Processing data using Spark Structured Streaming
Structured Streaming is a feature in Apache Spark where the incoming stream is treated as an unbounded table. The incoming streaming data is continuously appended to the table. This feature makes it easy to write streaming queries, as we can now write streaming transformations in the same way we handle table-based transformations. Hence, the same Spark batch processing syntax can be applied here, too. Spark treats the Structured Streaming queries as incremental queries on an unbounded table and runs them at frequent intervals to continuously process the data.
Spark supports three writing modes for the output of Structured Streaming:
