Compare IBM Spectrum Computing vs. Spark SQL

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Find out what your peers are saying about IBM Spectrum Computing vs. Spark SQL and other solutions. Updated: July 2020.
431,670 professionals have used our research since 2012.
Quotes From Members

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

Pros
We are satisfied with the technical support, we have no issues.The scalability of the solution is good.This solution is working for both VTL and tape.

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

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Cons
SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing.The initial setup is not so complex. However, the day-to-day administration is complex and there are far too many manual intervention requirements.This solution is no longer managing tapes correctly.

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

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Pricing and Cost Advice
This solution is expensive.

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Ranking
8th
out of 26 in Hadoop
Views
331
Comparisons
179
Reviews
3
Average Words per Review
289
Avg. Rating
7.0
6th
out of 26 in Hadoop
Views
603
Comparisons
440
Reviews
4
Average Words per Review
338
Avg. Rating
7.3
Popular Comparisons
Compared 22% of the time.
Compared 19% of the time.
Compared 13% of the time.
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.
Offer
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Sample Customers
London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic
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Find out what your peers are saying about IBM Spectrum Computing vs. Spark SQL and other solutions. Updated: July 2020.
431,670 professionals have used our research since 2012.
IBM Spectrum Computing is ranked 8th in Hadoop with 3 reviews while Spark SQL is ranked 6th in Hadoop with 4 reviews. IBM Spectrum Computing is rated 7.0, while Spark SQL is rated 7.2. 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, whereas Spark SQL is most compared with Informatica Big Data Parser, Apache Spark, Amazon EMR, AtScale Adaptive Analytics (A3) and Netezza Analytics. See our IBM Spectrum Computing vs. Spark SQL report.

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