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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 the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"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 fault tolerant feature is provided."
"The data processing framework is good."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"It is a very fast solution."
"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."
"Easy to operate and use."
"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."
"We are satisfied with the technical support, we have no issues."
"The most valuable feature is the backup capability."
"The most valuable feature is the backup capability."
"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."
"This solution worked as expected and it is reliable."
 

Cons

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Sometimes it is a nightmare on Linux trying to figure out what happened on the configuration and back-end."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"I do not know exactly what was the reason to move away from Apache Spark or the underlying database system, but it was simply a decision driven by the customer."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"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."
"The basic improvement would be to have integration with these solutions."
"We are not fully satisfied with this product at the moment because we are having issues with reliability."
"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."
"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."
"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."
"Software sometimes is a little slower. It takes two or three days sometimes."
"This solution is no longer managing tapes correctly."
"IBM's sales and support structure can be challenging."
 

Pricing and Cost Advice

"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."
"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."
"Apache Spark is an expensive solution."
"We are using the free version of the solution."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"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
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.
894,998 professionals have used our research since 2012.