🚀 Spark Bulk Data Processing Project

📌 Overview

This project demonstrates a Spark-based bulk data processing pipeline designed to handle large-scale datasets efficiently using Apache Spark. It focuses on high performance, scalability, and fault tolerance, making it suitable for enterprise data engineering and analytics workloads.

The project showcases how to:

• Read bulk data from multiple sources
• Apply distributed transformations
• Optimize performance using Spark best practices
• Write processed data in optimized formats

---

🏗️ Architecture

Data Source (CSV / Parquet / Hive)
        ↓
Spark Read Layer
        ↓
Transformations & Business Logic
        ↓
Optimized Output (Parquet / Hive / Delta)

---

🧰 Tech Stack

• Apache Spark (3.5.x / 4.x compatible)
• PySpark
• Python 3.10+
• Hadoop (Windows compatible setup)
• Parquet / CSV / Hive

---

📂 Project Structure

spark-bulk-data-processing/
│
├── src/
│   ├── main.py                 # Spark entry point
│   ├── reader.py               # Data ingestion logic
│   ├── transformer.py          # Business transformations
│   ├── writer.py               # Output writer logic
│   └── config.py               # Spark & app configurations
│
├── data/
│   ├── input/                  # Raw input data
│   └── output/                 # Processed data
│
├── logs/                        # Application logs
├── requirements.txt
└── README.md

---

⚙️ Features

• Bulk data ingestion using Spark DataFrame API
• Schema inference and validation
• Distributed transformations
• Partitioning and file optimization
• Fault-tolerant execution
• Windows & Linux compatible

---

▶️ How to Run the Project

1️⃣ Create Virtual Environment

python -m venv .venv
source .venv/bin/activate   # Linux/Mac
.venv\Scripts\activate      # Windows

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Configure Environment (Windows)

os.environ["PYSPARK_PYTHON"] = "path_to_python.exe"
os.environ["PYSPARK_DRIVER_PYTHON"] = "path_to_python.exe"

4️⃣ Run Spark Job

python src/main.py

---

📊 Sample Transformation Logic

• Data cleansing and filtering
• Column standardization
• Aggregations and joins
• Partitioning by business keys

---

📈 Performance Optimizations Used

• Columnar storage (Parquet)
• Predicate pushdown
• Partition pruning
• Avoiding shuffles where possible
• Lazy evaluation

---

🛠️ Output

• Optimized Parquet files
• Hive-compatible directory structure
• Ready for analytics and reporting

---

🧪 Testing

• Sample data validation
• Schema verification
• Row count reconciliation

---

🚀 Future Enhancements

• Integration with Hive Metastore
• Delta Lake support
• Spark Structured Streaming
• Deployment on Kubernetes
• Airflow orchestration

---

👤 Author

Bharat Singh
Senior Java & AWS Cloud Development Lead
Expertise: Spark | Python | AWS | Data Engineering

---

📄 License

This project is licensed for learning and demonstration purposes.