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 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."
"Easy to operate and use."
"We are satisfied with the technical support, we have no issues."
"This solution is useful to leverage within a distributed ecosystem."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"Overall the solution is excellent."
"The speed of getting data."
"Data validation and ease of use are the most valuable features."
"This solution is no longer managing tapes correctly."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"Lack of sufficient documentation, particularly in Spanish."
"We'd like to see some AI model training for machine learning."
"We have not been able to use deduplication."
"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."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"There are many inconsistencies in syntax for the different querying tasks."
"Anything to improve the GUI would be helpful."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"It would be useful if Spark SQL integrated with some data visualization tools."
"In the next release, maybe the visualization of some command-line features could be added."
IBM Spectrum Computing is ranked 7th in Hadoop with 2 reviews while Spark SQL is ranked 4th in Hadoop with 7 reviews. IBM Spectrum Computing is rated 7.8, while Spark SQL is rated 7.8. The top reviewer of IBM Spectrum Computing writes "One of the best tools in the data management and services area". On the other hand, the top reviewer of Spark SQL writes "Processing solution used for data engineering and transformation with the ability to process large datasets". 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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