What is Structured Streaming?

Structured Streaming is a scalable and fault-tolerant stream processing engine designed to efficiently handle real-time data streams. This framework is built on top of Spark SQL, allowing developers to work with streaming data using familiar DataFrame and SQL APIs. It processes data in small, continuous increments, enabling low-latency handling of incoming data, which is crucial for applications where timely insights are necessary.

The engine is adept at managing streaming data sources, maintaining accuracy, and ensuring that data is processed exactly once, which is vital for achieving reliability in streaming applications. By leveraging the underlying capabilities of Apache Spark, Structured Streaming can dynamically scale according to the volume of data being ingested, thus offering resilience and efficiency in processing large streams of data in real-time.

The other choices do not accurately describe Structured Streaming. Batch processing engines focus on static datasets and usually do not handle data in real time, whereas features for managing static data do not pertain to the dynamic and continuous nature of streaming. Lastly, while infrastructure improvements in data warehouses are significant, they do not capture the essence of the stream processing capabilities that Structured Streaming provides.