We performed a comparison between Apache Spark and IBM Streams based on real PeerSpot user reviews.Find out what your peers are saying about Apache, Cloudera, Amazon and others in Hadoop.
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Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Apache Spark is ranked 1st in Hadoop with 12 reviews while IBM Streams is ranked 16th in Streaming Analytics. Apache Spark is rated 8.0, while IBM Streams is rated 0.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas IBM Streams is most compared with Confluent, Apache NiFi, Google Cloud Dataflow, Amazon Kinesis and Dell Streaming Data Platform.
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