Apache Spark vs IBM Spectrum Computing comparison

Why PeerSpot?

Apache and IBM are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.4, while IBM is ranked #7. Apache holds a 14.3% mindshare in H, compared to IBM’s 4.9% 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

8,218 Views
3,287 Comparison Views

90% willing to recommend

IBM Spectrum Computing

Read 9 IBM Spectrum Computing reviews

2,931 Views
733 Comparison Views

57% willing to recommend

Apache Spark

IBM Spectrum Computing

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

September 2026

Download the complete report

Helped 919,586 peers since 2012

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 (43rd)

Mindshare comparison

As of October 2026, in the Hadoop category, the mindshare of Apache Spark is 14.3%, down from 19.0% compared to the previous year. The mindshare of IBM Spectrum Computing is 4.9%, up from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Apache Spark	14.3%
IBM Spectrum Computing	4.9%
Other	80.8%

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.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"Spark can handle small to huge data and is suitable for any size of company."

"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."

"Machine learning, real time streaming, and data processing are fantastic, as well as the resilient or fault tolerant feature."

"With Spark SQL we've now the capabilities to analyse very large quantities of data located in S3 on Amazon at very low cost comparing other solution we checked."

"The 2.3 version is quite stable, all of our customers use it, there are around 100,000+ users, and it runs 24/7."

"The fault tolerant feature is provided."

"Aggregations are very fast in our project since we started to use Spark."

"The most valuable feature of Apache Spark is its flexibility."

More Apache Spark pros

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

"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."

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

"Easy to operate and use."

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

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

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

"I tell everyone that they should go with IBM Spectrum Computing."

More IBM Spectrum Computing pros

Cons

"The initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific."

"They currently use a JDK version which is a little bit old. Not all features are on it."

"The solution needs to optimize shuffling between workers."

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

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"The initial setup was complex. It was not easy getting the correct version and dependencies set up."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

More Apache Spark cons

"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."

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

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

"The deduplication software isn't quite up to speed with the market."

"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."

"We have not been able to use deduplication."

"Lack of sufficient documentation, particularly in Spanish."

More IBM Spectrum Computing cons

Pricing and Cost Advice

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

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

"Apache Spark is an expensive solution."

"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."

"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."

"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."

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

"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."

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

16%

Outsourcing Company

10%

Comms Service Provider

Construction Company

Comms Service Provider

13%

Financial Services Firm

12%

Construction Company

11%

Outsourcing Company

11%

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

Comparisons

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

Amazon EC2 vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

AWS Batch vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

VMware Aria Automation vs IBM Spectrum Computing

Compared 10% of the time

Cloudera Distribution for Hadoop vs IBM Spectrum Computing

Compared 8% of the time

HPE Data Fabric vs IBM Spectrum Computing

Compared 7% of the time

Ensim Unify vs IBM Spectrum Computing

Compared 6% of the time

IBM Netezza Performance Server vs IBM Spectrum Computing

Compared 4% of the time

More IBM Spectrum Computing Competitors

Product Reports

Buyer's Guide

Apache Spark

October 2026

Download Apache Spark product report

Buyer's Guide

Hadoop

October 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

September 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: September 2026.

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