This repository demonstrates hands-on experience with modern big data technologies and cloud computing infrastructure. Through a series of structured exercises and projects, I've built practical expertise in distributed data processing, NoSQL databases, data warehousing, and real-time analytics using industry-standard tools hosted on AWS Elastic MapReduce (EMR).
- Cloud Platform: Amazon Web Services (AWS) - Elastic MapReduce (EMR)
- Distributed Processing: Apache Hadoop MapReduce, Apache Spark (PySpark)
- Data Warehousing: Apache Hive (HiveQL)
- NoSQL Database: Apache HBase
- Programming Languages: Python, SQL
- Environment: Jupyter Notebooks, AWS EMR Clusters
- Implemented scalable MapReduce jobs for distributed data processing
- Designed custom mapper and reducer functions for large-scale data transformations
- Optimized data partitioning and shuffling strategies for performance
- Processed multi-gigabyte datasets across distributed cluster nodes
Key Skills: Distributed computing, parallel processing, data partitioning, performance optimization
- Designed and implemented scalable NoSQL database schemas for high-throughput applications
- Performed CRUD operations on large-scale distributed datasets
- Optimized row key design for efficient data retrieval and scan operations
- Managed column families and implemented data versioning strategies
Key Skills: NoSQL database design, schema optimization, distributed data storage, data modeling
- Executed complex SQL-like queries using HiveQL for data warehousing tasks
- Created and managed partitioned and bucketed tables for optimized query performance
- Implemented ETL pipelines for structured and semi-structured data
- Performed advanced analytical queries including joins, aggregations, and window functions
Key Skills: Data warehousing, HiveQL, ETL processes, query optimization, analytical reporting
- Built distributed data processing applications using PySpark
- Implemented RDD transformations and actions for efficient data manipulation
- Developed batch processing workflows for large-scale data analysis
- Extracted actionable insights from complex datasets using Spark DataFrames and Spark SQL
Key Skills: PySpark, RDD operations, Spark DataFrames, Spark SQL, batch processing, data analysis
✅ Successfully deployed and managed multiple AWS EMR clusters for distributed computing
✅ Processed and analyzed large-scale datasets (GB-TB range) using distributed frameworks
✅ Demonstrated proficiency across the entire big data ecosystem (storage, processing, querying)
✅ Applied industry best practices for data partitioning, optimization, and cluster management
✅ Integrated multiple big data technologies to build comprehensive data pipelines
| Technology | Use Case | Key Features Implemented |
|---|---|---|
| Hadoop MapReduce | Distributed batch processing | Custom mappers/reducers, data partitioning, shuffling optimization |
| HBase | Real-time NoSQL storage | Row key design, column families, distributed scans, bulk loading |
| Hive | Data warehousing & SQL analytics | Partitioning, bucketing, complex queries, ETL pipelines |
| Spark | In-memory distributed processing | RDD transformations, DataFrames, Spark SQL, batch analytics |
| AWS EMR | Managed Hadoop/Spark clusters | Cluster provisioning, scaling, resource management |
Through these projects, I developed comprehensive skills in:
- Distributed Systems Architecture: Understanding of cluster computing, data locality, and fault tolerance
- Cloud Infrastructure: Hands-on experience with AWS EMR for big data workloads
- Data Pipeline Development: End-to-end ETL processes from ingestion to analysis
- Performance Optimization: Query tuning, data partitioning, and resource management
- Multi-Technology Integration: Combining different tools for comprehensive data solutions
Each project folder contains detailed documentation with:
- Problem statements and objectives
- Implementation approaches and code samples
- Results and performance metrics
- Key learnings and best practices
Interested in discussing big data solutions or potential collaboration opportunities?
Note: All projects were completed as part of advanced coursework in Big Data Technologies and deployed on AWS EMR infrastructure.
Last Updated: December 2025