We performed a comparison between Apache Spark and IBM Spectrum Computing 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."It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The solution is very stable."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The fault tolerant feature is provided."
"It provides a scalable machine learning library."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"The most valuable feature is the backup capability."
"This solution is working for both VTL and tape."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"Dynamic DataFrame options are not yet available."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"The setup I worked on was really complex."
"The logging for the observability platform could be better."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"We'd like to see some AI model training for machine learning."
"We have not been able to use deduplication."
"Lack of sufficient documentation, particularly in Spanish."
"This solution is no longer managing tapes correctly."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
Apache Spark is ranked 1st in Hadoop with 60 reviews while IBM Spectrum Computing is ranked 7th in Hadoop with 6 reviews. Apache Spark is rated 8.4, while IBM Spectrum Computing is rated 7.8. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of IBM Spectrum Computing writes "Provides stable backup for our databases and has good technical support ". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas IBM Spectrum Computing is most compared with HPE Ezmeral Data Fabric and IBM Turbonomic. See our Apache Spark vs. IBM Spectrum Computing report.
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