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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
68
Ranking in other categories
Compute Service (5th), Java Frameworks (2nd)
IBM Spectrum Computing
Ranking in Hadoop
6th
Average Rating
7.8
Reviews Sentiment
5.9
Number of Reviews
9
Ranking in other categories
Cloud Management (29th)
 

Mindshare comparison

As of February 2026, in the Hadoop category, the mindshare of Apache Spark is 13.4%, down from 18.4% compared to the previous year. The mindshare of IBM Spectrum Computing is 4.5%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.4%
IBM Spectrum Computing4.5%
Other82.1%
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

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"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."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"Apache Spark's ability to handle both batch and streaming data is the most valuable feature for me as it offers solid real-time processing capability, making it more efficient in managing data analytics."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"The best features of IBM Spectrum Computing are common across many of their storage products."
"This solution is working for both VTL and tape."
"The most valuable feature is the backup capability."
"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"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."
 

Cons

"Apache Spark should add some resource management improvements to the algorithms."
"The main concern is the overhead of Java when distributed processing is not necessary."
"Apache Spark provides very good performance The tuning phase is still tricky."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"It should support more programming languages."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"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."
"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."
"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."
"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."
"IBM's sales and support structure can be challenging."
"We'd like to see some AI model training for machine learning."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
 

Pricing and Cost Advice

"The product is expensive, considering the setup."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"Apache Spark is an expensive solution."
"Spark is an open-source solution, so there are no licensing costs."
"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
"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
24%
Computer Software Company
8%
Manufacturing Company
7%
University
5%
Financial Services Firm
18%
Manufacturing Company
16%
Outsourcing Company
12%
Performing Arts
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise15
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?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
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: February 2026.
882,744 professionals have used our research since 2012.