What role does Spark SQL play in structured streaming?

Spark SQL plays a crucial role in structured streaming by enabling the processing of real-time data streams through a structured API. This capability allows developers to work with data in a more organized manner using DataFrame and Dataset abstractions, which offer optimization and ease of use when dealing with both batch and streaming data. Through Spark SQL, users can run SQL queries on streaming data and leverage SQL syntax for complex analytical tasks, making it versatile for real-time analytics and insights.

The structured API provided by Spark SQL supports a wide range of operations such as filtering, aggregating, and joining data streams while maintaining the ability to handle out-of-order data and late arrivals. This makes it a powerful tool for real-time data processing, enabling users to define how they want data processed and queried effectively.

In contrast, while storage and visualization are important aspects of data engineering, they do not capture the primary functionality of Spark SQL in the context of structured streaming, which is centered around the processing and querying of data in real-time. Additionally, while data transformation is a part of data processing, it does not encompass the broader capability that Spark SQL offers in terms of structured stream handling.