Answer: Azure Data Factory

## Detailed Explanation ### Requirements Analysis: - **Data Source**: On-premises database (db1) requiring self-hosted integration runtime - **Destination**: Azure Data Lake Storage Gen2 (dl1) - **Transformation**: Power Query for data transformation - **Project Structure**: Four separate projects, each in separate Git repositories - **Development Goal**: Minimize development effort ### Option Evaluation: **A: Azure Synapse Analytics** - Primarily focused on data warehousing and analytics workloads - Limited native Power Query integration compared to dedicated ETL tools - Not optimized for creating multiple separate pipeline projects with Git integration - Higher complexity for simple data movement scenarios **B: Azure Logic Apps** - Designed for workflow automation and application integration - Limited Power Query capabilities and data transformation features - Not specialized for data pipeline development and management - Would require significant custom development to meet requirements **C: Azure Data Factory** ✓ **OPTIMAL CHOICE** - **Native Power Query Integration**: ADF provides built-in Power Query data flows for complex transformations - **Self-Hosted Integration Runtime Support**: Perfectly matches the requirement for on-premises data connectivity - **Git Integration**: Supports source control with Azure Repos or GitHub, allowing separate repositories per project - **Minimal Development**: Low-code/no-code approach with pre-built connectors and templates - **Project Management**: Can organize multiple pipelines as separate projects with independent Git repositories - **Cost-Effective**: Pay-per-use model aligns with minimizing development overhead **D: Microsoft Power BI** - Primarily a business intelligence and visualization tool - Power Query is used for data preparation within Power BI Desktop, not for pipeline development - No native support for self-hosted integration runtime - Not designed for creating reusable data pipelines to Azure Data Lake Storage ### Why Azure Data Factory is the Best Fit: 1. **Power Query Integration**: ADF's data flows use Power Query M language natively, providing familiar transformation capabilities 2. **On-Premises Connectivity**: Self-hosted integration runtime is a core feature for connecting to on-premises databases 3. **Git-Based Development**: Supports CI/CD with Git integration for version control and collaboration 4. **Project Organization**: Can create separate ADF instances or use folders to organize the four projects independently 5. **Development Efficiency**: Visual interface, templates, and pre-built activities minimize coding requirements 6. **Azure Data Lake Integration**: Native connector for ADLS Gen2 with optimized performance The combination of Power Query support, self-hosted IR compatibility, Git integration, and development efficiency makes Azure Data Factory the ideal solution that meets all specified requirements while minimizing development effort.