IBM Spectrum Computing vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #7 with an average rating of 9.0, while Apache is ranked #5 with an average rating of 8.5. IBM holds a 2.6% mindshare in H, compared to Apache’s 9.8% mindshare. Additionally, 57% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

IBM Spectrum Computing

Read 8 IBM Spectrum Computing reviews

216 Views
123 Comparison Views

57% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

824 Views
603 Comparison Views

85% willing to recommend

IBM Spectrum Computing

Spark SQL

Comparison Buyer's Guide

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Executive Summary

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.

To learn more, read our detailed IBM Spectrum Computing vs. Spark SQL Report (Updated: March 2025).

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

March 2025

Download the complete report

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Review summaries and opinions

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

Categories and Ranking

IBM Spectrum Computing

Ranking in Hadoop

7th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Management (23rd)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of IBM Spectrum Computing is 2.6%, up from 2.2% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 11.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Avra Jyoti Ghosh

Data QualityTechnical Architect at Mindtree

One of the best tools in the data management and services area

I mainly used Spectrum Computing for data management, governance, quality, and ETL activity Spectrum Computing's best features are its speed, robustness, and data processing and analysis. Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the…

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Sahil Taneja

Principal Consultant/Manager at Tenzing

Easy to use and do not require a learning curve

Spark SQL can improve the documentation they have provided. It can be a bit unclear at times. They could improve the documentation a bit more so that we can understand it more easily. Moreover, they could improve SparkUI to have more advanced versions of the performance and the queries and all.

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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

"Easy to operate and use."

"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."

"The comparison was challenging, but the IBM Spectrum Scale offered a balanced solution. Our engineers rated itsanalytics capabilities equally high as Pure Storage. For workload management, Spectrum Computing provided effective solutions that met our needs. Workload management is part of a complete solution that uses different tools. There were the cloud and HPC parts; within HPC, there were parts like liquid cooling, simple computing, storage, and orchestration. The orchestration team handled the workload management."

"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"

"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."

"The stability was fine. It behaved as expected."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"Data validation and ease of use are the most valuable features."

"The solution is easy to understand if you have basic knowledge of SQL commands."

"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."

"This solution is useful to leverage within a distributed ecosystem."

More Spark SQL pros

Cons

"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."

"We have not been able to use deduplication."

"IBM's sales and support structure can be challenging."

"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."

"We'd like to see some AI model training for machine learning."

"This solution is no longer managing tapes correctly."

"Lack of sufficient documentation, particularly in Spanish."

"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."

"There should be better integration with other solutions."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

"SparkUI could have more advanced versions of the performance and the queries and all."

"This solution could be improved by adding monitoring and integration for the EMR."

"In the next release, maybe the visualization of some command-line features could be added."

"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."

More Spark SQL cons

Pricing and Cost Advice

"This solution is expensive."

"Spectrum Computing is one of the most expensive products on the market."

"We use the open-source version, so we do not have direct support from Apache."

"There is no license or subscription for this solution."

"The solution is open-sourced and free."

"The solution is bundled with Palantir Foundry at no extra charge."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

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Top Industries

By visitors reading reviews

Financial Services Firm

40%

Computer Software Company

Retailer

Real Estate/Law Firm

Financial Services Firm

22%

Computer Software Company

15%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM Spectrum Computing?

Easy to operate and use.

See all answers

What is your experience regarding pricing and costs for IBM Spectrum Computing?

It is expensive.

See all answers

What needs improvement with IBM Spectrum Computing?

IBM's sales and support structure can be challenging. To work on an IBM deal, you often need to involve multiple specialists, each knowledgeable about only part of the product, rather than having a...

See all answers

What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

See all answers

What is your experience regarding pricing and costs for Spark SQL?

We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

Comparisons

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Compared 33% of the time

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Compared 26% of the time

Red Hat CloudForms vs IBM Spectrum Computing

Compared 22% of the time

Cloudera Data Platform vs IBM Spectrum Computing

Compared 19% of the time

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Apache Spark vs Spark SQL

Compared 50% of the time

SAP HANA vs Spark SQL

Compared 12% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 10% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 10% of the time

Netezza Analytics vs Spark SQL

Compared 9% of the time

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Product Reports

Buyer's Guide

Hadoop

March 2025

Download IBM Spectrum Computing product report

Buyer's Guide

Spark SQL

March 2025

Download Spark SQL product report

Also Known As

IBM Platform Computing

No data available

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.

IBM

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.

Apache

Sample Customers

London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

March 2025

Free Report: IBM Spectrum Computing vs. Spark SQL

Find out what your peers are saying about IBM Spectrum Computing vs. Spark SQL and other solutions. Updated: March 2025.

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