What is structured streaming in Spark?

Structured streaming in Spark is a powerful framework designed for processing continuous streams of data in real time. It enables users to process and analyze data as it arrives, allowing for immediate insights and actions based on current information. The key features of structured streaming include support for scalable, fault-tolerant stream processing, and the ability to work with both batch and real-time data in a unified way.

The underlying engine leverages Spark’s capability to handle large datasets efficiently and provides a simple and expressive API that facilitates the creation of streaming queries. Notable aspects include automatic handling of state management, watermarking for dealing with late data, and integration with various sources and sinks like Kafka, filesystems, and more.

This positions structured streaming as a robust solution for applications needing to respond to events or data changes in real-time, distinguishing it from batch processing models, which are more suited for analyzing static datasets. The choice offering a fault-tolerant stream processing engine accurately captures the essence of structured streaming and how it operates within the broader Spark ecosystem.