Spark Streaming, as a stream processing engine for micro-batch processing, converts the data of each time slice into a partition in an RDD for calculation.

Question

Options

• ATrue
• BFalse

• A
  79% (22)
• B
  21% (6)

Explanation

A (True) is correct because Spark Streaming operates on a micro-batch model where it divides the incoming data stream into small time-based chunks (called micro-batches or time slices). Each time slice is converted into an RDD (Resilient Distributed Dataset), where the data within that slice becomes one or more partitions - allowing Spark's standard batch processing engine to handle it using the familiar RDD API.

B is wrong because it implies this conversion doesn't happen, which contradicts how Spark Streaming fundamentally works: it abstracts a continuous stream into a sequence of discrete RDDs (collectively called a DStream), not a single continuous data structure.

Memory tip: Think of Spark Streaming as a "time slicer" - it takes the continuous river of data and freezes it into ice cubes (time slices → RDD partitions) for Spark to process one cube at a time. If you remember "micro-batch = RDD per slice," the True/False becomes automatic.

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