We performed a comparison between Apache Spark and Informatica Big Data Parser based on real PeerSpot user reviews.
Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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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
Informatica Big Data Parser enables access to the most difficult data and file formats in Hadoop, reducing the time and cost of developing data handlers by 70 percent. It enables IT organizations to efficiently manage industry standards, binary documents, and hierarchical data.
Big Data Parser provides a unique development environment for lean data integration. With this software, your IT organization can view data samples within Big Data Parser Studio and understand their structure and layout through a set of integrated tools
Apache Spark is ranked 1st in Hadoop with 13 reviews while Informatica Big Data Parser is ranked 8th in Hadoop. Apache Spark is rated 8.2, while Informatica Big Data Parser is rated 0.0. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas Informatica Big Data Parser is most compared with HPE Ezmeral Data Fabric and Spark SQL.
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