A comprehensive learning repository for Microsoft Azure Data Engineering that demonstrates modern cloud data pipeline development using Azure Databricks, Azure Data Factory, Delta Live Tables, and Azure Key Vault. This project provides hands-on examples and real-world use cases for building scalable data engineering solutions on Azure.
- Project Overview
- Repository Structure
- Azure Databricks
- Azure Data Factory
- Azure Key Vault
- Prerequisites
- Getting Started
- Learning Path
- Use Cases & Examples
- Contributing
This repository serves as a complete learning platform for Azure data engineering, focusing on:
- Big Data Processing with Azure Databricks and Apache Spark
- Data Pipeline Orchestration with Azure Data Factory
- Real-time Analytics with Delta Live Tables
- Security Management with Azure Key Vault
- Data Lake Architecture and best practices
- ETL/ELT Workflows in the Azure ecosystem
Azure-DataEngineering/
├── 📂 Azure Databricks/ # Databricks notebooks and Delta Lake examples
│ ├── 📓 Databricks_Commands.ipynb # Basic Databricks operations
│ └── 📂 Delta Live Tables/ # Advanced Delta Live Tables examples
├── 📂 Azure DataFactory/ # Data Factory pipelines and configurations
│ ├── 📂 DataFactory Repo/ # Complete ADF repository structure
│ └── 📂 Usecases/ # Real-world ADF use cases
├── 📂 Azure-KeyVault/ # Key Vault integration examples
└── 📄 README.md # This file
Location: Azure Databricks/
Essential Databricks operations and mounting configurations:
- DBFS Mounting: Connect to Azure Blob Storage
- Storage Integration: Access external data sources
- Authentication: Secure connection patterns
Delta Live Tables: Delta Live Tables/
Advanced data pipeline development with Delta Lake:
| Notebook | Focus | Description |
|---|---|---|
| 01_DeltaLiveTable_Basics.ipynb | Fundamentals | Delta table creation, CRUD operations, and time travel |
| 02_DeltaLiveTable_Pipeline_Output.ipynb | Pipeline Results | Analyzing and monitoring pipeline outputs |
| 02_DeltaLiveTable_Pipeline.ipynb | Pipeline Design | Building end-to-end data pipelines |
| 03_DeltaLiveTable_Pipeline.ipynb | Advanced Pipelines | Complex pipeline patterns and optimizations |
- ✅ Delta Lake Operations: Create, Read, Update, Delete (CRUD)
- ✅ Time Travel: Query historical data versions
- ✅ Data Versioning: Track changes and rollbacks
- ✅ Pipeline Orchestration: Automated data workflows
- ✅ Performance Optimization: Table optimization and cleanup
- ✅ Unity Catalog: Modern data governance
Location: Azure DataFactory/
DataFactory Repository: DataFactory Repo/
Complete Azure Data Factory project structure with production-ready components:
| Component | Directory | Description |
|---|---|---|
| 📊 Dataflows | dataflow/ |
Data transformation logic |
| 📝 Datasets | dataset/ |
Data source and sink definitions |
| 🔗 Linked Services | linkedService/ |
Connection configurations |
| 🚀 Pipelines | pipeline/ |
Orchestration workflows |
| ⏰ Triggers | trigger/ |
Pipeline execution schedules |
| Dataflow | Purpose | Description |
|---|---|---|
| DataflowPipeline_for_DimMovies.json | Dimension Table | Movie dimension data processing |
| DataflowPipeline_for_DimOnlineService.json | Service Dimension | Online service dimension creation |
| DataflowPipeline_for_FactOnlinePurchase.json | Fact Table | Online purchase fact table ETL |
| FactOnlinePurchase_MonthlySnapshot.json | Snapshots | Monthly aggregation snapshots |
Real-World Use Cases: Usecases/
Practical examples covering common data engineering scenarios:
| Use Case | Requirement | Implementation |
|---|---|---|
| Requirement 01 | File Copy | Copy CSV files between blob storages |
| Requirement 02 | File Cleanup | Delete processed files from blob storage |
| Requirement 03 | SQL to Blob | Extract data from SQL Database to Blob |
| Requirement 04 | Databricks Integration | Orchestrate Databricks notebooks |
| Requirement 05 | Database Migration | Copy data between SQL databases |
| Requirement 06 | Data Transformation | Implement dataflow activities |
| Requirement 07 | Pipeline Monitoring | Monitor and troubleshoot pipelines |
| Requirement 08 | CI/CD Deployment | Implement DevOps for Data Factory |
- ✅ Data Integration: Connect 90+ data sources
- ✅ ETL/ELT Pipelines: Transform data at scale
- ✅ Hybrid Connectivity: On-premises and cloud integration
- ✅ Monitoring & Alerting: Pipeline health and performance
- ✅ CI/CD Integration: DevOps best practices
- ✅ Cost Optimization: Efficient resource utilization
Location: Azure-KeyVault/
Secure secrets management and authentication:
| Component | Purpose | Description |
|---|---|---|
| 01_Create_secrets_from_python.py | Secret Management | Python-based secret creation and retrieval |
| requirements.txt | Dependencies | Required Azure SDK packages |
- ✅ Secret Storage: Secure API keys and connection strings
- ✅ Access Policies: Fine-grained permission control
- ✅ Auditing: Complete access logging
- ✅ Integration: Seamless Azure service integration
- Python 3.8+ with pip
- Azure CLI for authentication
- Azure Databricks workspace access
- Azure Data Factory instance
- Azure Key Vault access
- Azure Subscription: Active Azure subscription
- Resource Group: Create dedicated resource group
- Storage Account: Azure Data Lake Storage Gen2
- Databricks Workspace: Premium tier recommended
- Data Factory: V2 instance
- Key Vault: Standard tier
# Core Azure SDKs
pip install azure-identity
pip install azure-keyvault-secrets
pip install azure-storage-blob
pip install azure-mgmt-datafactory
# Databricks integration
pip install databricks-cli
pip install delta-sparkgit clone https://github.com/TheDataArtisanDev/Azure-DataEngineering.git
cd Azure-DataEngineering# Login to Azure
az login
# Set your subscription
az account set --subscription "your-subscription-id"
# Create service principal (for automation)
az ad sp create-for-rbac --name "data-engineering-sp"# Install Databricks CLI
pip install databricks-cli
# Configure authentication
databricks configure --token# Create Key Vault
az keyvault create --name "your-keyvault" --resource-group "your-rg"
# Set secrets
az keyvault secret set --vault-name "your-keyvault" --name "storage-key" --value "your-key"# Test Key Vault connection
cd Azure-KeyVault
python 01_Create_secrets_from_python.pyAfter completing this repository, you will understand:
- ✅ Azure Data Platform architecture and services
- ✅ Databricks & Spark for big data processing
- ✅ Delta Lake for reliable data lakes
- ✅ Data Factory for orchestration and integration
- ✅ Security Best Practices with Key Vault
- ✅ Production Deployment and monitoring
- ✅ Cost Optimization strategies
- 🏪 Retail Analytics: Customer purchase behavior analysis
- 🎬 Entertainment: Movie rating and recommendation systems
- 💰 Financial Services: Transaction processing and monitoring
- 📊 Business Intelligence: Dimensional modeling and reporting
- 🔄 Data Migration: Legacy system modernization
- Medallion Architecture: Bronze, Silver, Gold data layers
- CDC (Change Data Capture): Real-time data synchronization
- Metadata-Driven Pipelines: Dynamic and scalable ETL
- Error Handling: Robust pipeline resilience
- Performance Tuning: Optimized data processing
This is a learning project focused on Azure data engineering. Contributions are welcome:
- 💡 Suggest new use cases or Azure services
- 🐛 Report issues or improvements
- 📚 Add documentation or examples
- 🔧 Optimize existing pipelines or code
For major changes, please open an issue first to discuss your ideas.
- Azure Data Factory Documentation
- Azure Databricks Documentation
- Delta Lake Documentation
- Azure Key Vault Documentation
- Azure Data Lake Storage
Happy Learning! 🚀
This repository provides a comprehensive journey through Azure data engineering. Use it to build production-ready data solutions on Microsoft Azure!