Data Engineering Projects

Welcome to my comprehensive Data Engineering Projects repository! 🚀

This collection showcases modern data engineering implementations, architectural patterns, and industry best practices. Each project demonstrates end-to-end solutions for real-world data challenges, from raw data ingestion to business-ready analytics.

🎯 About This Repository

As a data engineering practitioner, I've created this repository to:

• 📚 Share Knowledge: Document proven data engineering patterns and implementations
• 🛠️ Demonstrate Skills: Showcase technical expertise across the data engineering stack
• 🏗️ Provide Templates: Offer reusable architectures for common data scenarios
• 📈 Track Evolution: Document the journey from concept to production-ready solutions

💡 What You'll Find Here

This repository contains production-quality data engineering projects that cover:

• Data Warehousing & Lakes: Modern medallion architecture implementations
• ETL/ELT Pipelines: Scalable data processing and transformation workflows
• Streaming Analytics: Real-time data processing with Apache Kafka and similar technologies
• Data Modeling: Dimensional modeling, star schemas, and data vault approaches
• Cloud Solutions: Cloud-native data architectures and services
• Data Quality: Monitoring, validation, and governance frameworks

Each project includes:

• ✅ Complete Implementation - Working code and configurations
• ✅ Comprehensive Documentation - Architecture diagrams and technical specs
• ✅ Best Practices - Industry-standard approaches and patterns
• ✅ Sample Data - Realistic datasets for testing and demonstration

🚀 Projects Overview

1. Data Warehouse Project

A comprehensive implementation of a modern data warehouse using the Medallion Architecture (Bronze-Silver-Gold) pattern.

Key Features:

• 🥉 Bronze Layer: Raw data ingestion from CRM and ERP systems
• 🥈 Silver Layer: Data transformation and cleansing with business rules
• 🥇 Gold Layer: Dimensional modeling with star schema for analytics
• 📊 Complete ETL Pipeline: T-SQL stored procedures for data processing
• 📋 Data Catalog: Comprehensive documentation of all tables and columns
• 🎨 Visual Documentation: Architecture diagrams and data flow visualizations

Technologies: SQL Server, T-SQL, Medallion Architecture, Dimensional Modeling

Status: ✅ Complete - Production Ready

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2. Kafka Streaming Project

A modern real-time ETL pipeline that simulates streaming marketing data from Marketo to Snowflake using Apache Kafka with KRaft mode.

Key Features:

• 🔄 Real-time Streaming: Apache Kafka with modern KRaft architecture (no ZooKeeper)
• 📊 Data Lake: MinIO (S3-compatible) for raw data storage with time-based partitioning
• ⚡ Stream Processing: Real-time data enrichment, scoring, and aggregation
• 🏢 Enterprise Integration: Snowflake data warehouse loading with batch processing
• 🔍 Observability: Confluent Control Center for monitoring and Redis for caching
• 🏗️ Industry Architecture: Lambda/Kappa architecture patterns used by major tech companies

Technologies: Apache Kafka, Python, Docker, MinIO, Redis, Snowflake, Confluent Platform

Status: ✅ Complete - Production Ready

Visual Evidence:

• 🎯 Real-time Kafka dashboard with live message flow
• 📊 Data lake storage showing time-partitioned Parquet files
• 🏗️ Complete system architecture diagram
• ⚡ Live streaming data processing in action

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🏗️ Repository Structure

📁 Data Engineering Projects/
├── 📁 data-warehouse-project/        # Medallion architecture data warehouse
│   ├── 📁 docs/                      # Documentation and diagrams
│   ├── 📁 Scripts/                   # SQL implementation files
│   │   ├── 📁 bronze/                # Bronze layer scripts
│   │   ├── 📁 silver/                # Silver layer scripts
│   │   └── 📁 gold/                  # Gold layer scripts
│   ├── 📁 source_crm/                # Sample CRM data
│   ├── 📁 source_erp/                # Sample ERP data
│   └── README.md                     # Detailed project documentation
├── 📁 Kafka-Marketo-S3-Snowflake-project/  # Real-time streaming ETL pipeline
│   ├── 📁 dataSource/                # Simulated Marketo data generation
│   ├── 📁 scripts/                   # ETL pipeline Python code
│   │   ├── 📁 config/                # Configuration management
│   │   ├── 📁 utils/                 # Shared utilities and validators
│   │   ├── 📁 producers/             # Kafka data producers
│   │   ├── 📁 processors/            # Stream processing components
│   │   └── 📁 consumers/             # Data consumers (Snowflake)
│   ├── 📁 docs/                      # Project documentation and screenshots
│   ├── docker-compose.yml            # Kafka infrastructure setup
│   ├── .env.example                  # Environment configuration template
│   ├── .gitignore                    # Git ignore patterns for streaming project
│   └── README.md                     # Detailed project documentation
└── README.md                         # This file

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🛠️ Technologies & Tools

Databases & Data Storage:

• Microsoft SQL Server
• Snowflake Data Warehouse
• MinIO (S3-compatible storage)
• Redis (Caching & Session Management)

Streaming & Processing:

• Apache Kafka (KRaft mode)
• Confluent Platform (Schema Registry, Control Center)
• Python (Pandas, Pydantic, Structured Logging)
• Docker & Docker Compose

Architecture Patterns:

• Medallion Architecture (Bronze-Silver-Gold)
• Lambda/Kappa Architecture (Real-time + Batch processing)
• Dimensional Modeling & Star Schema Design
• Event-Driven Architecture

Development & Documentation:

• T-SQL for data warehouse processing
• Python for stream processing and ETL
• Git version control with project-specific .gitignore files
• Draw.io for architecture diagrams
• Markdown for technical documentation
• Notion for project planning

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🎯 Skills Demonstrated

Data Engineering Fundamentals:

• Data Warehousing: End-to-end implementation from source to analytics
• ETL/ELT Processes: Both batch and real-time data processing pipelines
• Data Modeling: Dimensional modeling, star schema, and streaming data structures
• Data Architecture: Medallion and Lambda/Kappa architecture implementations

Modern Streaming Technologies:

• Real-time Processing: Apache Kafka streaming with KRaft mode
• Stream Analytics: Data enrichment, scoring, and real-time aggregations
• Event-Driven Architecture: Producer-consumer patterns and message queues
• Data Lake Implementation: S3-compatible storage with time-based partitioning

Development & Operations:

• Python Development: Complex ETL pipelines with proper error handling
• SQL Development: Complex T-SQL procedures and transformations
• Containerization: Docker and Docker Compose for infrastructure
• Data Validation: Pydantic models for schema enforcement and data quality

Enterprise Integration:

• Cloud Data Warehouses: Snowflake integration and batch loading
• Monitoring & Observability: Confluent Control Center and structured logging
• Configuration Management: Environment-based configuration and secrets
• Documentation: Comprehensive technical and architectural documentation

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📚 Additional Documentation

Project Documentation

Visit the Notion Workspace for extended project documentation, planning notes, and analysis.

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🚀 Getting Started

Each project contains its own detailed README with:

• Architecture overview
• Installation instructions
• Usage examples
• Technical documentation

Navigate to the specific project folder to get started!

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🤝 Contributing

These projects demonstrate data engineering best practices and are available for:

• Learning and educational purposes
• Reference implementations
• Portfolio demonstration
• Technical discussions

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📄 License

This repository is available under the MIT License. See individual project folders for specific licensing information.

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Showcasing modern data engineering practices and implementations.