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Apache Spark vs IBM Spectrum Computing comparison

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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 (43rd)
 

Mindshare comparison

As of October 2026, in the Hadoop category, the mindshare of Apache Spark is 14.3%, down from 19.0% compared to the previous year. The mindshare of IBM Spectrum Computing is 4.9%, up from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark14.3%
IBM Spectrum Computing4.9%
Other80.8%
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

"With Spark SQL we've now the capabilities to analyse very large quantities of data located in S3 on Amazon at very low cost comparing other solution we checked."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"It is an excellent tool to process massive amount of data."
"The product is useful for analytics."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"I found the solution stable. We haven't had any problems with it."
"The processing time is very much improved over the data warehouse solution that we were using."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"We are satisfied with the technical support, we have no issues."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"This solution worked as expected and it is reliable."
"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"
"Easy to operate and use."
"I tell everyone that they should go with IBM Spectrum Computing."
"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."
"This solution is working for both VTL and tape."
 

Cons

"Although you are able to perform complex transformations using Spark libraries, the support for SQL to perform transformations is still limited."
"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The main problem is, now in the market, there are not many people certified in Apache Spark."
"Apache Spark's GUI and scalability could be improved."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"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."
"The deduplication software isn't quite up to speed with the market. While IBM has excellent compression technology, specifically on their FlashCore modules, they lag behind competitors such as NetApp in deduplication capabilities."
"This solution is no longer managing tapes correctly."
"We have not been able to use deduplication."
"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"We are not fully satisfied with this product at the moment because we are having issues with reliability."
 

Pricing and Cost Advice

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Spark is an open-source solution, so there are no licensing costs."
"We are using the free version of the solution."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is an open-source tool."
"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."
"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
17%
Outsourcing Company
10%
Comms Service Provider
9%
Construction Company
9%
Comms Service Provider
13%
Financial Services Firm
12%
Construction Company
11%
Outsourcing Company
11%
 

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: September 2026.
916,956 professionals have used our research since 2012.