongoDB and SQL, really enjoyed the presentation.

-Anonymous Peer Reviewer.

Find presentation here!