Apache Spark vs IBM Spectrum Computing comparison

Apache and IBM are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while IBM is ranked #6 with an average rating of 8.0. Apache holds a 19.2% mindshare in H, compared to IBM’s 1.7% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 57% of IBM users who would recommend it.

Apache Spark

Read 67 Apache Spark reviews

4,468 Views
983 Comparison Views

90% willing to recommend

IBM Spectrum Computing

Read 9 IBM Spectrum Computing reviews

532 Views
138 Comparison Views

57% willing to recommend

Apache Spark

IBM Spectrum Computing

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Executive Summary

We performed a comparison between Apache Spark and IBM Spectrum Computing based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark vs. IBM Spectrum Computing Report (Updated: July 2025).

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

July 2025

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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

7.3

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

IBM Spectrum Computing

Ranking in Hadoop

6th

Average Rating

8.2

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

Cloud Management (27th)

Mindshare comparison

As of August 2025, in the Hadoop category, the mindshare of Apache Spark is 19.2%, down from 20.2% compared to the previous year. The mindshare of IBM Spectrum Computing is 1.7%, down from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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OmarIsmail1

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.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The solution has been very stable."

"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."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"This solution provides a clear and convenient syntax for our analytical tasks."

"The data processing framework is good."

"The most valuable feature of this solution is its capacity for processing large amounts of data."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

More Apache Spark pros

"The best features of IBM Spectrum Computing are common across many of their storage products."

"Easy to operate and use."

"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."

"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."

"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."

"The most valuable feature is the backup capability."

"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."

More IBM Spectrum Computing pros

Cons

"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"Dynamic DataFrame options are not yet available."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"The solution’s integration with other platforms should be improved."

"The solution needs to optimize shuffling between workers."

"There were some problems related to the product's compatibility with a few Python libraries."

More Apache Spark cons

"Lack of sufficient documentation, particularly in Spanish."

"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."

"SMB storage and HPC is not compatible and it should be supported by IBM Spectrum Computing."

"We have not been able to use deduplication."

"In Pakistan, IBM's disadvantage is the lack of OEM support and presence."

"This solution is no longer managing tapes correctly."

"We'd like to see some AI model training for machine learning."

"IBM's sales and support structure can be challenging."

More IBM Spectrum Computing cons

Pricing and Cost Advice

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"They provide an open-source license for the on-premise version."

"The product is expensive, considering the setup."

"We are using the free version of the solution."

"Apache Spark is an expensive solution."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for 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."

"It is an open-source solution, it is free of charge."

More Apache Spark pricing and cost advice

"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

26%

Computer Software Company

10%

Manufacturing Company

Comms Service Provider

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

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Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

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Compared 33% of the time

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Compared 26% of the time

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Compared 26% of the time

VMware Aria Automation vs IBM Spectrum Computing

Compared 15% of the time

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Product Reports

Buyer's Guide

Apache Spark

August 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

August 2025

Download IBM Spectrum Computing product report

Also Known As

No data available

IBM Platform Computing

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

IBM Spectrum Computing uses intelligent workload and policy-driven resource management to optimize resources across the data center, on premises and in the cloud. Now up to 150X faster and scalable to over 160,000 cores, IBM provides you with the latest advances in software-defined infrastructure to help you unleash the power of your distributed mission-critical high performance computing (HPC), analytics and big data applications as well as a new generation open source frameworks such as Hadoop and Spark.

IBM

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

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

July 2025

Free Report: Apache Spark vs. IBM Spectrum Computing

Find out what your peers are saying about Apache Spark vs. IBM Spectrum Computing and other solutions. Updated: July 2025.

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