This repository contains the full implementation and report for a Big Data assignment focused on modeling, loading, querying, and benchmarking an e-commerce dataset using three different database paradigms:
- PostgreSQL for the relational model
- MongoDB for the document-oriented model
- Neo4j for the graph model
The project includes database schemas, data cleaning scripts, loading scripts, analytical queries, benchmark outputs, screenshots, and the final report.
The goal of this assignment is to compare how different database systems handle the same analytical workload. The dataset was modeled and queried in PostgreSQL, MongoDB, and Neo4j, then benchmarked to evaluate performance differences across:
- Campaign effectiveness analysis
- Product recommendation analysis
- Product category search
The report also discusses modeling decisions, benchmarking methodology, and the strengths and weaknesses of each database paradigm.
bigdata-assignment/
├── scripts/
│ ├── clean_data.py
│ ├── load_data_psql.sql
│ ├── load_data_mongodb.js
│ ├── load_data_graph.cypher
│ ├── q1.sql
│ ├── q2.sql
│ ├── q3.sql
│ ├── q1.js
│ ├── q2.js
│ ├── q3.js
│ ├── q1.cypher
│ ├── q2.cypher
│ └── q3.cypher
│
├── screenshots/
│ ├── psql_model.png
│ ├── mongodb_model.png
│ ├── neo4j_model.png
│ └── query_result_screenshots...
│
├── output/
│ └── benchmarking_results...
│
├── bigdata_report.pdf
├── .gitignore
└── README.md