🔥 Apache Spark Data Engineering Portfolio

End-to-end PySpark solutions demonstrating production-grade data engineering patterns

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📋 Overview

This portfolio showcases hands-on Apache Spark data engineering projects covering large-scale data processing, optimization techniques, and real-world pipeline patterns. Built to demonstrate enterprise-level skills aligned with modern data engineering roles.

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🛠️ Projects & Topics Covered

1. ⚡ Local Sort vs. Global Sort in Spark

• Comparative analysis of sortWithinPartitions() vs sort()
• Performance benchmarking and shuffle optimization
• Use cases for each sorting strategy in production pipelines

2. 🔄 ETL Pipeline Patterns

• Incremental data loading strategies
• Schema evolution handling
• Data quality checks and validation layers

3. 📊 Spark SQL & Analytics

• Window functions for time-series analysis
• Aggregation optimization with partition pruning
• Joins: broadcast, shuffle hash, sort merge

4. ☁️ Cloud Integration

• Azure Data Lake Storage Gen2 (ADLS) integration
• Delta Lake for ACID transactions
• Databricks-compatible notebook patterns

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🧰 Tech Stack

Category	Technologies
Processing	Apache Spark (PySpark), Spark SQL
Storage	Azure Data Lake Storage, Delta Lake, Parquet
Orchestration	Apache Airflow
Cloud	Microsoft Azure
Languages	Python, SQL

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📁 Repository Structure

Apache-Spark-Data-Engineering-Portfolio/
├── sorting/
│   ├── local_sort_vs_global_sort.ipynb    # Sort benchmarking
│   └── partition_optimization.py
├── etl/
│   ├── incremental_load_pattern.py         # Incremental ETL
│   └── schema_evolution_handler.py
├── sql/
│   ├── window_functions.sql                # Advanced SQL
│   └── aggregation_patterns.ipynb
└── cloud/
    └── azure_adls_integration.py           # Cloud integration

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🚀 Key Takeaways

• Scalability: Designed to handle TB-scale datasets in distributed environments
• Performance: Optimized with partition strategies, caching, and broadcast joins
• Production-Ready: Error handling, logging, and monitoring patterns included

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📫 Connect