Which Azure tool is primarily used for batch processing of large datasets?

Azure Batch is specifically designed for running large-scale parallel and high-performance computing applications efficiently. It allows users to manage and distribute workloads across multiple nodes, making it ideal for batch processing scenarios. This tool handles the allocation of resources, scheduling of tasks, and scaling of applications, which is essential for processing large datasets that do not require immediate real-time processing.

The capability to execute jobs in parallel across a vast number of compute resources is one of Azure Batch's significant strengths. It automates tasks such as distributing data and managing dependencies, which simplifies the workflow for data scientists and enables the processing of substantial data volumes efficiently.

In contrast, Azure Stream Analytics is mainly focused on real-time analytics on streaming data. It is excellent for scenarios involving live data ingestion and processing but not suitable for batch processing of large datasets. Azure Logic Apps is primarily used for automating workflows and integrating applications and services, which does not involve the batch processing of data. Azure Functions, like Logic Apps, leans toward serverless computing and event-driven processing, handling discrete tasks rather than processing extensive datasets in batches.

Thus, Azure Batch is unequivocally the right choice for batch processing tasks involving large datasets due to its design and capabilities tailored for this purpose.