What type of data is often processed with Structured Streaming in Databricks?

Real-time streaming data is the type of data that is most effectively processed with Structured Streaming in Databricks. Structured Streaming is designed to handle continuously flowing data and allows you to process, analyze, and visualize data as it arrives in real time. This makes it highly suitable for scenarios such as monitoring live events, processing sensor data, or analyzing social media feeds, where timely insights are critical.

Structured Streaming provides a scalable and fault-tolerant stream processing engine that integrates easily with Spark DataFrames and queries, enabling users to express their computation in a simple and declarative manner. This framework allows for complex event processing and transformations on the data streams without needing to manage the underlying infrastructure manually.

In contrast, processing historical data, static data, or large batch datasets typically falls under batch processing paradigms rather than streaming. These types of data are usually processed in large, discrete batches where the focus is not on the real-time data ingestion but rather on analyzing and querying data that is already stored.