SUMMARY
For my Large-Scale Data Storage course at Northeastern, I built a graph database API from scratch to model a social network of people, books, and their relationships. The project explores how graph structures can be used to store and query connected data in a way that relational databases aren't naturally suited for.
The API is built in Python using Redis as the underlying database, storing nodes (people, books, prices) as hash sets and relationships (bought, knows) as adjacency lists with typed edges. On top of that, I wrote SQL queries against a relational version of the same schema to compare how the two approaches handle the same graph traversal problems. The project culminates in a book recommendation engine that surfaces titles purchased by a person's connections. This was to mimic a "people who bought this also bought" pattern used by platforms like Amazon.
