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Apache Spark vs IBM Spectrum Computing comparison

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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 (43rd)
 

Mindshare comparison

As of September 2026, in the Hadoop category, the mindshare of Apache Spark is 14.1%, down from 19.3% compared to the previous year. The mindshare of IBM Spectrum Computing is 4.9%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark14.1%
IBM Spectrum Computing4.9%
Other81.0%
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 most valuable feature of Apache Spark is its ease of use."
"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."
"Apache Spark is a framework, which allows one organization to perform business and data analytics, at a very low cost, as compared to Ab-Initio or Informatica."
"Machine learning, real time streaming, and data processing are fantastic, as well as the resilient or fault tolerant feature."
"After using Spark, we were able to accomplish this task within hours."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"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."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"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."
"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."
"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,"
 

Cons

"Better data lineage support."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Dynamic DataFrame options are not yet available."
"Spark Streaming is difficult to stabilize as you're always dependant to your stream flow."
"The Spark solution could improve in scheduling tasks and managing dependencies."
"Apache Spark is very difficult to use. It would require a data engineer."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Lack of sufficient documentation, particularly in Spanish."
"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."
"We are not fully satisfied with this product at the moment because we are having issues with reliability."
"We have not been able to use deduplication."
"The deduplication software isn't quite up to speed with the market."
"Software sometimes is a little slower. It takes two or three days sometimes."
"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."
 

Pricing and Cost Advice

"The solution is affordable and there are no additional licensing costs."
"The product is expensive, considering the setup."
"Spark is an open-source solution, so there are no licensing costs."
"It is an open-source solution, it is free of charge."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"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."
"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."
"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
17%
Outsourcing Company
9%
Comms Service Provider
9%
Construction Company
8%
Financial Services Firm
15%
Construction Company
12%
Comms Service Provider
12%
Manufacturing Company
12%
 

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: September 2026.
913,924 professionals have used our research since 2012.