We performed a comparison between IBM Spectrum Computing and Spark SQL 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."The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"The most valuable feature is the backup capability."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"This solution is working for both VTL and tape."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"The stability was fine. It behaved as expected."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"Overall the solution is excellent."
"I find the Thrift connection valuable."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"We'd like to see some AI model training for machine learning."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"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."
"We have not been able to use deduplication."
"This solution is no longer managing tapes correctly."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"SparkUI could have more advanced versions of the performance and the queries and all."
"It would be useful if Spark SQL integrated with some data visualization tools."
"I've experienced some incompatibilities when using the Delta Lake format."
"Anything to improve the GUI would be helpful."
"There should be better integration with other solutions."
"This solution could be improved by adding monitoring and integration for the EMR."
IBM Spectrum Computing is ranked 7th in Hadoop with 6 reviews while Spark SQL is ranked 4th in Hadoop with 14 reviews. IBM Spectrum Computing is rated 7.8, while Spark SQL is rated 7.8. The top reviewer of IBM Spectrum Computing writes "Provides stable backup for our databases and has good technical support ". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". IBM Spectrum Computing is most compared with Apache Spark, HPE Ezmeral Data Fabric and IBM Turbonomic, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our IBM Spectrum Computing vs. Spark SQL report.
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