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 8.2, while IBM is ranked #7 with an average rating of 8.0. Apache holds a 13.6% mindshare in H, compared to IBM’s 5.2% 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 69 Apache Spark reviews

5,582 Views
1,786 Comparison Views

90% willing to recommend

IBM Spectrum Computing

Read 9 IBM Spectrum Computing reviews

1,793 Views
430 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: April 2026).

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

April 2026

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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

6.9

Number of Reviews

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

Ranking in other categories

Cloud Management (29th)

Mindshare comparison

As of May 2026, in the Hadoop category, the mindshare of Apache Spark is 13.6%, down from 17.6% compared to the previous year. The mindshare of IBM Spectrum Computing is 5.2%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Apache Spark	13.6%
IBM Spectrum Computing	5.2%
Other	81.2%

Hadoop

Featured Reviews

Devindra Weerasooriya

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.

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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

"It allows the loading and investigation of very lard data sets, has MLlib for machine learning, Spark streaming, and both the new and old dataframe API."

"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 memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

"The product is useful for analytics."

"It is a better MR, supports streaming and micro-batch, and supports Spark ML and Spark SQL."

"I feel the streaming is its best feature."

"Apache Spark provides a very high-quality implementation of distributed data processing."

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

More Apache Spark pros

"This solution is working for both VTL and tape."

"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."

"IBM's ability to cluster compute resources is impressive, with built-in support for scenarios like VR and active-active configurations,"

"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."

"We are satisfied with the technical support, we have no issues."

"This solution worked as expected and it is reliable."

More IBM Spectrum Computing pros

Cons

"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."

"One limitation is that not all machine learning libraries and models support it."

"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 main concern is the overhead of Java when distributed processing is not necessary."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"The solution needs to optimize shuffling between workers."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

More Apache Spark cons

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

"This solution is no longer managing tapes correctly."

"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."

"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."

"Lack of sufficient documentation, particularly in Spanish."

"We are not fully satisfied with this product at the moment because we are having issues with reliability."

More IBM Spectrum Computing cons

Pricing and Cost Advice

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

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"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."

"The solution is affordable and there are no additional licensing costs."

"Apache Spark is an expensive solution."

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

"Spark is an open-source solution, so there are no licensing costs."

More Apache Spark pricing and cost advice

"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

22%

Comms Service Provider

Manufacturing Company

Computer Software Company

Manufacturing Company

14%

Financial Services Firm

14%

Construction Company

10%

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	16
Large Enterprise	33

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	1
Large Enterprise	6

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.

See all answers

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...

See all answers

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...

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 8% of the time

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Product Reports

Buyer's Guide

Apache Spark

May 2026

Download Apache Spark product report

Buyer's Guide

Hadoop

April 2026

Download IBM Spectrum Computing product report

Also Known As

No data available

IBM Platform Computing

Overview

Apache Spark is a leading open-source processing tool known for scalability and speed in managing large datasets. It supports both real-time and batch processing and is widely used for building data pipelines, machine learning applications, and analytics.

Apache Spark's strengths lie in its ability to process large data volumes efficiently through real-time and batch capabilities. With in-memory computation, it ensures fast data processing and significant performance gains. Its wide range of APIs, including those for machine learning, SQL, and analytics, make it versatile in handling complex data operations. While popular for ease of use and fault tolerance, Spark's management, debugging, and user-friendliness could benefit from improvements. Better GUIs, integration with BI tools, and enhanced monitoring are desired, alongside shuffling optimization and compatibility with more programming languages.

What are Apache Spark's key features?

Scalability: Efficiently manages large datasets across nodes.
Performance: In-memory computation for faster data processing.
Real-time Processing: Supports real-time analytics and data streaming.
APIs: Offers extensive APIs for machine learning, SQL, and analytics.

What benefits or ROI should users look for in reviews?

Ease of Use: Simplifies complex data tasks through intuitive operations.
Fault Tolerance: Ensures data reliability and continuous operations.
Integration Flexibility: Easily integrates with big data platforms and tools.

Organizations use Apache Spark predominantly for in-memory data processing, enabling seamless integration with big data frameworks. It's applied in security analytics, predictive modeling, and helps facilitate secure data transmissions in AI deployments. Industries leverage Spark's speed for sentiment analysis, data integration, and efficient ETL transformations.

Apache

IBM Spectrum Computing offers robust data backup and resource management capabilities, enhancing workload management and analytics for efficient data centers.

IBM Spectrum Computing is renowned for its backup capabilities and policy-driven resource management. It's used to cluster compute resources effectively and manage workloads efficiently. It supports data centers with intelligent workload management and predictive analytics, delivering speed and robustness. The ability to handle both VTL and tape with reliable technical support is a key advantage, although challenges include reliability issues, fragmented support, and compatibility concerns, particularly with Nutanix.

What are IBM Spectrum Computing's key features?

Backup Capability: Ensures secure data storage and disaster recovery.
Policy-Driven Resource Management: Optimizes resource allocation in data centers.
Intelligent Workload Management: Efficient handling of varied workloads.
Predictive Analytics: Offers insights for better decision-making.
Cluster Compute Resources: Effective clustering for increased performance.

What benefits should users consider?

High Performance: Fast and robust data processing capabilities.
Reliable Support: Strong technical assistance.
Security: Provides resilient business-oriented software.
Cost Efficiency: Despite high cost, provides significant value in data management.

IBM Spectrum Computing is implemented primarily for on-premises data backup and storage across industries safeguarding VMware, Hyper-V, and UNIX environments. It supports applications such as batch and on-demand processing, HPC, file servers, databases, ETL activities, Kubernetes, and mainframe operations, ensuring resilience and security.

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

April 2026

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: April 2026.

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