Which of the following enables real-time analytics with Spark using structured data?

Structured Streaming is the correct choice because it is an extension of the DataFrame API in Apache Spark that allows for real-time data processing. This enables users to work with structured data in a streaming fashion, meaning it can process data as it arrives in real-time. It treats streaming data as a continuously evolving table, providing capabilities to run incremental queries that can be managed and updated automatically as new data flows in. This capability is key for applications that require timely insights from streaming data, such as monitoring systems, real-time dashboards, and more.

The other options have their specific purposes but don't directly enable real-time analytics in the same way that Structured Streaming does. For instance, while Streaming APIs refer broadly to the programming interfaces to handle stream processing, it does not solely represent the structured data approach that Structured Streaming provides. Batch processing primarily deals with processing a large dataset in one go rather than in real-time. DataFrames are essential for working with structured data in a tabular form but do not inherently provide the streaming analytics capabilities necessary for real-time applications.