Structured Streaming in Spark Scala provides the capability to ingest real-time data from sources like sockets or Kafka. Users can process this data iteratively through transformations such as filtering, aggregation, and parsing. The learning exercise includes starting a socket server to simulate streaming click events or log messages, creating a DataFrame from this data and performing operations to count events per page, with updates available in real-time every few seconds. This approach allows for immediate insights, showcasing a shift from traditional batch processing to an interactive streaming model.
In Spark Structured Streaming, data is processed as an unbounded table, continuously arriving and allowing for real-time analytics on streaming data.
To start a socket server, running the command allows users to simulate real-time click events and log messages for testing and development.
The processing logic involves parsing the event data and applying transformations such as filtering to prepare the data for meaningful insights.
Output updates can be configured to display transformations in real-time every few seconds, emphasizing the live data capabilities of Structured Streaming.
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