IBM Spectrum Computing vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #6 with an average rating of 8.0, while Apache is ranked #5 with an average rating of 8.7. IBM holds a 1.7% mindshare in H, compared to Apache’s 10.6% mindshare. Additionally, 57% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

IBM Spectrum Computing

Read 8 IBM Spectrum Computing reviews

188 Views
104 Comparison Views

57% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

696 Views
483 Comparison Views

85% willing to recommend

IBM Spectrum Computing

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between IBM Spectrum Computing and Spark SQL 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 IBM Spectrum Computing vs. Spark SQL Report (Updated: June 2025).

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

June 2025

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Helped 858,038 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

IBM Spectrum Computing

Ranking in Hadoop

6th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Management (27th)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2025, in the Hadoop category, the mindshare of IBM Spectrum Computing is 1.7%, down from 2.5% compared to the previous year. The mindshare of Spark SQL is 10.6%, down from 11.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

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…

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SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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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 is working for both VTL and tape."

"Easy to operate and use."

"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."

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

"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."

"The most valuable feature is the backup capability."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

"The speed of getting data."

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

"Data validation and ease of use are the most valuable features."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"Overall the solution is excellent."

"The stability was fine. It behaved as expected."

More Spark SQL pros

Cons

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

"This solution is no longer managing tapes correctly."

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

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

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

"Lack of sufficient documentation, particularly in Spanish."

"We have not been able to use deduplication."

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

"In the next release, maybe the visualization of some command-line features could be added."

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"There should be better integration with other solutions."

"This solution could be improved by adding monitoring and integration for the EMR."

"There are many inconsistencies in syntax for the different querying tasks."

"Anything to improve the GUI would be helpful."

More Spark SQL cons

Pricing and Cost Advice

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

"This solution is expensive."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"The solution is bundled with Palantir Foundry at no extra charge."

"There is no license or subscription for this solution."

"We use the open-source version, so we do not have direct support from Apache."

"The solution is open-sourced and free."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

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Top Industries

By visitors reading reviews

Financial Services Firm

39%

Computer Software Company

Manufacturing Company

Real Estate/Law Firm

Financial Services Firm

18%

Computer Software Company

14%

Retailer

10%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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

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

What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

See all answers

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

We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

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Compared 47% of the time

SAP HANA vs Spark SQL

Compared 12% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 12% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 10% of the time

Amazon EMR vs Spark SQL

Compared 9% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Hadoop

June 2025

Download IBM Spectrum Computing product report

Buyer's Guide

Spark SQL

May 2025

Download Spark SQL product report

Also Known As

IBM Platform Computing

No data available

Overview

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

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

London South Bank University, Transvalor, Infiniti Red Bull Racing, Genomic

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

IBM Spectrum Computing vs. Spark SQL

June 2025

Free Report: IBM Spectrum Computing vs. Spark SQL

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

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