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Apache Spark vs IBM Spectrum Computing comparison

 

Comparison Buyer's Guide

Executive Summary

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

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
69
Ranking in other categories
Compute Service (6th), Java Frameworks (2nd)
IBM Spectrum Computing
Ranking in Hadoop
7th
Average Rating
7.8
Reviews Sentiment
5.9
Number of Reviews
9
Ranking in other categories
Cloud Management (29th)
 

Mindshare comparison

As of May 2026, in the Hadoop category, the mindshare of Apache Spark is 13.6%, down from 17.6% compared to the previous year. The mindshare of IBM Spectrum Computing is 5.2%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.6%
IBM Spectrum Computing5.2%
Other81.2%
Hadoop
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
OmarIsmail1 - PeerSpot reviewer
Infrastructure Technical Specialist II at Clicks Group
Senior Technical Specialist appreciates intelligent workload management, strong support, and scalability
The best features of IBM Spectrum Computing are common across many of their storage products. The software is solid, meaning that the code is stable. They take business seriously, which is what IBM stands for - International Business Machines. They always maintain a business-oriented approach in their software development. It's not simply clicking through interfaces; in IBM software, they consider their actions, process flows, and workflows around business processes. It requires understanding IBM and their methodology, as the software operates accordingly. I have utilized IBM Spectrum Computing's intelligent workload management feature. We use Insights, which is connected to the cloud. This provides AI capabilities for analyzing the configuration, offering smart recommendations on new code, warning about bugs in current code, and suggesting configuration improvements through its advisor tool. The predictive analytics feature in IBM Spectrum Computing enables optimal software performance through Insights. However, being a storage administrator requires foundational knowledge and understanding beyond these tools. For troubleshooting, it's efficient in spotting bottlenecks, but understanding the terms and metrics is essential as it provides answers that need interpretation.

Quotes from Members

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

Pros

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The fault tolerant feature is provided."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"It is useful for handling large amounts of data, and it is very useful for scientific purposes."
"Overall, it offers everything that I can imagine right now."
"There's a lot of functionality."
"The fast performance is the most valuable aspect of the solution."
"It is a better MR, supports streaming and micro-batch, and supports Spark ML and Spark SQL."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"
"Spectrum Computing is one of the best tools in the data management and services area, as it can process huge amounts of data with standardized data management and provides a great data governance capability."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"The most valuable feature is the backup capability."
"This solution worked as expected and it is reliable."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"The best features of IBM Spectrum Computing are common across many of their storage products."
 

Cons

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"It should support more programming languages."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"It needs to be simpler to use the machine learning algorithms supported by Octave (example polynomial regressions, polynomial interpolation)."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"Better integration of BI tools would be a much appreciated improvement."
"Spark Streaming is difficult to stabilize as you're always dependant to your stream flow."
"They currently use a JDK version which is a little bit old. Not all features are on it."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"The deduplication software isn't quite up to speed with the market."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"We'd like to see some AI model training for machine learning."
"IBM's sales and support structure can be challenging."
"Software sometimes is a little slower. It takes two or three days sometimes."
"This solution is no longer managing tapes correctly."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
 

Pricing and Cost Advice

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Spark is an open-source solution, so there are no licensing costs."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"The product is expensive, considering the setup."
"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is an expensive solution."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"This solution is expensive."
"Spectrum Computing is one of the most expensive products on the market."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Comms Service Provider
7%
Manufacturing Company
7%
Computer Software Company
6%
Manufacturing Company
14%
Financial Services Firm
14%
Construction Company
10%
Outsourcing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise1
Large Enterprise6
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
What is your experience regarding pricing and costs for IBM Spectrum Computing?
IBM Spectrum Computing consistently offers competitive pricing. When solutioning new implementations, IBM always presents the best solution and price. In a recent comparison with Pure Storage and N...
What needs improvement with IBM Spectrum Computing?
IBM Spectrum Computing had limitations with remote copy services between head office and disaster recovery sites. In the last year, IBM has improved the code by re-engineering it to policy-based re...
What is your primary use case for IBM Spectrum Computing?
The typical use case for IBM Spectrum Computing is that it's an all-rounder. It can be used in various scenarios, such as the retailer I work for that has batch processing. It's on-demand when perf...
 

Also Known As

No data available
IBM Platform Computing
 

Overview

 

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic
Find out what your peers are saying about Apache Spark vs. IBM Spectrum Computing and other solutions. Updated: April 2026.
893,438 professionals have used our research since 2012.