Recommendation System for Big Data

Academic Project

Overview

Developed a scalable recommendation system designed to handle large-scale data processing using distributed computing frameworks on a multi-node Hadoop cluster.

Key Contributions

• Implemented Breadth-First-Search algorithms optimized for distributed execution
• Deployed system on 4-node Hadoop cluster for distributed processing
• Utilized Apache Spark for in-memory computing and fast data processing
• Integrated MapReduce paradigm for efficient large-scale data analysis

Technical Skills Demonstrated

• Big Data Technologies: Hadoop, Spark, MapReduce
• Distributed Computing: Multi-node cluster management and optimization
• Algorithm Design: Graph traversal algorithms adapted for distributed systems
• Data Processing: Large-scale data analysis and recommendation generation

Impact

This project provided hands-on experience with industry-standard big data tools and distributed computing paradigms essential for modern data-intensive applications.