No more typing reviews! Try our Samantha, our new voice AI agent.

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 (31st)
 

Mindshare comparison

As of July 2026, in the Hadoop category, the mindshare of Apache Spark is 14.1%, down from 18.4% compared to the previous year. The mindshare of IBM Spectrum Computing is 5.0%, up from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark14.1%
IBM Spectrum Computing5.0%
Other80.9%
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

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"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 feel the streaming is its best feature."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."
"Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"Spark, as a tool, is easy to work with as you can work with Python, Scala, and Java."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"We are satisfied with the technical support, we have no issues."
"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."
"Easy to operate and use."
"I tell everyone that they should go with IBM Spectrum Computing."
"The best features of IBM Spectrum Computing are common across many of their storage products."
 

Cons

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"There is complexity when it comes to understanding the whole ecosystem, especially for beginners."
"I ran into Spark application performance issues."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"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 management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"I have to say it is bad. I can only ask for help in the Google group."
"The deduplication software isn't quite up to speed with the market."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"We have not been able to use deduplication."
"We are not fully satisfied with this product at the moment because we are having issues with reliability."
"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."
"Software sometimes is a little slower. It takes two or three days sometimes."
"We'd like to see some AI model training for machine learning."
 

Pricing and Cost Advice

"The solution is affordable and there are no additional licensing costs."
"Apache Spark is an open-source tool."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"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 open-source solution, and there is no cost involved in deploying the solution on-premises."
"It is an open-source solution, it is free of charge."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"They provide an open-source license for the on-premise version."
"This solution is expensive."
"Spectrum Computing is one of the most expensive products on the market."
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
905,601 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
20%
Construction Company
8%
Manufacturing Company
8%
Comms Service Provider
7%
Financial Services Firm
16%
Manufacturing Company
14%
Construction Company
11%
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: June 2026.
905,601 professionals have used our research since 2012.