Spark uses micro-batches to process events while Storm processes events one by one. It means that Spark has a latency of seconds while Storm provides a millisecond of latency. Spark Streaming provides a high-level abstraction called a Discretized Stream or DStream, which represents a continuous sequence of RDDs. (But, the latest version of Spark, 2.4 supports millisecond data latency.) The latest Spark version supports DataFrames.
Almost the same code (API) can be used for Spark Streaming and Spark batch jobs. That helps to reuse most of the code base for both programming models. Also, Spark supports Machine learning and the Graph API. So, again, the same codebase can be used for those use cases as well.
