Try our new research platform with insights from 80,000+ expert users

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
7th
Average Rating
7.8
Reviews Sentiment
5.9
Number of Reviews
9
Ranking in other categories
Cloud Management (28th)
 

Mindshare comparison

As of March 2026, in the Hadoop category, the mindshare of Apache Spark is 13.3%, down from 18.6% compared to the previous year. The mindshare of IBM Spectrum Computing is 4.8%, up from 2.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.3%
IBM Spectrum Computing4.8%
Other81.9%
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

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"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 tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"I found the solution stable. We haven't had any problems with it."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The data processing framework is good."
"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."
"I have utilized IBM Spectrum Computing's intelligent workload management feature through Insights, which is connected to the cloud."
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"Easy to operate and use."
"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."
"The most valuable feature is the backup capability."
 

Cons

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The product could improve the user interface and make it easier for new users."
"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."
"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."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"Apache Spark lacks geospatial data."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"Lack of sufficient documentation, particularly in Spanish."
"We have not been able to use deduplication."
"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."
"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."
"IBM's sales and support structure can be challenging."
"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."
"We'd like to see some AI model training for machine learning."
 

Pricing and Cost Advice

"The product is expensive, considering the setup."
"Apache Spark is an open-source tool."
"They provide an open-source license for the on-premise version."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is an expensive solution."
"The solution is affordable and there are no additional 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."
"Spectrum Computing is one of the most expensive products on the market."
"This solution is expensive."
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
883,824 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
24%
Computer Software Company
8%
Manufacturing Company
7%
Comms Service Provider
5%
Manufacturing Company
16%
Financial Services Firm
16%
Outsourcing Company
11%
Transportation Company
7%
 

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: March 2026.
883,824 professionals have used our research since 2012.