Database-Driven Yelp Analytics

📘 Description

This project uses EdgeDB to construct a graph-relational schema over Yelp’s business and review data, enabling advanced queries and structured analytics. Built for the MSDS 420-3 course, it highlights database design, data modeling, and query optimization.

Yelp’s raw data is transformed into a semantic knowledge graph that connects businesses, users, and reviews through typed relationships. From here, advanced queries reveal spatial patterns, user preferences, and categorical trends across Chicago’s business ecosystem.

⸻

🔧 Features

• Graph-Relational Modeling: Implements a hybrid data model using EdgeDB to connect relational tables with graph relationships.
• Normalized Schema: Structures Yelp data into entities like Business, User, and Review, with integrity constraints and indexes.
• Advanced Querying: Demonstrates queries using EdgeQL to extract insights like top-rated categories, prolific reviewers, and spatial outliers.
• Chicago-Focused Analytics: Filters the dataset to spotlight insights from Yelp businesses located in Chicago, IL.
• Visualization: Exports data to CSV for visualization of review patterns, business clusters, and user sentiment analysis.

⸻

💡 Key Insight

Database-first AI products require intentional design. This project shows how structuring and enriching data upfront leads to faster, deeper insights — with less reliance on LLM guesswork.

🔗 View the source code on GitHub