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 18.3% mindshare in H, compared to IBM’s 1.6% 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 66 Apache Spark reviews

1,064 Views
784 Comparison Views

90% willing to recommend

IBM Spectrum Computing

Read 9 IBM Spectrum Computing reviews

153 Views
88 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: June 2025).

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

June 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

7.4

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 (26th)

Mindshare comparison

As of July 2025, in the Hadoop category, the mindshare of Apache Spark is 18.3%, down from 20.4% compared to the previous year. The mindshare of IBM Spectrum Computing is 1.6%, down from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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

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

"The solution is very stable."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"The product is useful for analytics."

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

"Apache Spark can do large volume interactive data analysis."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"The processing time is very much improved over the data warehouse solution that we were using."

More Apache Spark pros

"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 most valuable feature is the backup capability."

"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."

"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."

More IBM Spectrum Computing pros

Cons

"Apache Spark provides very good performance The tuning phase is still tricky."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"Apache Spark should add some resource management improvements to the algorithms."

"The solution must improve its performance."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

More Apache Spark cons

"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."

"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."

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

"Lack of sufficient documentation, particularly in Spanish."

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

"This solution is no longer managing tapes correctly."

More IBM Spectrum Computing cons

Pricing and Cost Advice

"It is an open-source platform. We do not pay for its subscription."

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

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

"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 product is expensive, considering the setup."

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

"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 open-source. You have to pay only when you use any bundled product, such as Cloudera."

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

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

35%

Computer Software Company

Manufacturing Company

Transportation Company

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?

It is expensive.

See all answers

What needs improvement with IBM Spectrum Computing?

IBM's sales and support structure can be challenging. To work on an IBM deal, you often need to involve multiple specialists, each knowledgeable about only part of the product, rather than having a...

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What is your primary use case for IBM Spectrum Computing?

It is big on resilience and security. Their focus is on providing robust and secure solutions. Due to their high-end server models, IBM products are often more expensive than competitors. While IBM...

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 27% 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

June 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

June 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

June 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: June 2025.

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