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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
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

"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."
"The most valuable feature of Apache Spark is its ease of use."
"There's a lot of functionality."
"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."
"The scalability has been the most valuable aspect of the solution."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The most valuable feature is the backup capability."
"This solution is working for both VTL and tape."
"The best features of IBM Spectrum Computing are common across many of their storage products."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"We are satisfied with the technical support, we have no issues."
"Easy to operate and use."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
 

Cons

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"The solution needs to optimize shuffling between workers."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"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."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The setup I worked on was really complex."
"We have not been able to use deduplication."
"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."
"IBM's sales and support structure can be challenging."
"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."
"The deduplication software isn't quite up to speed with the market."
"We'd like to see some AI model training for machine learning."
 

Pricing and Cost Advice

"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."
"Spark is an open-source solution, so there are no licensing costs."
"Apache Spark is an expensive solution."
"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."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"It is an open-source platform. We do not pay for its subscription."
"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."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"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 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?
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.
882,961 professionals have used our research since 2012.