Storm VS Shark

Following texts copied from http://stackoverflow.com/questions/24119897/apache-spark-vs-apache-storm

Apache Spark is an in-memory distributed data analysis platform-- primarily targeted at speeding up batch analysis jobs, iterative machine learning jobs, interactive query and graph processing.

One of Spark's primary distinctions is its use of RDDs or Resilient Distributed Datasets. RDDs are great for pipelining parallel operators for computation and are, by definition, immutable, which allows Spark a unique form of fault tolerance based on lineage information. If you are interested in, for example, executing a Hadoop MapReduce job much faster, Spark is a great option (although memory requirements must be considered).

Apache Storm is focused on stream processing or what some call complex event processing. Storm implements a fault tolerant method for performing a computation or pipelining multiple computations on an event as it flows into a system. One might use Storm to transform unstructured data as it flows into a system into a desired format.

Storm and Spark are focused on fairly different use cases. The more "apples-to-apples" comparison would be between Storm and Spark Streaming. Since Spark's RDDs are inherently immutable, Spark Streaming implements a method for "batching" incoming updates in user-defined time intervals that get transformed into their own RDDs. Spark's parallel operators can then perform computations on these RDDs. This is different from Storm which deals with each event individually.

One key difference between these two technologies is that Spark performs Data-Parallel computations while Storm performs Task-Parallel computations. Either design makes tradeoffs that are worth knowing. I would suggest checking out these links.