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."We are satisfied with the technical support, we have no issues."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"Easy to operate and use."
"This solution is working for both VTL and tape."
"The most valuable feature is the backup capability."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"This solution is useful to leverage within a distributed ecosystem."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"It is a stable solution."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"I find the Thrift connection valuable."
"Data validation and ease of use are the most valuable features."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"This solution is no longer managing tapes correctly."
"We'd like to see some AI model training for machine learning."
"Lack of sufficient documentation, particularly in Spanish."
"We have not been able to use deduplication."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"Anything to improve the GUI would be helpful."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
"This solution could be improved by adding monitoring and integration for the EMR."
"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."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"SparkUI could have more advanced versions of the performance and the queries and all."
"I've experienced some incompatibilities when using the Delta Lake format."
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, HPE Ezmeral Data Fabric, SAP HANA and Netezza Analytics. See our IBM Spectrum Computing vs. Spark SQL report.
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