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Azure Data Factory is specifically designed for data integration and transformation tasks, making it the most suitable service for transforming data from Azure Blob storage to Azure Data Lake Storage using mapping data flow.

Mapping data flow within Azure Data Factory allows users to visually design data transformation pipelines without writing complex code. It provides a rich set of transformations and allows for the orchestration of data movement and transformation, including data sourced from Azure Blob storage and data destined for Azure Data Lake Storage. This makes it particularly effective for ETL (extract, transform, load) processes.

On the other hand, while Azure Databricks is powerful for processing data with Apache Spark, it requires more setup and is better suited for interactive data analytics and machine learning as opposed to standard data integration workflows. Azure Storage Sync is primarily for synchronizing data between local file servers and Azure file shares, which does not pertain to data transformation between blob storage and data lake. Azure Data Box Gateway is focused on transferring data between on-premises and Azure, not specifically for transforming data within Azure.

Thus, Azure Data Factory stands out as the optimal solution for this specific requirement.