Azure Data Factory and Databricks End-to-End Project

📋 Overview

An end-to-end data engineering solution implementing the Medallion Architecture (Bronze-Silver-Gold) for processing and analyzing trip transaction data using Azure cloud services. This project automates data ingestion, transformation, and analytics workflows with built-in monitoring and email notifications.

🎯 Project Impact

• Automated Data Pipeline: Seamless data flow from Azure SQL Database to structured storage layers
• Real-Time Processing: Continuous data ingestion and transformation for up-to-date insights
• Business Intelligence: Advanced analytics on customer behavior, driver performance, and operational metrics
• Scalability: Cloud-native architecture handles growing data volumes efficiently
• Data Quality: Medallion Architecture ensures progressive data refinement and validation

🏗️ Architecture

Azure SQL Database → ADF → Bronze Layer (Raw Data) 
                             ↓
                    Silver Layer (Cleaned & Transformed)
                             ↓
                    Gold Layer (Business Insights)
                             ↓
                    Hive Metastore + Email Notifications

🛠️ Tech Stack

Component	Technology
Languages	Python, SQL, PySpark
Data Integration	Azure Data Factory (ADF)
Data Storage	Azure Data Lake Storage Gen2 (ADLS)
Data Processing	Azure Databricks (Apache Spark)
Database	Azure SQL Database
Orchestration	ADF Pipelines, Logic Apps
Storage Format	Delta Lake

✨ Key Features

Delta Lake Benefits

• ✅ ACID Transactions - Reliable data operations with atomicity and consistency
• 📚 Data Versioning - Track changes and enable time travel queries
• 🔒 Schema Enforcement - Ensure data quality and consistency
• ⚡ Optimized Performance - Advanced indexing for faster queries

Pipeline Capabilities

• 📥 Full & Incremental Loads - Efficient data synchronization
• 🔄 Automated Scheduling - Hands-free pipeline execution
• 📧 Email Notifications - Real-time alerts via Logic Apps
• 📊 Monitoring Dashboard - Track pipeline health and performance

📊 Use Cases

1. Ride Analytics

   • Identify top-rated drivers and peak demand periods
   • Track trip delays and payment statuses
   • Analyze customer travel patterns

2. Driver Performance

   • Highest number of trips per driver
   • Driver ratings and customer satisfaction metrics

3. Financial Insights

   • Highest-spending customers
   • Fare trends and revenue analysis
   • Distance-based pricing optimization

🚀 Quick Start

Prerequisites

• Azure Subscription (Free Account)
• Azure CLI (optional)
• Basic knowledge of SQL and Python

Setup Steps

1. Create Azure Resources

   - Resource Group
   - Azure SQL Database
   - Azure Data Lake Storage Gen2
   - Azure Databricks Workspace
   - Azure Data Factory

2. Configure Data Source

   • Upload sample CSV files to Bronze folder
   • Execute PySpark script to populate Azure SQL Database
   • Configure firewall rules for Databricks access

3. Set Up Databricks

   • Create compute cluster
   • Import notebooks (Bronze/Silver/Gold zones)
   • Configure ADLS access credentials

4. Build ADF Pipeline

   • Create dataflows for data extraction
   • Configure incremental load filters
   • Set up Logic Apps for email notifications

5. Execute & Monitor

   • Run the pipeline manually or schedule
   • Monitor execution in ADF dashboard
   • Verify data in each layer (Bronze → Silver → Gold)

📁 Project Structure

├── Bronze_Zone/
│   └── Delta_Lake_Validation.py      # Raw data validation
├── Silver_Zone/
│   ├── Customer_Dimension.py         # Customer dimension table
│   ├── Driver_Dimension.py           # Driver dimension table
│   ├── Date_Dimension.py             # Date dimension table
│   ├── Trip_Transaction_Fact.py      # Trip fact table
│   └── Rewards_Points_Fact.py        # Rewards fact table
├── Gold_Zone/
│   └── Final_Reports.py              # Business insights & analytics
└── PySpark_upload_data_to_DB_Script.py

📚 Data Flow

1. Bronze Layer - Raw data ingestion from Azure SQL Database in Delta format
2. Silver Layer - Data cleansing, transformation, and creation of fact/dimension tables
3. Gold Layer - Aggregated analytics and business insights stored in Hive Metastore

🔐 Best Practices Implemented

• Multi-Factor Authentication (MFA) enabled
• Least privilege access control
• Budget alerts and cost monitoring
• Soft delete disabled for real-time access
• Secrets management for credentials
• Resource naming conventions

📖 Learn More

Official Documentation

• Azure Data Factory
• Azure Databricks
• Delta Lake
• PySpark

Tutorials & Guides

• Medallion Architecture
• Azure Logic Apps
• ADLS Gen2 Best Practices

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

📝 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Built with Azure cloud services
• Powered by Apache Spark and Delta Lake
• Inspired by modern data engineering practices

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Note: Remember to delete Azure resources after use to avoid unnecessary charges. Always monitor your Azure cost analysis dashboard.