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."DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"Spark can handle small to huge data and is suitable for any size of company."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The deployment of the product is easy."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"This solution is working for both VTL and tape."
"The most valuable feature is the backup capability."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"The solution’s integration with other platforms should be improved."
"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."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"Lack of sufficient documentation, particularly in Spanish."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"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."
"This solution is no longer managing tapes correctly."
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. See our Apache Spark vs. IBM Spectrum Computing report.
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