Answer: Yes

## Detailed Analysis ### Solution Components Evaluation **Azure Data Factory Schedule Trigger**: This component properly addresses the requirement for a **daily process** by enabling scheduled execution of the pipeline, which aligns with the incremental data ingestion need. **Azure Databricks Notebook Execution**: This is the critical component for R script execution. Azure Databricks fully supports R language through: - R notebooks with native R kernel - SparkR integration for distributed R processing - Support for R packages and libraries - Ability to read from Azure Data Lake Storage **Data Insertion into Azure Synapse Analytics**: The solution can handle this requirement in two ways: 1. **Within the Databricks notebook itself** - Using JDBC connectors or Synapse connector to write directly to the data warehouse 2. **Through subsequent ADF activities** - Though this approach has limitations with data passing between activities ### Why This Solution Works 1. **End-to-End Coverage**: The solution addresses all three requirements: - **Ingestion**: ADF can read incremental data from Azure Data Lake Storage - **Transformation**: Databricks notebook executes R script for data transformation - **Loading**: Data can be inserted into Synapse Analytics 2. **Technical Feasibility**: - ADF's Databricks Notebook activity can execute notebooks containing R code - Databricks can connect to both Azure Data Lake Storage and Azure Synapse Analytics - The entire workflow can be orchestrated through a single scheduled pipeline 3. **Best Practices Alignment**: This approach follows Azure's recommended pattern for ETL/ELT workflows where: - ADF handles orchestration and scheduling - Databricks handles complex transformations (including R-based) - Specialized services perform their optimal functions ### Potential Implementation Approaches - **Single Notebook Approach**: The Databricks notebook can perform all operations - read from ADLS, transform using R, and write to Synapse - **Multi-Activity Approach**: While technically possible, passing transformed data between ADF activities has limitations and is less efficient ### Conclusion The proposed solution **fully meets the goal** as it provides a complete, technically sound approach to: - Schedule daily incremental data processing - Execute R-based transformations via Databricks - Load transformed data into the target data warehouse - Maintain proper data flow and orchestration This represents a valid and commonly implemented pattern in Azure data engineering workflows.

Author: LeetQuiz Editorial Team