Which Azure service is most commonly used for processing data in an ELT framework?

Azure Data Factory is the most commonly used service for processing data within an ELT (Extract, Load, Transform) framework. It serves as a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation. In an ELT process, data is first extracted from source systems and loaded into a staging area, such as a data lake or data warehouse.

Once the data is loaded, transformations can occur directly in the data storage location, leveraging the scalable computing capabilities of platforms like Azure Databricks or SQL pools in Azure Synapse Analytics for high-performance processing. Azure Data Factory is specifically designed to manage and automate these processes, enabling users to create pipelines that coordinate the movement and transformation of data efficiently.

In contrast, while Azure Databricks provides robust capabilities for performing data transformations using Apache Spark, it is generally utilized within the transformation step of an ELT or ETL process. Azure Data Lake Storage acts primarily as a data repository, and Azure Stream Analytics is focused on real-time analytics rather than the comprehensive pipelines typically involved in an ELT framework. This clarity about the roles of each service highlights why Azure Data Factory is the most fitting choice for the ELT process.