How does Spark Structured Streaming model new data?

Spark Structured Streaming models new data as new rows appended to an unbounded table. This concept aligns well with the stream processing paradigm, where incoming data is treated as continuous records that are constantly added to a dataset that does not have a fixed size limit.

This approach allows Spark to process streaming data effectively by treating the stream as a table, where each new message or piece of data becomes a new row in this logically represented table. The unbounded nature of the table means that Spark can continuously process data as it arrives, thereby supporting real-time analytics and updates. This model is beneficial because it allows developers to leverage the familiar SQL operations when working with streaming data, enabling simpler and more intuitive data manipulation and querying.

In contrast, modeling new data as messages from a messaging bus would imply a focus on the messaging mechanics rather than on the continuous dataset perspective that Structured Streaming offers. Considering tasks executed in parallel suggests a focus on execution rather than data modeling, which does not accurately describe how new data is structured within Spark. Lastly, treating new data as direct inserts in a defined schema misses the essence of the unbounded table concept, as it emphasizes a more static approach to data insertion rather than the fluidity and continuous nature of streaming data processing.