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 #7 with an average rating of 9.0. Apache holds a 17.5% mindshare in H, compared to IBM’s 2.6% 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

1,404 Views
1,090 Comparison Views

90% willing to recommend

IBM Spectrum Computing

Read 8 IBM Spectrum Computing reviews

216 Views
123 Comparison Views

57% willing to recommend

Apache Spark

IBM Spectrum Computing

Comparison Buyer's Guide

Download the report

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 2025).

Buyer's Guide

Apache Spark vs. IBM Spectrum Computing

April 2025

Download the complete report

Helped 848,989 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

7.7

Number of Reviews

Ranking in other categories

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

IBM Spectrum Computing

Ranking in Hadoop

7th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Management (23rd)

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Apache Spark is 17.5%, down from 21.4% compared to the previous year. The mindshare of IBM Spectrum Computing is 2.6%, up from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

Read full review

Avra Jyoti Ghosh

Data QualityTechnical Architect at Mindtree

One of the best tools in the data management and services area

I mainly used Spectrum Computing for data management, governance, quality, and ETL activity Spectrum Computing's best features are its speed, robustness, and data processing and analysis. Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the…

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

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"The main feature that we find valuable is that it is very fast."

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"The solution has been very stable."

"The deployment of the product is easy."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

More Apache Spark pros

"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."

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

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

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

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

"The most valuable feature is the backup capability."

"Easy to operate and use."

Cons

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

"The solution must improve its performance."

"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."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

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

"Dynamic DataFrame options are not yet available."

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

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

More Apache Spark cons

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

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

"Lack of sufficient documentation, particularly in Spanish."

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

"We have not been able to use deduplication."

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

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

"This solution is no longer managing tapes correctly."

Pricing and Cost Advice

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

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

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

"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."

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

"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."

"Apache Spark is an expensive solution."

More Apache Spark pricing and cost advice

"This solution is expensive."

"Spectrum Computing is one of the most expensive products on the market."

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

848,989 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

40%

Computer Software Company

Retailer

Manufacturing 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?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

See all answers

What do you like most about IBM Spectrum Computing?

Easy to operate and use.

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...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Flexera Cloud Management Platform (CMP) vs IBM Spectrum Computing

Compared 33% of the time

Morpheus vs IBM Spectrum Computing

Compared 25% of the time

Cloudera Data Platform vs IBM Spectrum Computing

Compared 22% of the time

Red Hat CloudForms vs IBM Spectrum Computing

Compared 20% of the time

More IBM Spectrum Computing Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

March 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

April 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: April 2025.

DOWNLOAD NOW

848,989 professionals have used our research since 2012.

See our Apache Spark vs. IBM Spectrum Computing report.

See our list of best Hadoop vendors.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.