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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 (5th), 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 (27th)
 

Mindshare comparison

As of April 2026, in the Hadoop category, the mindshare of Apache Spark is 12.9%, down from 18.3% 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 Spark12.9%
IBM Spectrum Computing5.2%
Other81.9%
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

"We have built a product called NetBot where we take any form of data, such as large email data, images, videos, or transactional data, and transform unstructured textual and video data into structured transactional data to create an enterprise-wide smart data grid that is then used by downstream analytics tools."
"Having everything in the same framework has helped us out a lot."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Features include machine learning, real time streaming, and data processing."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"The fault tolerant feature is provided."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The solution has been very stable."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"Easy to operate and use."
"We are satisfied with the technical support, we have no issues."
"This solution worked as expected and it is reliable."
"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."
"I tell everyone that they should go with IBM Spectrum Computing."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"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."
 

Cons

"Apache Spark is very difficult to use. It would require a data engineer."
"It should support more programming languages."
"The solution’s integration with other platforms should be improved."
"Apache Spark should add some resource management improvements to the algorithms."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"It needs to be simpler to use the machine learning algorithms supported by Octave (example polynomial regressions, polynomial interpolation)."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"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."
"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."
"Lack of sufficient documentation, particularly in Spanish."
"We have not been able to use deduplication."
"This solution is no longer managing tapes correctly."
 

Pricing and Cost Advice

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."
"It is an open-source solution, it is free of charge."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"Apache Spark is an expensive solution."
"Spark is an open-source solution, so there are no licensing costs."
"It is an open-source platform. We do not pay for its subscription."
"We are using the free version of the solution."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"Spectrum Computing is one of the most expensive products on the market."
"This solution is expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
24%
Manufacturing Company
7%
Comms Service Provider
6%
Computer Software Company
6%
Financial Services Firm
16%
Manufacturing Company
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 Enterprise32
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise1
Large Enterprise6
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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 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.
886,011 professionals have used our research since 2012.