NitinKumarEngineering Manager at Sigmoid
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
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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 12 reviews while Informatica Big Data Parser is ranked 10th in Hadoop. Apache Spark is rated 8.6, while Informatica Big Data Parser is rated 0.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, SAP HANA and AWS Fargate, whereas Informatica Big Data Parser is most compared with Cloudera Distribution for Hadoop, Cloudera DataFlow, Cask, Hortonworks Data Platform and Spark SQL.
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