Compare IBM Spectrum Computing vs. Spark SQL

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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"This solution is working for both VTL and tape.""We are satisfied with the technical support, we have no issues.""The most valuable feature is the backup capability."

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"The stability was fine. It behaved as expected.""Overall the solution is excellent.""The speed of getting data.""The performance is one of the most important features. It has an API to process the data in a functional manner.""It is a stable solution."

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Cons
"This solution is no longer managing tapes correctly.""SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing.""We have not been able to use deduplication."

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"In the next release, maybe the visualization of some command-line features could be added.""The solution needs to include graphing capabilities. Including financial charts would help improve everything overall.""Anything to improve the GUI would be helpful.""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.""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."

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Questions from the Community
Top Answer: The most valuable feature is the backup capability.
Top Answer: We are not fully satisfied with this product at the moment because we are having issues with reliability. We have not been able to use deduplication. I cannot use any protocol other than VTL. We have… more »
Top Answer: We use this product to back up our data.
Top Answer: It is a stable solution.
Top Answer: 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… more »
Top Answer: We use it to gather all the transaction data. We have Hadoop and Spark in our system, and we use some easy process flows for transport.
Ranking
9th
out of 22 in Hadoop
Views
428
Comparisons
256
Reviews
3
Average Words per Review
283
Rating
7.3
6th
out of 22 in Hadoop
Views
662
Comparisons
311
Reviews
5
Average Words per Review
309
Rating
6.6
Popular Comparisons
Also Known As
IBM Platform Computing
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Overview

IBM Spectrum Computing uses intelligent workload and policy-driven resource management to optimize resources across the data center, on premises and in the cloud. Now up to 150X faster and scalable to over 160,000 cores, IBM provides you with the latest advances in software-defined infrastructure to help you unleash the power of your distributed mission-critical high performance computing (HPC), analytics and big data applications as well as a new generation open source frameworks such as Hadoop and Spark.

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
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Sample Customers
London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Top Industries
No Data Available
VISITORS READING REVIEWS
Comms Service Provider29%
Computer Software Company23%
Financial Services Firm8%
Media Company7%
Find out what your peers are saying about IBM Spectrum Computing vs. Spark SQL and other solutions. Updated: May 2021.
502,104 professionals have used our research since 2012.

IBM Spectrum Computing is ranked 9th in Hadoop with 3 reviews while Spark SQL is ranked 6th in Hadoop with 5 reviews. IBM Spectrum Computing is rated 7.4, while Spark SQL is rated 6.6. 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 "GUI could be improved. Useful for speedily processing big data". IBM Spectrum Computing is most compared with Cloudera Distribution for Hadoop, Red Hat CloudForms and HPE Ezmeral Data Fabric, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, Amazon EMR and Informatica Big Data Parser. See our IBM Spectrum Computing vs. Spark SQL report.

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