<sod>Spark Streaming leverages Spark Core's fast scheduling capability to perform [[Event stream processing|streaming analytics]]. It ingests data in mini-batches and performs RDD transformations on those mini-batches of data. This design enables the same set of application code written for batch analytics to be used in streaming analytics, on a single engine.{{Citation needed|date=September 2015}}<eod>
<sod>Spark Streaming leverages Spark Core's fast scheduling capability to perform [[Event stream processing|streaming analytics]]. It ingests data in mini-batches and performs RDD transformations on those mini-batches of data. This design enables the same set of application code written for batch analytics to be used in streaming analytics, on a single engine. Spark runs in-memory to process data with speed and sophistication than the other complement approaches like Hadoop Map-reduce. It can handle several terabytes of data at one time and perform efficient processing<eod>